diff --git a/index.html b/index.html index 1557ddf..3487cda 100644 --- a/index.html +++ b/index.html @@ -57,10 +57,9 @@

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Ici, une offre de stage de M2, au LBBE à Lyon / Here, an M2 internship offer at LBBE in Lyon:

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https://www.bioinfo-lyon.fr/app/download/13442526249/offre_stage_M2_bioinfo_Siberchicot_Delignette_v2.pdf?t=1732803324

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At LBBE in Lyon, one M1 and one M2 internship offer in 2024-2025.

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Please note! Since June 2024, this repository has belonged to the lbbe-software organization. To avoid confusion, we strongly recommend updating any existing local clones to point to the new repository URL. You can do this by using git remote on the command line:

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Please note! Since June 2024, this repository has belonged to the lbbe-software organization. To avoid confusion, we strongly recommend updating any existing local clones to point to the new repository URL. You can do this by using git remote on the command line:

git remote set-url origin git@github.com:lbbe-software/DRomics.git

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git remote set-url origin https://github.com/lbbe-software/DRomics.git

diff --git a/pkgdown.yml b/pkgdown.yml index a7d9efc..70cb151 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -3,4 +3,4 @@ pkgdown: 2.1.1 pkgdown_sha: ~ articles: DRomics_vignette: DRomics_vignette.html -last_built: 2024-12-04T14:33Z +last_built: 2024-12-04T14:50Z diff --git a/reference/drcfit.html b/reference/drcfit.html index ac12788..9e47418 100644 --- a/reference/drcfit.html +++ b/reference/drcfit.html @@ -424,7 +424,7 @@

Examples# save all plots to pdf using plotfit2pdf() plotfit2pdf(f, path2figs = tempdir()) #> -#> Figures are stored in /tmp/RtmpLuxTDf. +#> Figures are stored in /tmp/Rtmp2PXivj. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. @@ -463,7 +463,7 @@

Examplesplotfit2pdf(f, plot.type = "fitted_residuals", nrowperpage = 9, ncolperpage = 6, path2figs = tempdir()) #> -#> Figures are stored in /tmp/RtmpLuxTDf. +#> Figures are stored in /tmp/Rtmp2PXivj. #> pdf #> 2 diff --git a/search.json b/search.json index d7be318..c917786 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"/articles/DRomics_vignette.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Overview of the DRomics package","text":"DRomics freely available tool dose-response (concentration-response) characterization omics data. especially dedicated omics data obtained using typical dose-response design, favoring great number tested doses (concentrations) rather great number replicates (need replicates use DRomics). first step consists importing, checking needed normalizing/transforming data (step 1), aim proposed workflow select monotonic /biphasic significantly responsive items (e.g. probes, contigs, metabolites) (step 2), choose best-fit model among predefined family monotonic biphasic models describe response selected item (step 3), derive benchmark dose concentration fitted curve (step 4). steps can performed R using DRomics functions, using shiny application named DRomics-shiny. available version, DRomics supports single-channel microarray data (log2 scale), RNAseq data (raw counts) continuous omics data (log scale), metabolomics data calculated AUC values (area curve), proteomics data expressed protein abundance peak intensity values. Proteomics data expressed spectral counts analyzed RNAseq data using raw counts carefully checking validity assumptions made processing RNAseq data. order link responses across biological levels based common method, DRomics also handles continuous apical data long meet use conditions least squares regression (homoscedastic Gaussian regression, see section least squares reminder needed). built environmental risk assessment context omics data often collected non-sequenced species species communities, DRomics provide annotation pipeline. annotation items selected DRomics may complex context, must done outside DRomics using databases KEGG Gene Ontology. DRomics functions can used help interpretation workflow results view biological annotation. enables multi-omics approach, comparison responses different levels organization (view common biological annotation). can also used compare responses one organization level, measured different experimental conditions (e.g. different time points). interpretation can performed R using DRomics functions, using second shiny application DRomicsInterpreter-shiny. vignette intended help users start using DRomics package. complementary reference manual can find details function package. first part vignette (Main workflow, steps 1 4) also help users first shiny application DRomics-shiny. second part (Help biological interpretation DRomics outputs) also help users second shiny application DRomicsInterpreter-shiny. shiny applications can used locally R session installation package required shiny tools (see DRomics web page need help install package: https://lbbe.univ-lyon1.fr/fr/dromics) want install package computer, can also launch two shiny applications shiny server lab, respectively https://lbbe-shiny.univ-lyon1.fr/DRomics/inst/DRomics-shiny/ https://lbbe-shiny.univ-lyon1.fr/DRomics/inst/DRomicsInterpreter-shiny/. want use R functions prefer use shiny applications, locally computer shiny server, can skip pieces code focus explanations outputs also given shiny applications. one day want go using R functions, recommend start whole R code corresponding analysis provided last page two shiny applications.","code":"# Installation of required shiny packages install.packages(c(\"shiny\", \"shinyBS\", \"shinycssloaders\", \"shinyjs\", \"shinyWidgets\", \"sortable\")) # Launch of the first shiny application DRomics-shiny shiny::runApp(system.file(\"DRomics-shiny\", package = \"DRomics\")) # Launch of the second shiny application DRomicsInterpreter-shiny shiny::runApp(system.file(\"DRomicsInterpreter-shiny\", package = \"DRomics\"))"},{"path":[]},{"path":[]},{"path":[]},{"path":"/articles/DRomics_vignette.html","id":"textfile","dir":"Articles","previous_headings":"Main workflow > Step 1: importation, check and normalization / transformation of data if needed > General format of imported data","what":"Importation of data from a unique text file","title":"Overview of the DRomics package","text":"Whatever type data imported DRomics (e.g. RNAseq, microarray, metabolomic data), data can imported .txt file (e.g. “mydata.txt”) organized one row per item (e.g. transcript, probe, metabolite) one column per sample. additional first row, name item identifier (e.g. “id”), must tested doses concentrations numeric format corresponding sample (example, triplicates treatment, first line “item”, 0, 0, 0, 0.1, 0.1, 0.1, etc.). additional first column must give identifier item (identifier probe, transcript, metabolite, …, name endpoint anchoring data), columns give responses item sample. file imported within DRomics using internal call function read.table() default field separator (sep argument) default decimal separator (dec argument “.”). remember, necessary, transform another decimal separator (e.g. “,”) “.” importing data. Different examples .txt files formatted DRomics workflow available package, one named “RNAseq_sample.txt”. can look data coded file using following code. use local dataset formatted way, use datafilename type \"yourchosenname.txt\".","code":"# Import the text file just to see what will be automatically imported datafilename <- system.file(\"extdata\", \"RNAseq_sample.txt\", package = \"DRomics\") # datafilename <- \"yourchosenname.txt\" # for a local file # Have a look of what information is coded in this file d <- read.table(file = datafilename, header = FALSE) nrow(d) ## [1] 1000 head(d) ## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 ## 1 RefSeq 0 0 0.22 0.22 0.22 0.67 0.67 0.67 2 ## 2 NM_144958 2072 2506 2519.00 2116.00 1999.00 2113.00 2219.00 2322.00 2359 ## 3 NR_102758 0 0 0.00 0.00 0.00 0.00 0.00 0.00 0 ## 4 NM_172405 198 265 250.00 245.00 212.00 206.00 227.00 246.00 265 ## 5 NM_029777 18 29 25.00 19.00 19.00 13.00 22.00 19.00 19 ## 6 NM_001130188 0 0 0.00 0.00 0.00 0.00 0.00 1.00 0 ## V11 V12 V13 V14 V15 ## 1 2 2 6 6 6 ## 2 1932 1705 2110 2311 2140 ## 3 0 0 0 0 0 ## 4 205 175 288 315 242 ## 5 26 16 26 32 33 ## 6 0 0 1 0 1"},{"path":"/articles/DRomics_vignette.html","id":"Robject","dir":"Articles","previous_headings":"Main workflow > Step 1: importation, check and normalization / transformation of data if needed > General format of imported data","what":"Importation of data as an R object","title":"Overview of the DRomics package","text":"Alternatively R object class data.frame can directly given input, corresponds output read.table(file, header = FALSE) file described previous section. can see example RNAseq data set available DRomics R object (named Zhou_kidney_pce) extended version (rows) previous dataset coded “RNAseq_sample.txt”. data already imported R different format one described , can use formatdata4DRomics() function build R object directly useable DRomics workflow. formatdata4DRomics() needs two arguments input: matrix data one row item one column sample, numeric vector coding dose sample. names samples can added third optional argument (see ?formatdata4DRomics details). example using RNAseq dataset package coded R object named zebraf. Whatever way format data, strongly recommend carefully look following sections check use good scale data, depends type measured signal (counts reads, fluorescence signal, …).","code":"# Load and look at the dataset directly coded as an R object data(Zhou_kidney_pce) nrow(Zhou_kidney_pce) ## [1] 33395 head(Zhou_kidney_pce) ## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 ## 1 RefSeq 0 0 0.22 0.22 0.22 0.67 0.67 0.67 2 ## 2 NM_144958 2072 2506 2519.00 2116.00 1999.00 2113.00 2219.00 2322.00 2359 ## 3 NR_102758 0 0 0.00 0.00 0.00 0.00 0.00 0.00 0 ## 4 NM_172405 198 265 250.00 245.00 212.00 206.00 227.00 246.00 265 ## 5 NM_029777 18 29 25.00 19.00 19.00 13.00 22.00 19.00 19 ## 6 NM_001130188 0 0 0.00 0.00 0.00 0.00 0.00 1.00 0 ## V11 V12 V13 V14 V15 ## 1 2 2 6 6 6 ## 2 1932 1705 2110 2311 2140 ## 3 0 0 0 0 0 ## 4 205 175 288 315 242 ## 5 26 16 26 32 33 ## 6 0 0 1 0 1 # Load and look at the data as initially coded data(zebraf) str(zebraf) ## List of 3 ## $ counts: int [1:1000, 1:16] 453 331 897 12 326 533 1948 904 583 154 ... ## ..- attr(*, \"dimnames\")=List of 2 ## .. ..$ : chr [1:1000] \"ENSDARG00000102141\" \"ENSDARG00000102123\" \"ENSDARG00000114503\" \"ENSDARG00000115971\" ... ## .. ..$ : chr [1:16] \"I10_05mG_E5\" \"I10_05mG_E6\" \"I10_05mG_E7\" \"I10_C5\" ... ## $ dose : num [1:16] 500 500 500 0 0 0 0 50000 50000 50000 ... ## $ batch : Factor w/ 2 levels \"I10\",\"I17\": 1 1 1 1 1 1 1 2 2 2 ... (samples <- colnames(zebraf$counts)) ## [1] \"I10_05mG_E5\" \"I10_05mG_E6\" \"I10_05mG_E7\" \"I10_C5\" \"I10_C6\" ## [6] \"I10_C7\" \"I10_C8\" \"I17_50mG_E5\" \"I17_50mG_E6\" \"I17_50mG_E8\" ## [11] \"I17_5mG_E5\" \"I17_5mG_E7\" \"I17_5mG_E8\" \"I17_C5\" \"I17_C7\" ## [16] \"I17_C8\" # Formatting of data for use in DRomics # data4DRomics <- formatdata4DRomics(signalmatrix = zebraf$counts, dose = zebraf$dose, samplenames = samples) # Look at the dataset coded as an R object nrow(data4DRomics) ## [1] 1001 head(data4DRomics) ## I10_05mG_E5 I10_05mG_E6 I10_05mG_E7 I10_C5 I10_C6 I10_C7 ## 1 item 500 500 500 0 0 0 ## 2 ENSDARG00000102141 453 656 590 636 529 453 ## 3 ENSDARG00000102123 331 505 401 465 488 430 ## 4 ENSDARG00000114503 897 1055 1073 1017 1022 992 ## 5 ENSDARG00000115971 12 23 24 21 40 32 ## 6 ENSDARG00000098311 326 544 416 484 330 297 ## I10_C8 I17_50mG_E5 I17_50mG_E6 I17_50mG_E8 I17_5mG_E5 I17_5mG_E7 I17_5mG_E8 ## 1 0 50000 50000 50000 5000 5000 5000 ## 2 566 790 557 1005 688 775 425 ## 3 582 498 427 815 450 675 307 ## 4 1124 1153 900 2115 1064 1555 824 ## 5 30 22 14 34 19 36 21 ## 6 396 502 380 1065 522 688 332 ## I17_C5 I17_C7 I17_C8 ## 1 0 0 0 ## 2 719 516 667 ## 3 522 475 541 ## 4 1449 1152 1341 ## 5 44 27 22 ## 6 666 471 506"},{"path":[]},{"path":"/articles/DRomics_vignette.html","id":"description-of-the-classical-types-of-data-handled-by-dromics","dir":"Articles","previous_headings":"Main workflow > Step 1: importation, check and normalization / transformation of data if needed > What types of data can be analyzed using DRomics ?","what":"Description of the classical types of data handled by DRomics","title":"Overview of the DRomics package","text":"DRomics offers possibility work different types omics data (see following subsections description) also continuous anchoring data. working omics data, lines data frame (except first one coding doses concentrations) correspond type data (e.g. raw counts RNAseq data). working anchoring data, different lines (except first one coding doses concentrations) correspond different endpoints may correspond different types data (e.g. biomass, length,..), assumed continuous data compatible Gaussian (normal) error model (transformation needed, e.g. logarithmic transformation) selection modeling steps (see section least squares need reminder condition). Three types omics data may imported DRomics using following functions: RNAseqdata() used import RNAseq counts reads (details look example RNAseq data), microarraydata() used import single-channel microarray data log2 scale (details look example microarray data), continuousomicdata() used import continuous omics data metabolomics data, proteomics data (expressed intensity),…, scale enables use Gaussian error model (details look example metabolomic omics data). also possible import DRomics continuous anchoring data measured apical level, especially sake comparison benchmark doses (see Step 4 definition BMD) estimated different levels organization using metrics. Nevertheless, one keep mind DRomics workflow optimized automatic analysis high throughput omics data (especially implying selection modeling steps high-dimensional data) tools may better suited sole analysis apical dose-response data (details look example continuous apical data). Steps 1 2 count data internally analysed using functions Bioconductor package DESeq2, continuous omics data (microarray data continuous omics data) internally analysed using functions Bioconductor package limma continuous anchoring data internally analysed using classical lm() function.","code":""},{"path":"/articles/DRomics_vignette.html","id":"RNAseqexample","dir":"Articles","previous_headings":"","what":"Overview of the DRomics package","title":"Overview of the DRomics package","text":"RNAseq data, imperatively imported raw counts (counts come Kallisto Salmon put add argument round.counts = TRUE order round ), choose transformation method used stabilize variance (“rlog” “vst”). example “vst” used make vignette quick compile, “rlog” recommended chosen default even computer intensive “vst” except number samples large (> 30) (encountered situ data example: see ?RNAseqdata section dedicated situ data details point). Whatever chosen method, data automatically normalized respect library size transformed log2 scale. plot output shows distribution signal contigs/genes, sample, normalization transformation data.","code":"RNAseqfilename <- system.file(\"extdata\", \"RNAseq_sample.txt\", package = \"DRomics\") # RNAseqfilename <- \"yourchosenname.txt\" # for a local file (o.RNAseq <- RNAseqdata(RNAseqfilename, transfo.method = \"vst\")) ## Elements of the experimental design in order to check the coding of the data: ## Tested doses and number of replicates for each dose: ## ## 0 0.22 0.67 2 6 ## 2 3 3 3 3 ## Number of items: 999 ## Identifiers of the first 20 items: ## [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" ## [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" ## [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" ## [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" ## Data were normalized with respect to library size and tranformed using ## the following method: vst plot(o.RNAseq, cex.main = 0.8, col = \"green\")"},{"path":"/articles/DRomics_vignette.html","id":"microarrayexample","dir":"Articles","previous_headings":"","what":"Overview of the DRomics package","title":"Overview of the DRomics package","text":"single-channel microarray data, imperatively imported log scale (classical recommended log2 scale), can choose array normalization methods (“cyclicloess”, “quantile”, “scale” “none”). example , “quantile” used make vignette quick compile, “cyclicloess” recommended chosen default even computer intensive others (see ?microarraydata details). plot output shows distribution signal probes, sample, normalization data.","code":"microarrayfilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package = \"DRomics\") # microarrayfilename <- \"yourchosenname.txt\" # for a local file (o.microarray <- microarraydata(microarrayfilename, norm.method = \"quantile\")) ## Elements of the experimental design in order to check the coding of the data: ## Tested doses and number of replicates for each dose: ## ## 0 0.69 1.223 2.148 3.774 6.631 ## 5 5 5 5 5 5 ## Number of items: 1000 ## Identifiers of the first 20 items: ## [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" ## [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" ## Data were normalized between arrays using the following method: quantile plot(o.microarray, cex.main = 0.8, col = \"green\")"},{"path":"/articles/DRomics_vignette.html","id":"metabolomicexample","dir":"Articles","previous_headings":"","what":"Overview of the DRomics package","title":"Overview of the DRomics package","text":"Neither normalization transformation provided function continuousomicdata(). pre-treatment metabolomic data must done importation data, data must imported log scale, can directly modelled using Gaussian (normal) error model. strong hypothesis required selection items dose-reponse modeling (see section least squares reminder needed). context multi-omics approach recommend use log2 transformation, instead classical log10 data, facilitate comparison results obtained transcriptomics data generally handled log2 scale. instance, basic procedure pre-treatment metabolomic data follow three steps described thereafter: ) removing metabolites proportion missing data (non detected) across samples high (20 50 percents according tolerance level); ii) retrieving missing values data using half minimum method (.e. half minimum value found metabolite across samples); iii) log-transformation values. scaling total intensity (normalization sum signals sample) another normalization necessary pertinent, recommend three previously described steps. plot output shows distribution signal metabolites, sample. deprecated metabolomicdata() function renamed continuousomicdata() recent versions package (keeping first name available) offer use continuous omic data proteomics data (expressed intensity) RT-qPCR data. metabolomic data, pre-treatment continuous omic data must done importation, data must imported scale enables use Gaussian error model strong hypothesis required selection items dose-response modeling.","code":"metabolofilename <- system.file(\"extdata\", \"metabolo_sample.txt\", package = \"DRomics\") # metabolofilename <- \"yourchosenname.txt\" # for a local file (o.metabolo <- continuousomicdata(metabolofilename)) ## Elements of the experimental design in order to check the coding of the data: ## Tested doses and number of replicates for each dose: ## ## 0 0.69 1.1 1.79 2.92 4.78 7.76 ## 10 6 2 2 2 6 2 ## Number of items: 109 ## Identifiers of the first 20 items: ## ## [1] \"P_2\" \"P_4\" \"P_5\" \"P_6\" \"P_7\" \"P_10\" \"P_11\" \"P_12\" \"P_14\" \"P_16\" ## [11] \"P_19\" \"P_21\" \"P_22\" \"P_26\" \"P_32\" \"P_34\" \"P_35\" \"P_36\" \"P_37\" \"P_38\" plot(o.metabolo, col = \"green\")"},{"path":"/articles/DRomics_vignette.html","id":"apicalexample","dir":"Articles","previous_headings":"","what":"Overview of the DRomics package","title":"Overview of the DRomics package","text":"transformation provided function continuousanchoringdata(). needed pre-treatment data must done importation data, can directly modelled using Gaussian error model. strong hypothesis required selection responsive endpoints dose-reponse modeling (see section least squares reminder needed). following example argument backgrounddose used specify doses equal 0.1 considered 0 DRomics workflow. Specifying argument necessary dose 0 data (see section situ data details point). data plot() function simply provides dose-response plot endpoint. default dose represented log scale, responses control (null dose, minus infinity log scale) appear half points Y-axis. can changed using argument dose_log_transfo .","code":"anchoringfilename <- system.file(\"extdata\", \"apical_anchoring.txt\", package = \"DRomics\") # anchoringfilename <- \"yourchosenname.txt\" # for a local file (o.anchoring <- continuousanchoringdata(anchoringfilename, backgrounddose = 0.1)) ## Elements of the experimental design in order to check the coding of the data: ## Tested doses and number of replicates for each dose: ## ## 0 2.4 3.8 6.2 10.1 16.5 26.8 43.5 70.7 ## 12 6 2 2 2 6 2 2 2 ## Number of endpoints: 2 ## Names of the endpoints: ## [1] \"growth\" \"photosynthesis\" plot(o.anchoring) + theme_bw() plot(o.anchoring, dose_log_transfo = FALSE) + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"specificdesigns","dir":"Articles","previous_headings":"Main workflow > Step 1: importation, check and normalization / transformation of data if needed > What types of data can be analyzed using DRomics ?","what":"Handling of data collected through specific designs","title":"Overview of the DRomics package","text":"DRomics workflow first developed data collected typical dose-response experiment, reasonable number tested doses (concentrations - least 4 addition control ideally 6 8) small number replicates per dose. Recently made modifications package make possible use designs 3 doses addition control even type design recommended dose-response modeling. also extended workflow handle situ (observational) data, replication, dose (concentration) controlled (see example situ data details). also now possible handle experimental data collected using design batch effect using DRomics together functions Bioconductor package sva correct batch effect selection modeling steps. also developed PCAplot() function help visualizing batch effect impact batch effect correction (BEC) data (see example RNAseq data experiment batch effect details handle case see ?PCAplot details specific function also used identify potential outlier samples).","code":""},{"path":"/articles/DRomics_vignette.html","id":"insitudata","dir":"Articles","previous_headings":"","what":"Overview of the DRomics package","title":"Overview of the DRomics package","text":"One problem may occur particular situ data, absence real control samples, corresponding strictly null exposure dose concentration. prevent hazardous calculation BMD (see Step 4 definition BMD) extrapolation case, one use argument backgrounddose define maximal measured dose can considered negligible dose. doses equal value given backgrounddose fixed 0, considered background level exposition. situ data (generally data large number samples), use rlog transformation RNAseqdata() recommended, speed reason likely encounter problem rlog transformation case outliers case (see https://support.bioconductor.org/p/105334/ explanation author DESeq2 want see example problems may appear outliers case, just force transfo.method \"rlog\" following example). plot output shows distribution signal contigs, sample (box plots stuck due large number samples), normalization transformation data.","code":"datafilename <- system.file(\"extdata\", \"insitu_RNAseq_sample.txt\", package=\"DRomics\") # Importation of data specifying that observed doses below the background dose # fixed here to 2e-2 will be considered as null dose to have a control (o.insitu <- RNAseqdata(datafilename, backgrounddose = 2e-2, transfo.method = \"vst\")) ## Elements of the experimental design in order to check the coding of the data: ## Tested doses and number of replicates for each dose: ## ## 0 0.0205 0.0216 0.0248 0.0272 0.0322 0.0339 0.0387 0.0432 0.0463 ## 25 1 1 1 1 1 1 1 1 1 ## 0.04866 0.0528 0.0726 0.101 0.112 0.1122 0.167 2.089 2.474 2.892 ## 1 1 3 1 1 1 1 1 1 1 ## 2.899 2.904 3.008 3.16 3.199 3.251 3.323 3.483 3.604 4.484 ## 1 1 1 1 1 1 1 1 1 1 ## 4.917 4.924 5.509 8.53 9.16 9.38 9.5 9.83 10.35 11.06 ## 1 1 1 1 1 1 1 1 1 1 ## 11.34 11.94 12.11 12.39 13.51 14.8 16.26 16.89 18.2 18.28 ## 1 1 1 1 1 1 1 1 1 1 ## 19.14 20.62 20.7 21.13 23.0348 23.69 24.15 27.06 28.05 29.88 ## 1 1 1 1 1 1 1 1 1 1 ## 36.28 ## 1 ## Number of items: 1000 ## Identifiers of the first 20 items: ## [1] \"N00000000001_c0_g1\" \"N00000000002_c0_g1\" \"N00000000003_c0_g1\" ## [4] \"N00000000004_c0_g1\" \"N00000000005_c0_g1\" \"N00000000006_c0_g1\" ## [7] \"N00000000008_c0_g1\" \"N10000_c0_g1\" \"N10000_c0_g1.1\" ## [10] \"N10001_c0_g1\" \"N10008_c1_g1\" \"N10010_c0_g1\" ## [13] \"N10010_c1_g1\" \"N10011_c0_g1\" \"N10012_c0_g1\" ## [16] \"N100156_c0_g1\" \"N1001_c0_g1\" \"N10021_c0_g1\" ## [19] \"N10021_c0_g1.1\" \"N10021_c0_g1.2\" ## Data were normalized with respect to library size and tranformed using ## the following method: vst plot(o.insitu)"},{"path":"/articles/DRomics_vignette.html","id":"batcheffect","dir":"Articles","previous_headings":"","what":"Overview of the DRomics package","title":"Overview of the DRomics package","text":"omics data collected design known potential batch effect, DRomics function PCAplot() can used example visualize impact batch effect data. seems necessary, functions specific packages can used perform batch effect correction (BEC). recommend use functions ComBat() ComBat_seq() Bioconductor sva package purpose, respectively microarray (continuous omic data) RNAseq data. sva Bioconductor package, must installed way DESeq2 limma previously loaded. needed look DRomics web page get good instruction install Bioconductor packages: https://lbbe.univ-lyon1.fr/fr/dromics). example using ComBat-seq() RNAseq data batch effect. sva package import RNAseq data format DRomics, necessary use DRomics function formatdata4DRomics() interoperate ComBat-seq DRomics functions (see section importation R object details function ?formatdata4DRomics). appears design data obtained using batches, controlled condition (null dose) appearing batches. PCA plot shows impact batch effect, clearly appears controls (red points) obtained two different batches. PCA plot BEC shows impact correction batch effect visible controls (red points).","code":"# Load of data data(zebraf) str(zebraf) ## List of 3 ## $ counts: int [1:1000, 1:16] 453 331 897 12 326 533 1948 904 583 154 ... ## ..- attr(*, \"dimnames\")=List of 2 ## .. ..$ : chr [1:1000] \"ENSDARG00000102141\" \"ENSDARG00000102123\" \"ENSDARG00000114503\" \"ENSDARG00000115971\" ... ## .. ..$ : chr [1:16] \"I10_05mG_E5\" \"I10_05mG_E6\" \"I10_05mG_E7\" \"I10_C5\" ... ## $ dose : num [1:16] 500 500 500 0 0 0 0 50000 50000 50000 ... ## $ batch : Factor w/ 2 levels \"I10\",\"I17\": 1 1 1 1 1 1 1 2 2 2 ... # Look at the design of this dataset xtabs(~ zebraf$dose + zebraf$batch) ## zebraf$batch ## zebraf$dose I10 I17 ## 0 4 3 ## 500 3 0 ## 5000 0 3 ## 50000 0 3 # Formating of data using the formatdata4DRomics() function data4DRomics <- formatdata4DRomics(signalmatrix = zebraf$counts, dose = zebraf$dose) # Importation of data just to use DRomics functions # As only raw data will be given to ComBat_seq after (o <- RNAseqdata(data4DRomics)) ## Just wait, the transformation using regularized logarithm (rlog) may ## take a few minutes. ## Elements of the experimental design in order to check the coding of the data: ## Tested doses and number of replicates for each dose: ## ## 0 500 5000 50000 ## 7 3 3 3 ## Number of items: 1000 ## Identifiers of the first 20 items: ## [1] \"ENSDARG00000102141\" \"ENSDARG00000102123\" \"ENSDARG00000114503\" ## [4] \"ENSDARG00000115971\" \"ENSDARG00000098311\" \"ENSDARG00000104839\" ## [7] \"ENSDARG00000100143\" \"ENSDARG00000102474\" \"ENSDARG00000104049\" ## [10] \"ENSDARG00000102226\" \"ENSDARG00000103095\" \"ENSDARG00000102128\" ## [13] \"ENSDARG00000110470\" \"ENSDARG00000100422\" \"ENSDARG00000104632\" ## [16] \"ENSDARG00000100660\" \"ENSDARG00000113107\" \"ENSDARG00000099787\" ## [19] \"ENSDARG00000112451\" \"ENSDARG00000070546\" ## Data were normalized with respect to library size and tranformed using ## the following method: rlog # PCA plot with the sample labels PCAdataplot(o, label = TRUE) + theme_bw() # PCA plot to visualize the batch effect PCAdataplot(o, batch = zebraf$batch) + theme_bw() # Batch effect correction using ComBat_seq{sva} require(sva) BECcounts <- ComBat_seq(as.matrix(o$raw.counts), batch = as.factor(zebraf$batch), group = as.factor(o$dose)) # Formating of data after batch effect correction BECdata4DRomics <- formatdata4DRomics(signalmatrix = BECcounts, dose = o$dose) o.BEC <- RNAseqdata(BECdata4DRomics) ## Just wait, the transformation using regularized logarithm (rlog) may ## take a few minutes. # PCA plot after batch effect correction PCAdataplot(o.BEC, batch = zebraf$batch) + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"step2","dir":"Articles","previous_headings":"Main workflow","what":"Step 2: selection of significantly responding items","title":"Overview of the DRomics package","text":"second step workflow, function itemselect() must used simply taking input first argument output function used step 1 (output RNAseqdata(), microarraydata(), continuousomicdata() continuousanchoringdata()). example microarray data. false discovery rate (FDR) corresponds expected proportion items falsely detected responsive. large data set important define selection step based FDR reduce number items processed, also remove noisy dose-response signals may impair quality results. recommend set value 0.001 0.1 depending initial number items. number high (several tens thousands), recommend FDR less 0.05 (0.001 0.01) increase robustness results (Larras et al. 2018). Concerning method used selection, recommend default choice (“quadratic”) typical omics dose-response design (many doses/concentrations replicates per condition). enables selection monotonic biphasic dose-response relationships. want focus monotonic dose-response relationships, “linear” method chosen. design small number doses/concentrations many replicates (optimal dose-response modeling), “ANOVA” method preferable. situ data (observational data without replicates due uncontrolled dose), trend tests proposed use ANOVA test absence replicates conditions reasonable. three methods proposed selection step based use simple model (quadratic,linear ANOVA-type) linking signal dose rank scale. model internally fitted data empirical Bayesian approach using respective packages DESeq2 limma RNAseq data microarray continuous omics data, classical linear regression using lm() function continuous anchoring data. adjustment p-values according specified FDR performed case, even continuous anchoring data, ensure unicity workflow independently type data. See ?itemselect details Larras et al. 2018 comparison three proposed methods example. easy, using example package VennDiagram, compare selection items obtained using two different methods, following example.","code":"(s_quad <- itemselect(o.microarray, select.method = \"quadratic\", FDR = 0.01)) ## Number of selected items using a quadratic trend test with an FDR of 0.01: 150 ## Identifiers of the first 20 most responsive items: ## [1] \"383.2\" \"384.2\" \"363.1\" \"383.1\" \"384.1\" \"363.2\" \"364.2\" \"364.1\" \"300.2\" ## [10] \"301.1\" \"300.1\" \"301.2\" \"263.2\" \"27.2\" \"25.1\" \"368.1\" \"351.1\" \"15\" ## [19] \"370\" \"350.2\" require(VennDiagram) s_lin <- itemselect(o.microarray, select.method = \"linear\", FDR = 0.01) index_quad <- s_quad$selectindex index_lin <- s_lin$selectindex plot(c(0,0), c(1,1), type = \"n\", xaxt = \"n\", yaxt = \"n\", bty = \"n\", xlab = \"\", ylab = \"\") draw.pairwise.venn(area1 = length(index_quad), area2 = length(index_lin), cross.area = length(which(index_quad %in% index_lin)), category = c(\"quadratic trend test\", \"linear trend test\"), cat.col=c(\"cyan3\", \"darkorange1\"), col=c(\"black\", \"black\"), fill = c(\"cyan3\", \"darkorange1\"), lty = \"blank\", cat.pos = c(1,11))"},{"path":[]},{"path":"/articles/DRomics_vignette.html","id":"fit-of-the-best-model","dir":"Articles","previous_headings":"Main workflow > Step 3: fit of dose-response models, choice of the best fit for each curve","what":"Fit of the best model","title":"Overview of the DRomics package","text":"Step 3 function drcfit() simply takes input first argument output itemselect(). item selected Step 2, model best fits dose-response data chosen among family five simple models built describe wide variety monotonic biphasic dose-response curves (DRC) (exclusively monotonic biphasic curves : flexible models polynomial third fourth order classical polynomial models deliberately considered). complete description models see last section Step 3 Larras et al. 2018. procedure used select best fit based information criterion described Larras et al. 2018 ?drcfit. classical former default option AIC (Akaike criterion - default information criterion used DRomics versions < 2.2-0) replaced default use AICc (second-order Akaike criterion) order prevent overfitting may occur dose-response designs small number data points, recommended now classically done regression (Hurvich Tsai, 1989; Burnham Anderson DR, 2004). call drcfit() function may take time number pre-selected items large, default progressbar provided. arguments function can used specify parallel computing accelerate computation (see ?drcfit details). following can see first lines output data frame example (see ?drcfit complete description columns output data frame.) output data frame provides information item, best-fit model, parameter values, standard residual error (SDres) (see section least squares definition), coordinates particular points, trend curve (among increasing, decreasing, U-shaped, bell-shaped). extensive description outputs complete DRomics workflow provided last section main workflow. Note number items successfully fitted (output Step 3) often smaller number items selected Step 2, selected items, models may fail converge fail significantly better describe data constant model.","code":"(f <- drcfit(s_quad, progressbar = FALSE)) ## Results of the fitting using the AICc to select the best fit model ## 22 dose-response curves out of 150 previously selected were removed ## because no model could be fitted reliably. ## Distribution of the chosen models among the 128 fitted dose-response curves: ## ## Hill linear exponential Gauss-probit ## 2 29 39 49 ## log-Gauss-probit ## 9 ## Distribution of the trends (curve shapes) among the 128 fitted dose-response curves: ## ## bell dec inc U ## 38 37 34 19 head(f$fitres) ## id irow adjpvalue model nbpar b c d e f SDres ## 1 383.2 725 2.08e-07 Gauss-probit 4 5.5836 8.58 8.58 1.70 3.62 0.157 ## 2 384.2 727 2.08e-07 exponential 3 -0.0298 NA 12.24 1.76 NA 0.160 ## 3 363.1 686 2.24e-07 exponential 3 -0.2058 NA 9.10 3.11 NA 0.218 ## 4 383.1 724 2.24e-07 Gauss-probit 4 5.4879 8.75 8.75 1.72 3.58 0.169 ## 5 384.1 726 3.41e-07 Gauss-probit 4 6.8453 7.26 7.26 1.77 5.01 0.158 ## 6 363.2 687 7.01e-07 exponential 3 -0.1467 NA 9.10 2.77 NA 0.206 ## typology trend y0 yatdosemax yrange maxychange xextrem yextrem ## 1 GP.bell bell 12.0 11.03 1.17 1.004 1.70 12.2 ## 2 E.dec.concave dec 12.2 10.99 1.25 1.251 NA NA ## 3 E.dec.concave dec 9.1 7.58 1.53 1.527 NA NA ## 4 GP.bell bell 12.2 11.15 1.18 1.012 1.72 12.3 ## 5 GP.bell bell 12.1 11.15 1.11 0.949 1.77 12.3 ## 6 E.dec.concave dec 9.1 7.64 1.46 1.461 NA NA"},{"path":"/articles/DRomics_vignette.html","id":"plot-of-fitted-curves","dir":"Articles","previous_headings":"Main workflow > Step 3: fit of dose-response models, choice of the best fit for each curve","what":"Plot of fitted curves","title":"Overview of the DRomics package","text":"default plot() function used output drcfit() function provides first 20 fitted curves (ones specify using argument items) observed points. Fitted curves represented red, replicates represented open circles means replicates dose/concentration represented solid circles. fitted curves may saved pdf file using plotfit2pdf() function (see ?drcfit). fitted curves default represented using log scale dose/concentration, suited common cases range observed doses/concentrations wide /tested doses/concentrations obtained dilutions. observations control appear differently observations, half circles y-axis, remind true value minus infinity log scale. Use dose_log_transfo = FALSE keep raw scale doses (see ). Another specific plot function named targetplot() can used plot targeted items, whether selected step 2 fitted step 3. See example details ?targetplot. example, default arbitrary space y-axis (points control) points first non null doses enlarged fixing limits x-axis :","code":"plot(f) targetitems <- c(\"88.1\", \"1\", \"3\", \"15\") targetplot(targetitems, f = f) + scale_x_log10(limits = c(0.2, 10))"},{"path":"/articles/DRomics_vignette.html","id":"residuals","dir":"Articles","previous_headings":"Main workflow > Step 3: fit of dose-response models, choice of the best fit for each curve","what":"Plot of residuals","title":"Overview of the DRomics package","text":"check assumption Gaussian error model (see section least squares), two types residual plots can used, \"dose_residuals\" plot residuals observed doses/concentrations, \"fitted_residuals\" plot residuals fitted values modeled signal. residual plots items may also saved pdf file using plotfit2pdf() function (see ?drcfit).","code":"plot(f, plot.type = \"dose_residuals\")"},{"path":"/articles/DRomics_vignette.html","id":"models","dir":"Articles","previous_headings":"Main workflow > Step 3: fit of dose-response models, choice of the best fit for each curve","what":"Description of the family of dose-response models fitted in DRomics","title":"Overview of the DRomics package","text":"best fit model chosen among five following models describing observed signal yy function xx dose (concentration): linear model: y=d+b×xy = d + b \\times x 2 parameters, bb slope dd mean signal control. e>0e>0 dose response curve - DRC - increasing b>0b>0 decreasing b<0b<0, asymptote high doses. e<0e<0 DRC increasing b<0b<0 decreasing b>0b>0, asymptote d−bd-b high doses. Hill model: y=c+d−c1+(xe)= c + \\frac{d-c}{1+(\\frac{x}{e})^b} 4 parameters,bb (>0>0) shape parameter, cc asymtotic signal high doses, dd mean signal control, ee (>0>0) dose inflection point sigmoid. Gauss-probit model built sum Gauss probit part sharing parameters defined :y=f×exp(−0.5(x−eb)2)+d+(c−d)×Φ(x−eb)y = f \\times exp\\left(-0.5 \\left(\\frac{x-e}{b}\\right)^2\\right) +d+(c-d) \\times \\Phi\\left(\\frac{x-e}{b}\\right) 5 parameters,bb (>0>0) shape parameter corresponding standard deviation Gauss part model, cc asymtotic signal high doses, dd asymptotic signal left DRC (generally corresponding fictive negative dose), ee (>0>0) shape parameter corresponding mean Gauss part model, ff amplitude sign Gauss part model (model U-shaped f<0f<0 bell-shaped f<0f<0).Φ\\Phi represents cumulative distribution function (CDF) standard Gauss (also named normal Gaussian) distribution. model encompasses two simplifed versions 4 parameters, one monotonic (f=0f=0) one symmetrical asymptotes (c=dc=d). log-Gauss-probit model, variant previous one log scale dose: y=f×exp(−0.5(ln(x)−ln(e)b)2)+d+(c−d)×Φ(ln(x)−ln(e)b)y = f \\times exp\\left(-0.5\\left(\\frac{ln(x)-ln(e)}{b}\\right)^2\\right) +d+(c-d) \\times \\Phi\\left(\\frac{ln(x)-ln(e)}{b}\\right) 5 parameters,bb (>0>0) shape parameter corresponding standard deviation Gauss part model, cc asymtotic signal high dose, dd asymptotic signal left DRC, reached control (ln(x)=ln(0)=−∞ln(x) = ln(0) = -\\infty), ln(e)ln(e) (>0>0) shape parameter corresponding mean Gauss part model, ff amplitude sign Gauss part model (model U-shaped f<0f<0 bell-shaped f<0f<0).Φ\\Phi represents cumulative distribution function (CDF) standard Gauss distribution. previous one, model encompasses two simplifed versions 4 parameters, one monotonic (f=0f=0) one symmetrical asymptotes (c=dc=d). family five models built able describe wide range monotonic biphasic DRC. following plot represented typologies curves can described using models, depending definition parameters. following plot curves represented signal y-axis raw dose x-axis. range tested observed doses often large, decided plot model fits default using log scale doses. shape models transformed log x-scale, especially linear model appear straight line .","code":""},{"path":"/articles/DRomics_vignette.html","id":"leastsquares","dir":"Articles","previous_headings":"Main workflow > Step 3: fit of dose-response models, choice of the best fit for each curve","what":"Reminder on least squares regression","title":"Overview of the DRomics package","text":"important using DRomics mind dose-response models fitted using least squares regression, assuming additive Gaussian (normal) error model observed signal. scale import data important: log (pseudo-log) transformation may necessary meet use conditions model types data. Let us recall formulation Gaussian model defining signal (transformation needed) yy function dose (concentration) xx, ff one five models previously described, θ\\theta vector parameters (length 2 5). y=f(x,θ)+ϵy = f(x, \\theta) + \\epsilon ϵ∼N(0,σ)\\epsilon \\sim N(0, \\sigma)N(0,σ)N(0, \\sigma) representing Gaussian (normal) distribution mean 00 standard deviation (SD) σ\\sigma. model, residual standard deviation σ\\sigma assumed constant. classical “homoscedasticity” hypothesis (see following figure illustration). examination residuals (see section plot residuals) good way check error model strongly violated data.","code":""},{"path":[]},{"path":"/articles/DRomics_vignette.html","id":"calculation-of-bmd","dir":"Articles","previous_headings":"Main workflow > Step 4: calculation of benchmark doses (BMD)","what":"Calculation of BMD","title":"Overview of the DRomics package","text":"two types benchmark doses (BMD-zSD BMD-xfold) proposed EFSA (2017) systematically calculated fitted dose-response curve using function bmdcalc() output drcfit() function first argument, strongly recommend use first one (BMD-zSD) reasons explained Larras et al. 2018 (see ?bmdcalc details function). recommended BMD-zSD,argument zz, default 1, used define BMD-zSD dose response reaching BMR (benchmark response) defined BMR=y0±z×SDBMR = y_0 \\pm z \\times SD y0y_0 level control given dose-response fitted model SDSD residual standard deviation dose-response fitted model (also named σ\\sigma previous mathematical definition Gaussian model). less recommended BMD-xfold, argument xx, default 10 (10%), used define BMD-xfold dose response reaching BMR defined BMR=y0±x100×y0BMR = y_0 \\pm \\frac{x}{100} \\times y_0. second BMD version take account residual standard deviation, strongly dependent magnitude y0y_0, may problem signal control close 0, rare omics data classically handled log scale. following can see first lines output data frame function bmdcalc() example. BMD values coded NA BMR stands within range response values defined model outside range tested doses, NaN BMR stands outside range response values defined model due asymptotes. low BMD values obtained extrapolation 0 smallest non null tested dose, correspond sensitive items (want exclude), thresholded minBMD, argument default fixed smallest non null tested dose divided 100, can fixed user considers negligible dose. extensive description outputs complete DRomics workflow provided last section main workflow. can also see ?bmdcalc complete description arguments columns output data frame.","code":"(r <- bmdcalc(f, z = 1, x = 10)) ## 1 BMD-xfold values and 0 BMD-zSD values are not defined (coded NaN as ## the BMR stands outside the range of response values defined by the model). ## 60 BMD-xfold values and 0 BMD-zSD values could not be calculated (coded ## NA as the BMR stands within the range of response values defined by the ## model but outside the range of tested doses). head(r$res) ## id irow adjpvalue model nbpar b c d e f SDres ## 1 383.2 725 2.08e-07 Gauss-probit 4 5.5836 8.58 8.58 1.70 3.62 0.157 ## 2 384.2 727 2.08e-07 exponential 3 -0.0298 NA 12.24 1.76 NA 0.160 ## 3 363.1 686 2.24e-07 exponential 3 -0.2058 NA 9.10 3.11 NA 0.218 ## 4 383.1 724 2.24e-07 Gauss-probit 4 5.4879 8.75 8.75 1.72 3.58 0.169 ## 5 384.1 726 3.41e-07 Gauss-probit 4 6.8453 7.26 7.26 1.77 5.01 0.158 ## 6 363.2 687 7.01e-07 exponential 3 -0.1467 NA 9.10 2.77 NA 0.206 ## typology trend y0 yatdosemax yrange maxychange xextrem yextrem BMD.zSD ## 1 GP.bell bell 12.0 11.03 1.17 1.004 1.70 12.2 1.33 ## 2 E.dec.concave dec 12.2 10.99 1.25 1.251 NA NA 3.26 ## 3 E.dec.concave dec 9.1 7.58 1.53 1.527 NA NA 2.25 ## 4 GP.bell bell 12.2 11.15 1.18 1.012 1.72 12.3 1.52 ## 5 GP.bell bell 12.1 11.15 1.11 0.949 1.77 12.3 1.41 ## 6 E.dec.concave dec 9.1 7.64 1.46 1.461 NA NA 2.43 ## BMR.zSD BMD.xfold BMR.xfold ## 1 12.19 NA 10.83 ## 2 12.08 6.59 11.02 ## 3 8.89 5.26 8.19 ## 4 12.33 NA 10.95 ## 5 12.26 NA 10.89 ## 6 8.90 5.47 8.19"},{"path":"/articles/DRomics_vignette.html","id":"plots-of-the-bmd-distribution","dir":"Articles","previous_headings":"Main workflow > Step 4: calculation of benchmark doses (BMD)","what":"Plots of the BMD distribution","title":"Overview of the DRomics package","text":"default plot output bmdcalc() function provides distribution benchmark doses ECDF (Empirical Cumulative Density Function) plot chosen BMD (“zSD”” “xfold”). See example . Different alternative plots proposed (see ?bmdcalc details) can obtained using argument plottype choose type plot (“ecdf”, “hist” “density”) argument split plot example “trend”. can also use bmdplot() function make ECDF plot BMDs personalize (see ?bmdplot details). BMD ECDF plot one can add color gradient item coding intensity signal (shift control signal 0) function dose (see ?bmdplotwithgradient details example ). generally necessary use argument line.size manually adjust width lines plot default value always give satisfactory resut. also recommended (mandatory default option argument scaling) scale signal order focus shape dose-reponse curves amplitude signal change.","code":"plot(r, BMDtype = \"zSD\", plottype = \"ecdf\") + theme_bw() bmdplotwithgradient(r$res, BMDtype = \"zSD\", facetby = \"trend\", shapeby = \"model\", line.size = 1.2, scaling = TRUE)"},{"path":"/articles/DRomics_vignette.html","id":"bootstrap","dir":"Articles","previous_headings":"Main workflow > Step 4: calculation of benchmark doses (BMD)","what":"Calculation of confidence intervals on the BMDs by bootstrap","title":"Overview of the DRomics package","text":"Confidence intervals BMD values can calculated bootstrap. call function may take much time, default progressbar provided arguments can used specify parallel computing accelerate computation (see ?bmdboot details). example , small number iterations used just make vignette quick compile, default value argument niter (1000) considered minimal value obtain stable results. function gives output corresponding output bmdcalc() function completed bounds BMD confidence intervals (default 95% confidence intervals) number bootstrap iterations model successfully fitted data. extensive description outputs complete DRomics workflow provided last section main workflow. plot() function applied output bmdboot() function gives ECDF plot chosen BMD confidence interval BMD (see ?bmdcalc examples). default BMDs infinite confidence interval bound plotted.","code":"(b <- bmdboot(r, niter = 50, progressbar = FALSE)) ## Bootstrap confidence interval computation failed on 17 items among 128 ## due to lack of convergence of the model fit for a fraction of the ## bootstrapped samples greater than 0.5. ## For 4 BMD.zSD values and 68 BMD.xfold values among 128 at least one ## bound of the 95 percent confidence interval could not be computed due ## to some bootstrapped BMD values not reachable due to model asymptotes ## or reached outside the range of tested doses (bounds coded Inf)). head(b$res) ## id irow adjpvalue model nbpar b c d e f SDres ## 1 383.2 725 2.08e-07 Gauss-probit 4 5.5836 8.58 8.58 1.70 3.62 0.157 ## 2 384.2 727 2.08e-07 exponential 3 -0.0298 NA 12.24 1.76 NA 0.160 ## 3 363.1 686 2.24e-07 exponential 3 -0.2058 NA 9.10 3.11 NA 0.218 ## 4 383.1 724 2.24e-07 Gauss-probit 4 5.4879 8.75 8.75 1.72 3.58 0.169 ## 5 384.1 726 3.41e-07 Gauss-probit 4 6.8453 7.26 7.26 1.77 5.01 0.158 ## 6 363.2 687 7.01e-07 exponential 3 -0.1467 NA 9.10 2.77 NA 0.206 ## typology trend y0 yatdosemax yrange maxychange xextrem yextrem BMD.zSD ## 1 GP.bell bell 12.0 11.03 1.17 1.004 1.70 12.2 1.33 ## 2 E.dec.concave dec 12.2 10.99 1.25 1.251 NA NA 3.26 ## 3 E.dec.concave dec 9.1 7.58 1.53 1.527 NA NA 2.25 ## 4 GP.bell bell 12.2 11.15 1.18 1.012 1.72 12.3 1.52 ## 5 GP.bell bell 12.1 11.15 1.11 0.949 1.77 12.3 1.41 ## 6 E.dec.concave dec 9.1 7.64 1.46 1.461 NA NA 2.43 ## BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower ## 1 12.19 NA 10.83 0.489 3.93 Inf ## 2 12.08 6.59 11.02 1.724 4.58 6.40 ## 3 8.89 5.26 8.19 1.366 3.52 4.61 ## 4 12.33 NA 10.95 0.423 4.17 Inf ## 5 12.26 NA 10.89 0.481 4.36 Inf ## 6 8.90 5.47 8.19 1.334 3.31 4.75 ## BMD.xfold.upper nboot.successful ## 1 Inf 36 ## 2 Inf 47 ## 3 5.96 50 ## 4 Inf 36 ## 5 Inf 26 ## 6 6.00 50"},{"path":"/articles/DRomics_vignette.html","id":"bmdfilter","dir":"Articles","previous_headings":"Main workflow > Step 4: calculation of benchmark doses (BMD)","what":"Filtering BMDs according to estimation quality","title":"Overview of the DRomics package","text":"Using bmdfilter() function, possible use one three filters proposed retain items associated best estimated BMD values. default retained items BMD confidence interval defined (using \"CIdefined\") (excluding items bootstrap procedure failed). One can even restrictive retaining items BMD confidence interval within range tested/observed doses (using \"CIfinite\"), less restrictive (using \"BMDdefined\") requiring BMD point estimate must defined within range tested/observed doses. Let us recall bmdcalc() output, case BMD coded NA NaN. example application different filters based BMD-xfold values, chosen just better illustrate way filters work, far bad BMD-xfold estimations bad BMD-zSD estimations.","code":"# Plot of BMDs with no filtering subres <- bmdfilter(b$res, BMDfilter = \"none\") bmdplot(subres, BMDtype = \"xfold\", point.size = 2, point.alpha = 0.4, add.CI = TRUE, line.size = 0.4) + theme_bw() # Plot of items with defined BMD point estimate subres <- bmdfilter(b$res, BMDtype = \"xfold\", BMDfilter = \"definedBMD\") bmdplot(subres, BMDtype = \"xfold\", point.size = 2, point.alpha = 0.4, add.CI = TRUE, line.size = 0.4) + theme_bw() # Plot of items with defined BMD point estimate and CI bounds subres <- bmdfilter(b$res, BMDtype = \"xfold\", BMDfilter = \"definedCI\") bmdplot(subres, BMDtype = \"xfold\", point.size = 2, point.alpha = 0.4, add.CI = TRUE, line.size = 0.4) + theme_bw() # Plot of items with finite BMD point estimate and CI bounds subres <- bmdfilter(b$res, BMDtype = \"xfold\", BMDfilter = \"finiteCI\") bmdplot(subres, BMDtype = \"xfold\", point.size = 2, point.alpha = 0.4, add.CI = TRUE, line.size = 0.4) + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"plot-of-fitted-curves-with-bmd-values-and-confidence-intervals","dir":"Articles","previous_headings":"Main workflow > Step 4: calculation of benchmark doses (BMD)","what":"Plot of fitted curves with BMD values and confidence intervals","title":"Overview of the DRomics package","text":"possible add output bmdcalc() (bmdboot()) argument BMDoutput plot() function drcfit(), order add BMD values (defined) vertical line fitted curve, bounds confidence intervals (successfully calculated) two dashed lines. Horizontal dotted lines corresponding two BMR potential values also added. See example . fitted curves may also saved way pdf file using plotfit2pdf() function (see ?drcfit).","code":"# If you do not want to add the confidence intervals just replace b # the output of bmdboot() by r the output of bmdcalc() plot(f, BMDoutput = b)"},{"path":"/articles/DRomics_vignette.html","id":"plot-of-all-the-fitted-curves-in-one-figure-with-points-at-bmd-bmr-values","dir":"Articles","previous_headings":"Main workflow > Step 4: calculation of benchmark doses (BMD)","what":"Plot of all the fitted curves in one figure with points at BMD-BMR values","title":"Overview of the DRomics package","text":"possible use curvesplot() function plot fitted curves one figure add use curvesplot() function extensively described next parts corresponding help page. default plot curves scaled focus shape dose-response amplitude (add scaling = FALSE see curves without scaling) log dose scale used. following plot also added vertical lines corresponding tested doses plot add transparency visualize density curves shapes similar (especially case linear shapes). use package plotly make plot interactive can interesting example get identifiant curve choose group curves eliminate focus . can try following code get interactive version previous figure.","code":"tested.doses <- unique(f$omicdata$dose) g <- curvesplot(r$res, xmax = max(tested.doses), colorby = \"trend\", line.size = 0.8, line.alpha = 0.3, point.size = 2, point.alpha = 0.6) + geom_vline(xintercept = tested.doses, linetype = 2) + theme_bw() print(g) if (require(plotly)) { ggplotly(g) }"},{"path":"/articles/DRomics_vignette.html","id":"outputs","dir":"Articles","previous_headings":"Main workflow > Step 4: calculation of benchmark doses (BMD)","what":"Description of the outputs of the complete DRomics workflow","title":"Overview of the DRomics package","text":"output complete DRomics workflow, given b$res b output bmdboot(), output bmdfilter(b$res) (see previous section description BMD filtering options) data frame reporting results fit BMD computation selected item sorted ascending order adjusted p-values returned item selection step. columns data frame : id: item identifier irow: row number initial dataset adjpvalue: adjusted p-values returned item selection step model: best model fitted nbpar: number parameters best model (may smaller maximal number parameters model simplified version chosen) b, c, d, e, f, model parameter values SDres: residual standard deviation best model “H.inc” increasing Hill curves “H.dec” decreasing Hill curves “L.inc” increasing linear curves “L.dec” decreasing linear curves “E.inc.convex” increasing convex exponential curves “E.dec.concave” decreasing concave exponential curves “E.inc.concave” increasing concave exponential curves “E.dec.convex” decreasing convex exponential curves “GP.U” U-shape Gauss-probit curves “GP.bell” bell-shape Gauss-probit curves “GP.inc” increasing Gauss-probit curves “GP.dec” decreasing Gauss-probit curves “lGP.U” U-shape log-Gauss-probit curves “lGP.bell” bell-shape log-Gauss-probit curves “lGP.inc” increasing log-Gauss-probit curves “lGP.decreasing” decreasing log-Gauss-probit curves U shape bell shape increasing decreasing y0: y theoretical value control yatdosemax: theoretical y value maximal dose yrange: theoretical y range x within range tested doses maxychange: maximal absolute y change () control xextrem: biphasic curves, x value extremum reached yextrem: corresponding y value extremum BMD.zSD: BMD-zSD value BMR.zSD: corresponding BMR-zSD value BMD.xfold: BMD-xfold value BMR.xfold: corresponding BMR-xfold value nboot.successful: number bootstrap iterations model successfully fitted data incomplete version data frame also given end Step 3 (f$fitres f output drcfit()) bootstrap calculation BMD values (r$res r output bmdcalc()).","code":"str(b$res) ## 'data.frame': 128 obs. of 28 variables: ## $ id : chr \"383.2\" \"384.2\" \"363.1\" \"383.1\" ... ## $ irow : int 725 727 686 724 726 687 689 688 568 569 ... ## $ adjpvalue : num 2.08e-07 2.08e-07 2.24e-07 2.24e-07 3.41e-07 ... ## $ model : Factor w/ 5 levels \"Hill\",\"linear\",..: 4 3 3 4 4 3 3 3 3 3 ... ## $ nbpar : num 4 3 3 4 4 3 3 3 3 3 ... ## $ b : num 5.5836 -0.0298 -0.2058 5.4879 6.8453 ... ## $ c : num 8.58 NA NA 8.75 7.26 ... ## $ d : num 8.58 12.24 9.1 8.75 7.26 ... ## $ e : num 1.7 1.76 3.11 1.72 1.77 ... ## $ f : num 3.62 NA NA 3.58 5.01 ... ## $ SDres : num 0.157 0.16 0.218 0.169 0.158 ... ## $ typology : Factor w/ 12 levels \"E.dec.concave\",..: 5 1 1 5 5 1 1 1 2 2 ... ## $ trend : Factor w/ 4 levels \"bell\",\"dec\",\"inc\",..: 1 2 2 1 1 2 2 2 2 2 ... ## $ y0 : num 12 12.2 9.1 12.2 12.1 ... ## $ yatdosemax : num 11.03 10.99 7.58 11.15 11.15 ... ## $ yrange : num 1.17 1.25 1.53 1.18 1.11 ... ## $ maxychange : num 1.004 1.251 1.527 1.012 0.949 ... ## $ xextrem : num 1.7 NA NA 1.72 1.77 ... ## $ yextrem : num 12.2 NA NA 12.3 12.3 ... ## $ BMD.zSD : num 1.33 3.26 2.25 1.52 1.41 ... ## $ BMR.zSD : num 12.19 12.08 8.89 12.33 12.26 ... ## $ BMD.xfold : num NA 6.59 5.26 NA NA ... ## $ BMR.xfold : num 10.83 11.02 8.19 10.95 10.89 ... ## $ BMD.zSD.lower : num 0.489 1.724 1.366 0.423 0.481 ... ## $ BMD.zSD.upper : num 3.93 4.58 3.52 4.17 4.36 ... ## $ BMD.xfold.lower : num Inf 6.4 4.61 Inf Inf ... ## $ BMD.xfold.upper : num Inf Inf 5.96 Inf Inf ... ## $ nboot.successful: num 36 47 50 36 26 50 50 50 50 50 ..."},{"path":"/articles/DRomics_vignette.html","id":"interpreter","dir":"Articles","previous_headings":"","what":"Help for biological interpretation of DRomics outputs","title":"Overview of the DRomics package","text":"section illustrates functions developed DRomics help biological interpretation outputs. idea augment output data frame new column bringing biological information, generally provided biological annotation items (e.g. kegg pathway classes GO terms), use information organize visualisation DRomics output. shiny application DRomicsInterpreter-shiny can used implement steps described vignette without coding R. case, biological annotation items selected first DRomics workflow must previously done outside DRomics using database Gene Ontology (GO) kegg databases. section first present simple example metabolomic dataset [example two molecular levels] (#multilevels) using metabolomic transcriptomic data experiment, illustrate compare responses different experimental levels (example different molecular levels). different experimental levels also different time points, different experimental settings, different species, …","code":""},{"path":[]},{"path":"/articles/DRomics_vignette.html","id":"augmentation","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Interpretation of DRomics results in a simple case with only one data set obtained in one experimental condition","what":"Augmentation of the data frame of DRomics results with biological annotation","title":"Overview of the DRomics package","text":"augmentation done using DRomics functions, using simple R functions merge(). Nevertheless possible perform augmentation without coding R, using shiny application DRomicsInterpreter-shiny. Report introduction section see launch shiny application. example proceed: Import data frame DRomics results: output $res bmdcalc() bmdboot() functions Step 4 main DRomics workflow. step necessary previous steps done directly R, using DRomics package, described previously vignette (see section describing output data frame). example, order take real example took long time completely run, results stored package. Import data frame biological annotation (descriptor/category want use), KEGG pathway classes item present ‘res’ file. Examples embedded DRomics package, cautious, generally file must produced user. item may one annotation (.e. one line). items annotated whatever selected DRomics workflow , previously reduce dimension annotation file selecting items present DRomics output least one biological annotation. annotation stands one word, surround quotes, use tab column separator annotation file, import adding sep = \"\\t\" arguments read.table(). Merging previous data frames order obtain -called ‘extendedres’ data frame gathering, item, metrics derived DRomics workflow biological annotation. Arguments .x .y merge() function indicate column name res annot data frames respectively, must used merging.","code":"# code to import the file for this example stored in our package resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package = \"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) # to see the first lines of this data frame head(res) ## id irow adjpvalue model nbpar b c d e f ## 1 NAP_2 2 6.23e-05 exponential 3 0.4598 NA 5.94 -1.648 NA ## 2 NAP_23 21 1.11e-05 linear 2 -0.0595 NA 5.39 NA NA ## 3 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA ## 4 NAP_38 34 1.89e-03 exponential 3 0.6011 NA 6.86 -0.321 NA ## 5 NAP_42 38 4.16e-03 exponential 3 0.6721 NA 6.21 -0.323 NA ## 6 NAP_52 47 3.92e-02 log-Gauss-probit 5 0.4501 7.2 7.29 1.309 -0.144 ## SDres typology trend y0 yrange maxychange xextrem yextrem BMD.zSD ## 1 0.1260 E.dec.convex dec 5.94 0.456 0.456 NA NA 0.528 ## 2 0.0793 L.dec dec 5.39 0.461 0.461 NA NA 1.333 ## 3 0.0520 L.dec dec 7.86 0.350 0.350 NA NA 1.154 ## 4 0.2338 E.dec.convex dec 6.86 0.601 0.601 NA NA 0.158 ## 5 0.2897 E.dec.convex dec 6.21 0.672 0.672 NA NA 0.182 ## 6 0.0709 lGP.U U 7.29 0.191 0.191 1.46 7.1 0.732 ## BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower ## 1 5.82 NA 5.35 0.2001 1.110 Inf ## 2 5.31 NA 4.85 0.8534 1.746 7.611 ## 3 7.81 NA 7.07 0.7519 1.465 Inf ## 4 6.62 NA 6.17 0.0554 0.680 0.561 ## 5 5.92 0.832 5.59 0.0810 0.794 0.329 ## 6 7.22 NA 8.02 0.4247 1.052 Inf ## BMD.xfold.upper nboot.successful ## 1 Inf 957 ## 2 Inf 1000 ## 3 Inf 1000 ## 4 Inf 648 ## 5 Inf 620 ## 6 Inf 872 # code to import the file for this example in our package annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package = \"DRomics\") # annotfilename <- \"yourchosenname.txt\" # for a local file annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) # to see the first lines of this data frame head(annot) ## metab.code path_class ## 1 NAP_2 Lipid metabolism ## 2 NAP_23 Carbohydrate metabolism ## 3 NAP_30 Carbohydrate metabolism ## 4 NAP_30 Biosynthesis of other secondary metabolites ## 5 NAP_30 Membrane transport ## 6 NAP_30 Signal transduction # Merging extendedres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") # to see the first lines of the merged data frame head(extendedres) ## id irow adjpvalue model nbpar b c d e f SDres ## 1 NAP_2 2 6.23e-05 exponential 3 0.4598 NA 5.94 -1.65 NA 0.1260 ## 2 NAP_23 21 1.11e-05 linear 2 -0.0595 NA 5.39 NA NA 0.0793 ## 3 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## 4 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## 5 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## 6 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## typology trend y0 yrange maxychange xextrem yextrem BMD.zSD BMR.zSD ## 1 E.dec.convex dec 5.94 0.456 0.456 NA NA 0.528 5.82 ## 2 L.dec dec 5.39 0.461 0.461 NA NA 1.333 5.31 ## 3 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## 4 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## 5 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## 6 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## BMD.xfold BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower ## 1 NA 5.35 0.200 1.11 Inf ## 2 NA 4.85 0.853 1.75 7.61 ## 3 NA 7.07 0.752 1.46 Inf ## 4 NA 7.07 0.752 1.46 Inf ## 5 NA 7.07 0.752 1.46 Inf ## 6 NA 7.07 0.752 1.46 Inf ## BMD.xfold.upper nboot.successful path_class ## 1 Inf 957 Lipid metabolism ## 2 Inf 1000 Carbohydrate metabolism ## 3 Inf 1000 Carbohydrate metabolism ## 4 Inf 1000 Biosynthesis of other secondary metabolites ## 5 Inf 1000 Membrane transport ## 6 Inf 1000 Signal transduction"},{"path":[]},{"path":"/articles/DRomics_vignette.html","id":"bmdplot","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Interpretation of DRomics results in a simple case with only one data set obtained in one experimental condition > Various plots of results by biological group","what":"BMD ECDF plots split by group defined from biological annotation","title":"Overview of the DRomics package","text":"Using function bmdplot() argument facetby, BMD ECDF plot can split group (KEGG pathway class). Confidence intervals can added plot color coding trend example (See ?bmdplot options). function ecdfplotwithCI() can also used alternative provide plot differing coloring intervals . (See ?ecdfplotwithCI options.) Using function bmdplotwithgradient() argument facetby, BMD plot color gradient can split KEGG pathway class. (See ?bmdplotwithgradient options). One can focus group interest, instance group “Lipid metabolism”, add labels items using argument add.label display item identifiers instead points. case can useful control limits color gradient limits x-axis order use x-scale signal-scale global previous plot, following example (see ?bmdplotwithgradient details).","code":"bmdplot(extendedres, BMDtype = \"zSD\", add.CI = TRUE, facetby = \"path_class\", colorby = \"trend\") + theme_bw() ecdfplotwithCI(variable = extendedres$BMD.zSD, CI.lower = extendedres$BMD.zSD.lower, CI.upper = extendedres$BMD.zSD.upper, by = extendedres$path_class, CI.col = extendedres$trend) + labs(col = \"trend\") bmdplotwithgradient(extendedres, BMDtype = \"zSD\", scaling = TRUE, facetby = \"path_class\", shapeby = \"trend\") extendedres_lipid <- extendedres[extendedres$path_class == \"Lipid metabolism\",] bmdplotwithgradient(extendedres_lipid, BMDtype = \"zSD\", scaling = TRUE, facetby = \"path_class\", add.label = TRUE, xmin = 0, xmax = 6, label.size = 3, line.size = 2)"},{"path":"/articles/DRomics_vignette.html","id":"sensitivityplot","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Interpretation of DRomics results in a simple case with only one data set obtained in one experimental condition > Various plots of results by biological group","what":"Sensitivity plot of biological groups","title":"Overview of the DRomics package","text":"also possible visualize sensitivity biological group using sensitivityplot() function, choosing BMD summary argument BMDsummary fixed \"first.quartile\", \"median\" \"median..IQR\" (medians interquartile range interval). Moreover, function provide information number items involved pathway/category (coding size points). (See ?sensitivityplot options). example, ECDF plot 25th quantiles BMD-zSD calculated pathway class. possible use medians BMD values represent order groups sensitivity plot optionally add interquartile range line plot, : can customize sensitivity plot position pathway class labels next point instead y-axis, using ggplot2 functions.","code":"sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\") + theme_bw() sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"median.and.IQR\") + theme_bw() psens <- sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\") psens + theme_bw() + theme(axis.text.y = element_blank(), axis.ticks.y = element_blank()) + geom_text(aes(label = paste(\" \", psens$data$groupby, \" \")), size = 3, hjust = \"inward\")"},{"path":"/articles/DRomics_vignette.html","id":"trendplot","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Interpretation of DRomics results in a simple case with only one data set obtained in one experimental condition > Various plots of results by biological group","what":"Trend plot per biological group","title":"Overview of the DRomics package","text":"possible represent repartition trends biological group using trendplot() function (see ?trendplot details).","code":"trendplot(extendedres, group = \"path_class\") + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"curvesplot","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Interpretation of DRomics results in a simple case with only one data set obtained in one experimental condition > Various plots of results by biological group","what":"Plot of dose-response curves per biological group","title":"Overview of the DRomics package","text":"function curvesplot() can show dose-response curves obtained different groups (one chosen group). use bmdplotwithgradient(), scaling curves can used default used focus shape , amplitude signal change. use function define dose range want computation dose-response fitted curves, strongly recommend choose range corresponding range tested/observed doses dataset. code plot dose-response curves split biological group (argument facetby) colored trend (argument colorby). also possible add point BMD-BMR values curve (See ?curvesplot options). also possible using function visualize modeled response item one biological group, :","code":"# Plot of all the scaled dose-reponse curves split by path class curvesplot(extendedres, facetby = \"path_class\", scaling = TRUE, npoints = 100, colorby = \"trend\", xmax = 6.5) + theme_bw() # Plot of the unscaled dose-reponses for one chosen group, split by metabolite LMres <- extendedres[extendedres$path_class == \"Lipid metabolism\", ] curvesplot(LMres, facetby = \"id\", npoints = 100, point.size = 1.5, line.size = 1, colorby = \"trend\", scaling = FALSE, xmax = 6.5) + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"multilevels","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs","what":"Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach","title":"Overview of the DRomics package","text":"section illustrates use DRomics functions help interpretation outputs different data sets obtained different experimental levels (different molecular levels, different time points, different experimental settings, …). idea perform augmentation DRomics output data frame obtained experimental level (previously described one level), bind augmented data frames add column coding experimental level use column organize visualisation DRomics output make possible comparison responses experimental levels. used example corresponding multi-omics approach, experimental level corresponding molecular level, transcriptomic (microarray) metabolomic data set issued experiment. example uses metabolomics transcriptomics data Scenedesmus triclosan published Larras et al. 2020. possible perform without R coding within shiny application DRomicsInterpreter-shiny. Report introduction section see launch shiny application.","code":""},{"path":"/articles/DRomics_vignette.html","id":"augmentation-of-the-data-frames-of-dromics-results-with-biological-annotation","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach","what":"Augmentation of the data frames of DRomics results with biological annotation","title":"Overview of the DRomics package","text":"Following steps described one level, example R code import DRomics results microarray data, merge data frame giving biological annotation selected items. previouly created metabolomics data frame (extended results biological annotation) renamed sake homogeneity.","code":"# 1. Import the data frame with DRomics results to be used contigresfilename <- system.file(\"extdata\", \"triclosanSVcontigres.txt\", package = \"DRomics\") contigres <- read.table(contigresfilename, header = TRUE, stringsAsFactors = TRUE) # 2. Import the data frame with biological annotation (or any other descriptor/category # you want to use, here KEGG pathway classes) contigannotfilename <- system.file(\"extdata\", \"triclosanSVcontigannot.txt\", package = \"DRomics\") # contigannotfilename <- \"yourchosenname.txt\" # for a local file contigannot <- read.table(contigannotfilename, header = TRUE, stringsAsFactors = TRUE) # 3. Merging of both previous data frames contigextendedres <- merge(x = contigres, y = contigannot, by.x = \"id\", by.y = \"contig\") # to see the first lines of the data frame head(contigextendedres) ## id irow adjpvalue model nbpar b c d e f SDres ## 1 c00134 2802 2.76e-04 linear 2 -0.21794 NA 10.9 NA NA 0.417 ## 2 c00276 39331 9.40e-07 exponential 3 1.49944 NA 12.4 -2.20 NA 0.287 ## 3 c00281 41217 2.89e-06 exponential 3 1.40817 NA 12.4 -2.41 NA 0.281 ## 4 c00322 52577 1.88e-03 exponential 3 0.00181 NA 16.4 1.15 NA 0.145 ## 5 c00323 52590 1.83e-03 exponential 3 1.48605 NA 15.3 -2.31 NA 0.523 ## 6 c00380 53968 4.16e-04 exponential 3 1.31958 NA 14.5 -2.52 NA 0.395 ## typology trend y0 yrange maxychange xextrem yextrem BMD.zSD BMR.zSD ## 1 L.dec dec 10.9 1.445 1.445 NA NA 1.913 10.4 ## 2 E.dec.convex dec 12.4 1.426 1.426 NA NA 0.467 12.1 ## 3 E.dec.convex dec 12.4 1.319 1.319 NA NA 0.536 12.1 ## 4 E.inc.convex inc 16.4 0.567 0.567 NA NA 5.073 16.6 ## 5 E.dec.convex dec 15.3 1.402 1.402 NA NA 1.004 14.8 ## 6 E.dec.convex dec 14.5 1.225 1.225 NA NA 0.896 14.1 ## BMD.xfold BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower ## 1 4.98 9.77 1.255 2.759 3.94 ## 2 3.88 11.19 0.243 0.825 2.32 ## 3 5.13 11.17 0.282 0.925 2.79 ## 4 NA 18.05 2.650 5.573 Inf ## 5 NA 13.80 0.388 2.355 3.06 ## 6 NA 13.08 0.366 2.090 4.58 ## BMD.xfold.upper nboot.successful path_class ## 1 Inf 500 Energy metabolism ## 2 Inf 497 Nucleotide metabolism ## 3 Inf 495 Nucleotide metabolism ## 4 Inf 332 Translation ## 5 Inf 466 Metabolism of cofactors and vitamins ## 6 Inf 469 Folding, sorting and degradation metabextendedres <- extendedres"},{"path":"/articles/DRomics_vignette.html","id":"binding","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach","what":"Binding of the data frames corresponding the results at each experimental level","title":"Overview of the DRomics package","text":"next step bind augmented data frames results obtained different levels (transcriptomics metabolomics data frames) add variable (named level) coding level (factor two levels, metabolites contigs).","code":"extendedres <- rbind(metabextendedres, contigextendedres) extendedres$explevel <- factor(c(rep(\"metabolites\", nrow(metabextendedres)), rep(\"contigs\", nrow(contigextendedres)))) # to see the first lines of the data frame head(extendedres) ## id irow adjpvalue model nbpar b c d e f SDres ## 1 NAP_2 2 6.23e-05 exponential 3 0.4598 NA 5.94 -1.65 NA 0.1260 ## 2 NAP_23 21 1.11e-05 linear 2 -0.0595 NA 5.39 NA NA 0.0793 ## 3 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## 4 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## 5 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## 6 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## typology trend y0 yrange maxychange xextrem yextrem BMD.zSD BMR.zSD ## 1 E.dec.convex dec 5.94 0.456 0.456 NA NA 0.528 5.82 ## 2 L.dec dec 5.39 0.461 0.461 NA NA 1.333 5.31 ## 3 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## 4 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## 5 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## 6 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## BMD.xfold BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower ## 1 NA 5.35 0.200 1.11 Inf ## 2 NA 4.85 0.853 1.75 7.61 ## 3 NA 7.07 0.752 1.46 Inf ## 4 NA 7.07 0.752 1.46 Inf ## 5 NA 7.07 0.752 1.46 Inf ## 6 NA 7.07 0.752 1.46 Inf ## BMD.xfold.upper nboot.successful path_class ## 1 Inf 957 Lipid metabolism ## 2 Inf 1000 Carbohydrate metabolism ## 3 Inf 1000 Carbohydrate metabolism ## 4 Inf 1000 Biosynthesis of other secondary metabolites ## 5 Inf 1000 Membrane transport ## 6 Inf 1000 Signal transduction ## explevel ## 1 metabolites ## 2 metabolites ## 3 metabolites ## 4 metabolites ## 5 metabolites ## 6 metabolites"},{"path":"/articles/DRomics_vignette.html","id":"comparisonR","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach","what":"Comparison of results obtained at the different experimental levels using basic R functions","title":"Overview of the DRomics package","text":"examples illustrations can made using basic R functions globally compare results obtained different experimental levels, example compute plot frequencies pathways molecular levels . plot proportions, just apply function prop.table() table frequencies t.pathways. ggplot2 grammar used plot ECDF BMD_zSD using different colors different molecular levels, removing redundant lines corresponding items corresponding one pathway.","code":"(t.pathways <- table(extendedres$path_class, extendedres$explevel)) ## ## contigs metabolites ## Amino acid metabolism 54 14 ## Biosynthesis of other secondary metabolites 0 8 ## Carbohydrate metabolism 76 10 ## Energy metabolism 49 5 ## Lipid metabolism 46 12 ## Membrane transport 13 13 ## Metabolism of other amino acids 26 7 ## Signal transduction 13 8 ## Translation 68 7 ## Folding, sorting and degradation 35 0 ## Glycan biosynthesis and metabolism 14 0 ## Metabolism of cofactors and vitamins 57 0 ## Metabolism of terpenoids and polyketides 15 0 ## Nucleotide metabolism 30 0 ## Replication and repair 9 0 ## Transcription 23 0 ## Transport and catabolism 14 0 ## Xenobiotics biodegradation and metabolism 20 0 original.par <- par() par(las = 2, mar = c(4,13,1,1)) barplot(t(t.pathways), beside = TRUE, horiz = TRUE, cex.names = 0.7, legend.text = TRUE, main = \"Frequencies of pathways\") par(original.par) unique.items <- unique(extendedres$id) ggplot(extendedres[match(unique.items, extendedres$id), ], aes(x = BMD.zSD, color = explevel)) + stat_ecdf(geom = \"step\") + ylab(\"ECDF\") + theme_bw()"},{"path":[]},{"path":"/articles/DRomics_vignette.html","id":"ecdf-plot-of-bmd-values-per-group-and-experimental-level-using-dromics-functions","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach > Comparison of results obtained at the different experimental levels using DRomics functions","what":"ECDF plot of BMD values per group and experimental level using DRomics functions","title":"Overview of the DRomics package","text":"Using function bmdplot() ECDF plot BMD-zSD values can colored split experimental level /split group (KEGG pathway class) . (See ?bmdplot options, example add confidence intervals, …, previous section presenting bmdplot()).","code":"# BMD ECDF plot split by molecular level, after removing items redundancy bmdplot(extendedres[match(unique.items, extendedres$id), ], BMDtype = \"zSD\", facetby = \"explevel\", point.alpha = 0.4) + theme_bw() # BMD ECDF plot colored by molecular level and split by path class bmdplot(extendedres, BMDtype = \"zSD\", facetby = \"path_class\", colorby = \"explevel\", point.alpha = 0.4) + labs(col = \"molecular level\") + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"plot-of-the-trend-repartition-per-group-and-experimental-level","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach > Comparison of results obtained at the different experimental levels using DRomics functions","what":"Plot of the trend repartition per group and experimental level","title":"Overview of the DRomics package","text":"Using function trendplot() arguments facetby possible show repartition trends responses biological group experimental levels.","code":"# Preliminary optional alphabetic ordering of path_class groups extendedres$path_class <- factor(extendedres$path_class, levels = sort(levels(extendedres$path_class), decreasing = TRUE)) # Trend plot trendplot(extendedres, group = \"path_class\", facetby = \"explevel\") + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"sensitivity-plot-per-group-and-experimental-level","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach > Comparison of results obtained at the different experimental levels using DRomics functions","what":"Sensitivity plot per group and experimental level","title":"Overview of the DRomics package","text":"Using function sensitivityplot() arguments group colorby, possible show summary BMD values size points coding number items group example, 25th quartiles BMD values represented per KEGG pathway class molecular level. (See ?sensitivityplot options).","code":"sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", colorby = \"explevel\", BMDsummary = \"first.quartile\") + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"selectgroups","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach > Comparison of results obtained at the different experimental levels using DRomics functions","what":"Selection of groups on which to focus using the selectgroups() function","title":"Overview of the DRomics package","text":"number biological groups obtained annotation items high, may useful select groups focus, enhance visibility plots. can done example using results enrichment procedures case enrichment possible (e.g. sequenced organisms). One also use selection criteria based number items biological group (argument nitems, select represented groups, represented nitems) /BMD summary value (argument BMDmax, select sensitive groups, BMDmax). selectgroups() function can used purpose example (see ?selectgroups details). using function may optionally choose keep results experimental levels (comparison purpose) soon criteria met group least one experimental level (example fixing argument keepallexplev TRUE).","code":"selectedres <- selectgroups(extendedres, group = \"path_class\", explev = \"explevel\", BMDmax = 0.75, BMDtype = \"zSD\", BMDsummary = \"first.quartile\", nitems = 3, keepallexplev = TRUE) # BMDplot on this selection bmdplot(selectedres, BMDtype = \"zSD\", add.CI = TRUE, facetby = \"path_class\", facetby2 = \"explevel\", colorby = \"trend\") + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"bmd-ecdf-plot-with-color-gradient-split-by-group-and-experimental-level","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach > Comparison of results obtained at the different experimental levels using DRomics functions","what":"BMD ECDF plot with color gradient split by group and experimental level","title":"Overview of the DRomics package","text":"Using function bmdplotwithgradient() arguments facetby facetby2, BMD plot color gradient can split group experimental level, example manual selection pathway classes present molecular levels.(See ?bmdplotwithgradient options). Especially metabolomic data transcriptomic data imported DRomics scale (log2 transcriptomics log10 metabolomics), use scaling option dose-response curve interesting . option focuses shape responses, skipping amplitude changes control.","code":"# Manual selection of groups on which to focus chosen_path_class <- c(\"Nucleotide metabolism\", \"Membrane transport\", \"Lipid metabolism\", \"Energy metabolism\") selectedres2 <- extendedres[extendedres$path_class %in% chosen_path_class, ] bmdplotwithgradient(selectedres2, BMDtype = \"zSD\", scaling = TRUE, facetby = \"path_class\", facetby2 = \"explevel\")"},{"path":"/articles/DRomics_vignette.html","id":"plot-of-the-dose-response-curves-for-a-selection-of-groups","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach > Comparison of results obtained at the different experimental levels using DRomics functions","what":"Plot of the dose-response curves for a selection of groups","title":"Overview of the DRomics package","text":"Using function curvesplot(), specific dose-response curves can explored. following example, results related “lipid metabolism” pathway class explored, using argument facetby split experimental level. second example, plot split biological group using argument facetby experimental level using argument facetby2 . (See ?curvesplot options). scaling curves used second plot can interesting focus shapes curves, skipping amplitude changes control. helps evaluate homogeneity shapes responses within group. may example interesting observe example, transcriptomics responses (contigs) gathering shape (use scaling option - done default) just differing sign (increasing / decreasing, U-shape/bell-shape), clearly appear dose-response curves scaled (first plot).","code":"# Plot of the unscaled dose-response curves for the \"lipid metabolism\" path class # using transparency to get an idea of density of curves with the shame shape LMres <- extendedres[extendedres$path_class == \"Lipid metabolism\", ] curvesplot(LMres, facetby = \"explevel\", free.y.scales = TRUE, npoints = 100, line.alpha = 0.4, line.size = 1, colorby = \"trend\", xmax = 6.5) + labs(col = \"DR trend\") + theme_bw() # Plot of the scaled dose-response curves for previously chosen path classes curvesplot(selectedres2, scaling = TRUE, facetby = \"path_class\", facetby2 = \"explevel\", npoints = 100, line.size = 1, line.alpha = 0.4, colorby = \"trend\", xmax = 6.5) + labs(col = \"DR trend\") + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Overview of the DRomics package","text":"Burnham, KP, Anderson DR (2004). Multimodel inference: understanding AIC BIC model selection. Sociological methods & research, 33(2), 261-304. Delignette-Muller ML, Siberchicot , Larras F, Billoir E (2023). DRomics, workflow exploit dose-response omics data ecotoxicology. Peer Community Journal. doi : 10.24072/pcjournal.325. https://peercommunityjournal.org/articles/10.24072/pcjournal.325/ EFSA Scientific Committee, Hardy , Benford D, Halldorsson T, Jeger MJ, Knutsen KH, … & Schlatter JR (2017). Update: use benchmark dose approach risk assessment. EFSA Journal, 15(1), e04658.https://efsa.onlinelibrary.wiley.com/doi/full/10.2903/j.efsa.2017.4658 Hurvich, CM, Tsai, CL (1989). Regression time series model selection small samples. Biometrika, 76(2), 297-307.https://www.stat.berkeley.edu/~binyu/summer08/Hurvich.AICc.pdf Larras F, Billoir E, Baillard V, Siberchicot , Scholz S, Wubet T, Tarkka M, Schmitt-Jansen M Delignette-Muller ML (2018). DRomics : turnkey tool support use dose-response framework omics data ecological risk assessment. Environmental Science & Technology. https://pubs.acs.org/doi/10.1021/acs.est.8b04752. can also find article : https://hal.science/hal-02309919 Larras F, Billoir E, Scholz S, Tarkka M, Wubet T, Delignette-Muller ML, Schmitt-Jansen M (2020). multi-omics concentration-response framework uncovers novel understanding triclosan effects chlorophyte Scenedesmus vacuolatus. Journal Hazardous Materials. https://doi.org/10.1016/j.jhazmat.2020.122727.","code":""},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Marie-Laure Delignette-Muller. Author. Elise Billoir. Author. Floriane Larras. Contributor. Aurelie Siberchicot. Author, maintainer.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Delignette-Muller, Marie Laure; Siberchicot, Aurélie; Larras, Floriane; Billoir, Elise. DRomics, workflow exploit dose-response omics data ecotoxicology. Peer Community Journal, Volume 3 (2023). doi : 10.24072/pcjournal.325. https://peercommunityjournal.org/articles/10.24072/pcjournal.325/ Larras F, Billoir E, Baillard V, Siberchicot , Scholz S, Wubet T, Tarkka M, Schmitt-Jansen M Delignette-Muller ML (2018). DRomics: turnkey tool support use dose-response framework omics data ecological risk assessment. Environmental science & technology. URL https://doi.org/10.1021/acs.est.8b04752.","code":"@Article{, title = {DRomics: a turnkey tool to support the use of the dose-response framework for omics data in ecological risk assessment}, author = {{Marie Laure Delignette-Muller} and {Aurélie Siberchicot} and {Floriane Larras} and {Elise Billoir}}, journal = {Peer Community Journal,}, year = {2023}, doi = {10.24072/pcjournal.325}, url = {https://peercommunityjournal.org/articles/10.24072/pcjournal.325/}, } @Article{, title = {DRomics: a turnkey tool to support the use of the dose-response framework for omics data in ecological risk assessment}, author = {{Floriane Larras} and {Elise Billoir} and {Vincent Baillard} and {Aurélie Siberchicot} and {Stefan Scholz} and {Wubet Tefaye} and {Mika Tarkka} and {Mechthild Schmitt-Jansen} and {Marie Laure Delignette-Muller}}, journal = {Environmental Science & Technology}, year = {2018}, doi = {10.1021/acs.est.8b04752}, url = {https://doi.org/10.1021/acs.est.8b04752}, }"},{"path":"/index.html","id":"dromics-dose-response-for-omics-","dir":"","previous_headings":"","what":"Dose Response for Omics","title":"Dose Response for Omics","text":"Ici, une offre de stage de M2, au LBBE à Lyon / , M2 internship offer LBBE Lyon: https://www.bioinfo-lyon.fr/app/download/13442526249/offre_stage_M2_bioinfo_Siberchicot_Delignette_v2.pdf?t=1732803324 Please note! Since June 2024, repository belonged lbbe-software organization. avoid confusion, strongly recommend updating existing local clones point new repository URL. can using git remote command line: git remote set-url origin git@github.com:lbbe-software/DRomics.git git remote set-url origin https://github.com/lbbe-software/DRomics.git DRomics freely available tool dose-response (concentration-response) characterization omics data. especially dedicated omics data obtained using typical dose-response design, favoring great number tested doses (concentrations) rather great number replicates (need replicates use DRomics). first step consists importing, checking needed normalizing/transforming data (step 1), aim proposed workflow select monotonic /biphasic significantly responsive items (e.g. probes, contigs, metabolites) (step 2), choose best-fit model among predefined family monotonic biphasic models describe response selected item (step 3), derive benchmark dose concentration fitted curve (step 4). steps can performed R using DRomics functions, using shiny application named DRomics-shiny. available version, DRomics supports single-channel microarray data (log2 scale), RNAseq data (raw counts) continuous omics data metabolomics proteomics (log scale). order link responses across biological levels based common method, DRomics also handles continuous apical data long meet use conditions least squares regression (homoscedastic Gaussian regression). built environmental risk assessment context omics data often collected non-sequenced species species communities, DRomics provide annotation pipeline. annotation items selected DRomics may complex context, must done outside DRomics using databases KEGG Gene Ontology. DRomics functions can used help interpretation workflow results view biological annotation. enables multi-omics approach, comparison responses different levels organization (view common biological annotation). can also used compare responses one organization level, measured different experimental conditions (e.g. different time points). interpretation can performed R using DRomics functions, using second shiny application DRomicsInterpreter-shiny. informations DRomics can also found https://lbbe.univ-lyon1.fr/fr/dromics. Keywords : dose response modelling / benchmark dose (BMD) / environmental risk assessment / transcriptomics / proteomics / metabolomics / toxicogenomics / multi-omics","code":""},{"path":"/index.html","id":"the-package","dir":"","previous_headings":"","what":"The package","title":"Dose Response for Omics","text":"limma DESeq2 packages Bioconductor must installed use DRomics (can take long time): stable version DRomics can installed CRAN using: development version DRomics can installed GitHub (remotes needed): Finally load package current R session following R command:","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) { install.packages(\"BiocManager\") } else { BiocManager::install(ask = FALSE, update = TRUE) } BiocManager::install(c(\"limma\", \"DESeq2\")) install.packages(\"DRomics\") if (!requireNamespace(\"remotes\", quietly = TRUE)) install.packages(\"remotes\") remotes::install_github(\"lbbe-software/DRomics\") require(\"DRomics\")"},{"path":"/index.html","id":"vignette-and-cheat-sheet","dir":"","previous_headings":"","what":"Vignette and cheat sheet","title":"Dose Response for Omics","text":"vignette attached DRomics package. vignette intended help users start using DRomics package. complementary reference manual can find details function package. first part vignette (Main workflow, steps 1 4) also help users first shiny application DRomics-shiny. second part (Help biological interpretation DRomics outputs) also help users second shiny application DRomicsInterpreter-shiny. vignette can reached : Note , default, vignette installed package installed GitHub. following command (rather long execute large size vignette) allow access vignette development version package installed GitHub: cheat sheet sum DRomics workflow also available.","code":"vignette(\"DRomics_vignette\") remotes::install_github(\"lbbe-software/DRomics\", build_vignettes = TRUE)"},{"path":"/index.html","id":"two-shiny-apps","dir":"","previous_headings":"","what":"Two shiny apps","title":"Dose Response for Omics","text":"two shiny apps (DRomics-shiny DRomicsInterpreter-shiny) work DRomics available : https://lbbe-shiny.univ-lyon1.fr/DRomics/inst/DRomics-shiny/ https://lbbe-shiny.univ-lyon1.fr/DRomics/inst/DRomicsInterpreter-shiny/ https://biosphere.france-bioinformatique.fr/catalogue/appliance/176/ DRomics-shiny https://biosphere.france-bioinformatique.fr/catalogue/appliance/209/ DRomicsInterpreter-shiny install.packages(c(\"shiny\", \"shinyBS\", \"shinycssloaders\", \"shinyjs\", \"shinyWidgets\", \"sortable\")) shiny::runApp(system.file(\"DRomics-shiny\", package = \"DRomics\")) shiny::runApp(system.file(\"DRomicsInterpreter-shiny\", package = \"DRomics\")) shiny apps runing development version DRomics.","code":""},{"path":"/index.html","id":"authors--contacts","dir":"","previous_headings":"","what":"Authors & Contacts","title":"Dose Response for Omics","text":"need yet covered, feedback package / Shiny app, training needs, feel free email us dromics@univ-lyon1.fr . Issues can reported https://github.com/lbbe-software/DRomics/issues . Elise Billoir: elise.billoir@univ-lorraine.fr Marie-Laure Delignette-Muller: marielaure.delignettemuller@vetagro-sup.fr Floriane Larras: floriane.larras@kreatis.eu Mechthild Schmitt-Jansen: mechthild.schmitt@ufz.de Aurélie Siberchicot: aurelie.siberchicot@univ-lyon1.fr","code":""},{"path":"/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Dose Response for Omics","text":"use Dromics, cite: Delignette-Muller ML, Siberchicot , Larras F, Billoir E (2023). DRomics, workflow exploit dose-response omics data ecotoxicology. Peer Community Journal. https://peercommunityjournal.org/articles/10.24072/pcjournal.325/ Larras F, Billoir E, Baillard V, Siberchicot , Scholz S, Wubet T, Tarkka M, Schmitt-Jansen M Delignette-Muller ML (2018). DRomics : turnkey tool support use dose-response framework omics data ecological risk assessment. Environmental Science & Technology. https://pubs.acs.org/doi/10.1021/acs.est.8b04752 can find article : https://hal.science/hal-02309919 can also look following citation complete example use: Larras F, Billoir E, Scholz S, Tarkka M, Wubet T, Delignette-Muller ML, Schmitt-Jansen M (2020). multi-omics concentration-response framework uncovers novel understanding triclosan effects chlorophyte Scenedesmus vacuolatus. Journal Hazardous Materials. https://doi.org/10.1016/j.jhazmat.2020.122727.","code":""},{"path":"/reference/PCAdataplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs and plots the results of a PCA on omic data — PCAdataplot","title":"Performs and plots the results of a PCA on omic data — PCAdataplot","text":"Provides two dimensional plot (two first components) principal component analysis (PCA) performed omic data normalization /transformation, check promiximity samples exposed dose optionally presence/absence potential batch effect.","code":""},{"path":"/reference/PCAdataplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs and plots the results of a PCA on omic data — PCAdataplot","text":"","code":"PCAdataplot(omicdata, batch, label)"},{"path":"/reference/PCAdataplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs and plots the results of a PCA on omic data — PCAdataplot","text":"omicdata object class \"microarraydata\", \"RNAseqdata\" \"continuousomicdata\" respectively returned functions microarraydata, RNAseqdata continuousomicdata. batch Optionnally factor coding potential batch effect (factor length number samples dataset). label FALSE (default choice), TRUE character vector defining sample names. two last cases, points replaced labels samples (batch identified shape points, may appear sample names.","code":""},{"path":"/reference/PCAdataplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs and plots the results of a PCA on omic data — PCAdataplot","text":"ggplot object.","code":""},{"path":"/reference/PCAdataplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Performs and plots the results of a PCA on omic data — PCAdataplot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/PCAdataplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs and plots the results of a PCA on omic data — PCAdataplot","text":"","code":"# (1) on a microarray dataset # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package=\"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. print(o) #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 100 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"6.1\" \"7.1\" \"8.1\" \"9.1\" \"10.1\" #> [11] \"11.1\" \"12.1\" \"13.1\" \"14.1\" \"15\" \"16.1\" \"17.1\" \"18.1\" \"19.1\" \"20.1\" #> Data were normalized between arrays using the following method: cyclicloess plot(o) PCAdataplot(o) PCAdataplot(o, label = TRUE) samplenames <- paste0(\"sample\", 1:ncol(o$data)) PCAdataplot(o, label = samplenames) # \\donttest{ # (2) an example on an RNAseq dataset with a potential batch effect # data(zebraf) str(zebraf) #> List of 3 #> $ counts: int [1:1000, 1:16] 453 331 897 12 326 533 1948 904 583 154 ... #> ..- attr(*, \"dimnames\")=List of 2 #> .. ..$ : chr [1:1000] \"ENSDARG00000102141\" \"ENSDARG00000102123\" \"ENSDARG00000114503\" \"ENSDARG00000115971\" ... #> .. ..$ : chr [1:16] \"I10_05mG_E5\" \"I10_05mG_E6\" \"I10_05mG_E7\" \"I10_C5\" ... #> $ dose : num [1:16] 500 500 500 0 0 0 0 50000 50000 50000 ... #> $ batch : Factor w/ 2 levels \"I10\",\"I17\": 1 1 1 1 1 1 1 2 2 2 ... data4DRomics <- formatdata4DRomics(signalmatrix = zebraf$counts, dose = zebraf$dose) o <- RNAseqdata(data4DRomics, transfo.method = \"vst\") #> converting counts to integer mode #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. PCAdataplot(o, batch = zebraf$batch) PCAdataplot(o, label = TRUE) # }"},{"path":"/reference/RNAseqdata.html","id":null,"dir":"Reference","previous_headings":"","what":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"RNAseq data raw counts (integer values) imported .txt file (internally imported using function read.table), checked R object class data.frame (see description argument file required format data), normalized respect library size tranformed log2 scale using variance stabilizing transformation regularized logarithm.","code":""},{"path":"/reference/RNAseqdata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"","code":"RNAseqdata(file, backgrounddose, check = TRUE, transfo.method, transfo.blind = TRUE, round.counts = FALSE) # S3 method for class 'RNAseqdata' print(x, ...) # S3 method for class 'RNAseqdata' plot(x, range4boxplot = 0, ...)"},{"path":"/reference/RNAseqdata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"file name .txt file (e.g. \"mydata.txt\") containing one row per item, first column corresponding identifier item, columns giving responses item replicate dose concentration. first line, name identifier column, must tested doses concentrations numeric format corresponding replicate (example, triplicates treatment, first line \"item\", 0, 0, 0, 0.1, 0.1, 0.1, etc.). file imported within function using function read.table default field separator (sep argument) default decimal separator (dec argument \".\"). Alternatively R object class data.frame can directly given input, corresponding output read.table(file, header = FALSE) file described . two alternatives illustrated examples. backgrounddose argument must used dose zero data, prevent calculation BMD extrapolation. doses equal value given backgrounddose fixed 0, considered background level exposition. check TRUE format input file checked. transfo.method method chosen transform raw counts log2 scale using DESeq2: \"rlog\" regularized logarithm \"vst\" variance stabilizing transformation. missing, default value defined \"rlog\" datasets less 30 samples \"vst\" transfo.blind Argument given function rlog vst, see rlog vst explaination, default TRUE DESeq2 package . round.counts Put TRUE counts come Kallisto Salmon order round treatment DESeq2. x object class \"RNAseqdata\". range4boxplot argument passed boxplot(), fixed default 0 prevent producing large plot files due many outliers. Can put 1.5 obtain classical boxplots. ... arguments passed print plot functions.","code":""},{"path":"/reference/RNAseqdata.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"function imports data, checks format (see description argument file required format data) gives print information help user check coding data correct : tested doses (concentrations) number replicates dose, number items, identifiers first 20 items. Data normalized respect library size tranformed using functions rlog vst DESeq2 package depending specified method : \"rlog\" (recommended default choice) \"vst\".","code":""},{"path":"/reference/RNAseqdata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"RNAseqdata returns object class \"RNAseqdata\", list 9 components: data numeric matrix normalized transformed responses item replicate (one line per item, one column per replicate) dose numeric vector tested doses concentrations corresponding column data item character vector identifiers items, corresponding line data design table experimental design (tested doses number replicates dose) control user data.mean numeric matrix mean responses item per dose (mean corresponding replicates) (one line per item, one column per unique value dose) data.sd numeric matrix standard deviations response item per dose (sd corresponding replicates, NA replicate) (one line per item, one column per unique value dose) transfo.method transformation method specified input raw.counts numeric matrix non transformed responses (raw counts) item replicate (one line per item, one column per replicate) normalization containsNA always FALSE RNAseq data allowed contain NA values print RNAseqdata object gives tested doses (concentrations) number replicates dose, number items, identifiers first 20 items (check good coding data) tranformation method. plot RNAseqdata object shows data distribution dose concentration replicate normalization tranformation.","code":""},{"path":[]},{"path":"/reference/RNAseqdata.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"Love MI, Huber W, Anders S (2014), Moderated estimation fold change dispersion RNA-seq data DESeq2. Genome biology, 15(12), 550.","code":""},{"path":"/reference/RNAseqdata.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/RNAseqdata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"","code":"# (1) import, check, normalization and transformation of RNAseq data # An example on a subsample of a data set published by Zhou et al. 2017 # Effect on mouse kidney transcriptomes of tetrachloroethylene # (see ? Zhou for details) # datafilename <- system.file(\"extdata\", \"RNAseq_sample.txt\", package=\"DRomics\") (o <- RNAseqdata(datafilename, check = TRUE, transfo.method = \"vst\")) #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 999 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: vst plot(o) # If you want to use your own data set just replace datafilename, # the first argument of RNAseqdata(), # by the name of your data file (e.g. \"mydata.txt\") # # You should take care that the field separator of this data file is one # of the default field separators recognised by the read.table() function # when it is used with its default field separator (sep argument) # Tabs are recommended. # Use of an R object of class data.frame # below the same example taking a subsample of the data set # Zhou_kidney_pce (see ?Zhou for details) data(Zhou_kidney_pce) subsample <- Zhou_kidney_pce[1:1000, ] (o <- RNAseqdata(subsample, check = TRUE, transfo.method = \"vst\")) #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 999 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: vst plot(o) PCAdataplot(o) # (2) transformation with two methods on the whole data set # \\donttest{ data(Zhou_kidney_pce) # variance stabilizing tranformation (o1 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = \"vst\")) #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 33394 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: vst plot(o1) # regularized logarithm (o2 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = \"rlog\")) #> Just wait, the transformation using regularized logarithm (rlog) may #> take a few minutes. #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 33394 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: rlog plot(o2) # variance stabilizing tranformation (blind to the experimental design) (o3 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = \"vst\", transfo.blind = TRUE)) #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 33394 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: vst plot(o3) # regularized logarithm (o4 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = \"rlog\", transfo.blind = TRUE)) #> Just wait, the transformation using regularized logarithm (rlog) may #> take a few minutes. #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 33394 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: rlog plot(o4) # }"},{"path":"/reference/Scenedesmus.html","id":null,"dir":"Reference","previous_headings":"","what":"Concentration-response effect of triclosan in Scenedesmus vacuolatus — Scenedesmus","title":"Concentration-response effect of triclosan in Scenedesmus vacuolatus — Scenedesmus","text":"Metabolomic apical data sets effect triclosan chlorophyte Scenedesmus vacuolatus.","code":""},{"path":"/reference/Scenedesmus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Concentration-response effect of triclosan in Scenedesmus vacuolatus — Scenedesmus","text":"","code":"data(Scenedesmus_metab) data(Scenedesmus_apical)"},{"path":"/reference/Scenedesmus.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Concentration-response effect of triclosan in Scenedesmus vacuolatus — Scenedesmus","text":"Scenedesmus_metab contains one row per metabolite, first column corresponding identifier metabolite, columns giving log10 tranformed area curve replicate concentration. first line, name identifier column, tested concentrations corresponding replicate. Scenedesmus_apical contains one row per apical endpoint, first column corresponding identifier endpoint, columns giving measured value endpoint replicate concentration. first line, name identifier column, tested concentrations corresponding replicate.","code":""},{"path":"/reference/Scenedesmus.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Concentration-response effect of triclosan in Scenedesmus vacuolatus — Scenedesmus","text":"Larras, F., Billoir, E., Scholz, S., Tarkka, M., Wubet, T., Delignette-Muller, M. L., & Schmitt-Jansen, M. (2020). multi-omics concentration-response framework uncovers novel understanding triclosan effects chlorophyte Scenedesmus vacuolatus. Journal Hazardous Materials, 122727.","code":""},{"path":"/reference/Scenedesmus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Concentration-response effect of triclosan in Scenedesmus vacuolatus — Scenedesmus","text":"","code":"# (1.1) load of metabolomics data # data(Scenedesmus_metab) head(Scenedesmus_metab) #> V1 V2 V3 V4 V5 V6 V7 V8 #> 1 metab.code 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 7.760000 #> 2 NAP_1 4.338845 4.727077 4.664407 4.741994 4.338845 4.667462 4.338845 #> 3 NAP_2 5.923194 5.997305 5.897229 6.092802 5.966068 5.733371 5.548711 #> 4 NAP_3 4.780252 4.890248 5.103817 5.060089 5.037458 4.829921 4.948354 #> 5 NAP_4 4.027370 4.457973 4.027370 4.027370 4.350887 4.027370 4.027370 #> 6 NAP_5 5.269317 4.660272 5.407287 5.282763 4.660272 4.660272 5.306268 #> V9 V10 V11 V12 V13 V14 V15 V16 #> 1 4.780000 2.920000 1.790000 1.100000 0.690000 7.760000 7.760000 4.780000 #> 2 4.639875 4.684765 4.338845 4.338845 4.855040 4.338845 4.927042 4.338845 #> 3 5.478389 5.708228 5.585534 5.832640 5.853180 5.425401 5.590360 5.478412 #> 4 4.863668 4.923078 4.922019 4.870656 5.071359 4.869461 5.115907 5.135603 #> 5 4.027370 4.027370 4.027370 4.027370 4.027370 4.027370 4.027370 4.027370 #> 6 4.660272 5.342616 5.295892 4.660272 5.319847 5.104808 4.660272 5.219089 #> V17 V18 V19 V20 V21 V22 V23 V24 #> 1 4.780000 2.920000 2.920000 1.790000 1.790000 1.100000 1.100000 0.690000 #> 2 4.338845 4.338845 4.733983 4.338845 4.338845 4.338845 5.078072 4.338845 #> 3 5.460895 5.485156 5.448148 5.582259 5.700495 5.976869 5.435696 5.875684 #> 4 5.002352 5.325395 4.479222 4.979134 5.164020 5.067967 5.279681 5.166167 #> 5 4.027370 4.027370 4.027370 4.521230 4.328400 4.422097 4.689859 4.492537 #> 6 4.660272 4.660272 4.961302 4.660272 4.660272 4.660272 5.455795 5.462184 #> V25 #> 1 0.690000 #> 2 4.703429 #> 3 5.656397 #> 4 5.018734 #> 5 4.027370 #> 6 4.660272 str(Scenedesmus_metab) #> 'data.frame':\t225 obs. of 25 variables: #> $ V1 : chr \"metab.code\" \"NAP_1\" \"NAP_2\" \"NAP_3\" ... #> $ V2 : num 0 4.34 5.92 4.78 4.03 ... #> $ V3 : num 0 4.73 6 4.89 4.46 ... #> $ V4 : num 0 4.66 5.9 5.1 4.03 ... #> $ V5 : num 0 4.74 6.09 5.06 4.03 ... #> $ V6 : num 0 4.34 5.97 5.04 4.35 ... #> $ V7 : num 0 4.67 5.73 4.83 4.03 ... #> $ V8 : num 7.76 4.34 5.55 4.95 4.03 ... #> $ V9 : num 4.78 4.64 5.48 4.86 4.03 ... #> $ V10: num 2.92 4.68 5.71 4.92 4.03 ... #> $ V11: num 1.79 4.34 5.59 4.92 4.03 ... #> $ V12: num 1.1 4.34 5.83 4.87 4.03 ... #> $ V13: num 0.69 4.86 5.85 5.07 4.03 ... #> $ V14: num 7.76 4.34 5.43 4.87 4.03 ... #> $ V15: num 7.76 4.93 5.59 5.12 4.03 ... #> $ V16: num 4.78 4.34 5.48 5.14 4.03 ... #> $ V17: num 4.78 4.34 5.46 5 4.03 ... #> $ V18: num 2.92 4.34 5.49 5.33 4.03 ... #> $ V19: num 2.92 4.73 5.45 4.48 4.03 ... #> $ V20: num 1.79 4.34 5.58 4.98 4.52 ... #> $ V21: num 1.79 4.34 5.7 5.16 4.33 ... #> $ V22: num 1.1 4.34 5.98 5.07 4.42 ... #> $ V23: num 1.1 5.08 5.44 5.28 4.69 ... #> $ V24: num 0.69 4.34 5.88 5.17 4.49 ... #> $ V25: num 0.69 4.7 5.66 5.02 4.03 ... # \\donttest{ # (1.2) import and check of metabolomics data # (o_metab <- continuousomicdata(Scenedesmus_metab)) #> Warning: #> We recommend you to check that your omic data were correctly pretreated #> before importation. In particular data (e.g. metabolomic signal) should #> have been log-transformed, without replacing 0 values by NA values #> (consider using the half minimum method instead for example). #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.1 1.79 2.92 4.78 7.76 #> 6 3 3 3 3 3 3 #> Number of items: 224 #> Identifiers of the first 20 items: #> #> [1] \"NAP_1\" \"NAP_2\" \"NAP_3\" \"NAP_4\" \"NAP_5\" \"NAP_6\" \"NAP_7\" \"NAP_8\" #> [9] \"NAP_9\" \"NAP_11\" \"NAP_13\" \"NAP_14\" \"NAP_15\" \"NAP_16\" \"NAP_17\" \"NAP_18\" #> [17] \"NAP_19\" \"NAP_20\" \"NAP_21\" \"NAP_22\" plot(o_metab) # (2.1) load of apical data # data(Scenedesmus_apical) head(Scenedesmus_apical) #> V1 V2 V3 V4 V5 V6 V7 V8 #> 1 endpoint 0.10000 0.10000 0.10000 0.10000 0.10000 0.10000 0.1000 #> 2 growth 4.05405 -3.86402 -0.40118 0.21115 4.78474 -0.28645 -1.9627 #> 3 photosynthesis 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.0000 #> V9 V10 V11 V12 V13 V14 V15 V16 #> 1 0.10000 0.10000 0.10000 0.10000 0.10000 2.40000 2.40000 2.40000 #> 2 -2.53559 8.21172 -4.63598 -1.64234 -1.93339 -7.98142 7.33857 3.34705 #> 3 0.00000 0.00000 0.00000 0.00000 0.00000 -7.18853 -4.50187 -8.42766 #> V17 V18 V19 V20 V21 V22 V23 V24 #> 1 2.40000 2.40000 2.40000 3.80000 3.80000 6.20000 6.20000 10.10000 #> 2 -2.55707 -8.68987 -11.48974 1.41102 -10.93750 -4.08453 -12.98564 2.45072 #> 3 -5.63618 -11.47670 -6.96251 -7.53671 -8.01109 -6.37088 -8.79645 -7.67328 #> V25 V26 V27 V28 V29 V30 V31 V32 #> 1 10.10000 16.50000 16.50000 16.50000 16.50000 16.50000 16.50000 26.80000 #> 2 -3.71622 27.30152 29.05854 17.60842 14.03268 18.95971 16.65211 64.81147 #> 3 -7.28024 -6.78936 -12.60403 -5.77177 -5.86055 -18.67683 -14.58052 -5.76963 #> V33 V34 V35 V36 V37 #> 1 26.8000 43.50000 43.50000 70.70000 70.70000 #> 2 58.3826 77.06083 72.71959 72.94341 81.89225 #> 3 5.8997 22.08560 19.35689 21.28191 35.79860 str(Scenedesmus_apical) #> 'data.frame':\t3 obs. of 37 variables: #> $ V1 : chr \"endpoint\" \"growth\" \"photosynthesis\" #> $ V2 : num 0.1 4.05 0 #> $ V3 : num 0.1 -3.86 0 #> $ V4 : num 0.1 -0.401 0 #> $ V5 : num 0.1 0.211 0 #> $ V6 : num 0.1 4.78 0 #> $ V7 : num 0.1 -0.286 0 #> $ V8 : num 0.1 -1.96 0 #> $ V9 : num 0.1 -2.54 0 #> $ V10: num 0.1 8.21 0 #> $ V11: num 0.1 -4.64 0 #> $ V12: num 0.1 -1.64 0 #> $ V13: num 0.1 -1.93 0 #> $ V14: num 2.4 -7.98 -7.19 #> $ V15: num 2.4 7.34 -4.5 #> $ V16: num 2.4 3.35 -8.43 #> $ V17: num 2.4 -2.56 -5.64 #> $ V18: num 2.4 -8.69 -11.48 #> $ V19: num 2.4 -11.49 -6.96 #> $ V20: num 3.8 1.41 -7.54 #> $ V21: num 3.8 -10.94 -8.01 #> $ V22: num 6.2 -4.08 -6.37 #> $ V23: num 6.2 -13 -8.8 #> $ V24: num 10.1 2.45 -7.67 #> $ V25: num 10.1 -3.72 -7.28 #> $ V26: num 16.5 27.3 -6.79 #> $ V27: num 16.5 29.1 -12.6 #> $ V28: num 16.5 17.61 -5.77 #> $ V29: num 16.5 14.03 -5.86 #> $ V30: num 16.5 19 -18.7 #> $ V31: num 16.5 16.7 -14.6 #> $ V32: num 26.8 64.81 -5.77 #> $ V33: num 26.8 58.4 5.9 #> $ V34: num 43.5 77.1 22.1 #> $ V35: num 43.5 72.7 19.4 #> $ V36: num 70.7 72.9 21.3 #> $ V37: num 70.7 81.9 35.8 # (2.2) import and check of apical data # (o_apical <- continuousanchoringdata(Scenedesmus_apical, backgrounddose = 0.1)) #> Warning: #> We recommend you to check that your anchoring data are continuous and #> defined in a scale that enable the use of a normal error model (needed #> at each step of the workflow including the selection step). #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 2.4 3.8 6.2 10.1 16.5 26.8 43.5 70.7 #> 12 6 2 2 2 6 2 2 2 #> Number of endpoints: 2 #> Names of the endpoints: #> [1] \"growth\" \"photosynthesis\" # It is here necessary to define the background dose as there is no dose at 0 in the data # The BMD cannot be computed without defining the background level plot(o_apical) #> Warning: log-10 transformation introduced infinite values. # (2.3) selection of responsive endpoints on apical data # (s_apical <- itemselect(o_apical, select.method = \"quadratic\", FDR = 0.05)) #> Number of selected items using a quadratic trend test with an FDR of 0.05: 2 #> Identifiers of the responsive items: #> [1] \"growth\" \"photosynthesis\" # (2.4) fit of dose-response models on apical data # (f_apical <- drcfit(s_apical, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |=================================== | 50% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> Distribution of the chosen models among the 2 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 0 0 0 2 #> log-Gauss-probit #> 0 #> Distribution of the trends (curve shapes) among the 2 fitted dose-response curves: #> #> U #> 2 f_apical$fitres #> id irow adjpvalue model nbpar b c #> 1 growth 1 4.120696e-17 Gauss-probit 4 12.20929 77.03040 #> 2 photosynthesis 2 2.669437e-13 Gauss-probit 4 16.87413 28.65168 #> d e f SDres typology trend y0 yatdosemax #> 1 77.03040 5.38810 -84.19789 5.236305 GP.U U 0.644969 77.03035 #> 2 28.65168 12.88431 -40.04502 3.788006 GP.U U -1.267411 28.53860 #> yrange maxychange xextrem yextrem #> 1 84.19784 76.38538 5.38810 -7.167487 #> 2 39.93195 29.80601 12.88431 -11.393343 plot(f_apical) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. plot(f_apical, dose_log_trans = TRUE) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. plot(f_apical, plot.type = \"dose_residuals\") #> Warning: log-10 transformation introduced infinite values. # (2.5) Benchmark dose calculation on apical data # r_apical <- bmdcalc(f_apical, z = 1) r_apical$res #> id irow adjpvalue model nbpar b c #> 1 growth 1 4.120696e-17 Gauss-probit 4 12.20929 77.03040 #> 2 photosynthesis 2 2.669437e-13 Gauss-probit 4 16.87413 28.65168 #> d e f SDres typology trend y0 yatdosemax #> 1 77.03040 5.38810 -84.19789 5.236305 GP.U U 0.644969 77.03035 #> 2 28.65168 12.88431 -40.04502 3.788006 GP.U U -1.267411 28.53860 #> yrange maxychange xextrem yextrem BMD.zSD BMR.zSD BMD.xfold #> 1 84.19784 76.38538 5.38810 -7.167487 2.344346 -4.591336 0.02400000 #> 2 39.93195 29.80601 12.88431 -11.393343 2.978888 -5.055417 0.09375678 #> BMR.xfold #> 1 0.5804721 #> 2 -1.3941523 # }"},{"path":"/reference/Zhou.html","id":null,"dir":"Reference","previous_headings":"","what":"Dose-response kidney transcriptomic effect of Tetrachloroethylene in mouse — Zhou","title":"Dose-response kidney transcriptomic effect of Tetrachloroethylene in mouse — Zhou","text":"RNAseq data set effect Tetrachloroethylene (PCE) mouse kidney. environmental contaminant administered gavage aqueous vehicle male B6C3F1/J mice, within dose-reponse design including five doses plus control.","code":""},{"path":"/reference/Zhou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dose-response kidney transcriptomic effect of Tetrachloroethylene in mouse — Zhou","text":"","code":"data(Zhou_kidney_pce)"},{"path":"/reference/Zhou.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Dose-response kidney transcriptomic effect of Tetrachloroethylene in mouse — Zhou","text":"Zhou_kidney_pce contains one row per transcript, first column corresponding identifier transcript, columns giving count reads replicate dose. first line, name identifier column, tested doses corresponding replicate.","code":""},{"path":"/reference/Zhou.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Dose-response kidney transcriptomic effect of Tetrachloroethylene in mouse — Zhou","text":"Zhou, Y. H., Cichocki, J. ., Soldatow, V. Y., Scholl, E. H., Gallins, P. J., Jima, D., ... & Rusyn, . 2017. Comparative dose-response analysis liver kidney transcriptomic effects trichloroethylene tetrachloroethylene B6C3F1 mouse. Toxicological sciences, 160(1), 95-110.","code":""},{"path":"/reference/Zhou.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dose-response kidney transcriptomic effect of Tetrachloroethylene in mouse — Zhou","text":"","code":"# (1) load of data # data(Zhou_kidney_pce) head(Zhou_kidney_pce) #> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 #> 1 RefSeq 0 0 0.22 0.22 0.22 0.67 0.67 0.67 2 #> 2 NM_144958 2072 2506 2519.00 2116.00 1999.00 2113.00 2219.00 2322.00 2359 #> 3 NR_102758 0 0 0.00 0.00 0.00 0.00 0.00 0.00 0 #> 4 NM_172405 198 265 250.00 245.00 212.00 206.00 227.00 246.00 265 #> 5 NM_029777 18 29 25.00 19.00 19.00 13.00 22.00 19.00 19 #> 6 NM_001130188 0 0 0.00 0.00 0.00 0.00 0.00 1.00 0 #> V11 V12 V13 V14 V15 #> 1 2 2 6 6 6 #> 2 1932 1705 2110 2311 2140 #> 3 0 0 0 0 0 #> 4 205 175 288 315 242 #> 5 26 16 26 32 33 #> 6 0 0 1 0 1 str(Zhou_kidney_pce) #> 'data.frame':\t33395 obs. of 15 variables: #> $ V1 : chr \"RefSeq\" \"NM_144958\" \"NR_102758\" \"NM_172405\" ... #> $ V2 : int 0 2072 0 198 18 0 0 3 0 61 ... #> $ V3 : int 0 2506 0 265 29 0 0 1 0 65 ... #> $ V4 : num 0.22 2519 0 250 25 ... #> $ V5 : num 0.22 2116 0 245 19 ... #> $ V6 : num 0.22 1999 0 212 19 ... #> $ V7 : num 0.67 2113 0 206 13 ... #> $ V8 : num 0.67 2219 0 227 22 ... #> $ V9 : num 0.67 2322 0 246 19 ... #> $ V10: int 2 2359 0 265 19 0 0 0 0 91 ... #> $ V11: int 2 1932 0 205 26 0 0 0 0 59 ... #> $ V12: int 2 1705 0 175 16 0 0 0 0 47 ... #> $ V13: int 6 2110 0 288 26 1 0 0 0 42 ... #> $ V14: int 6 2311 0 315 32 0 0 0 0 60 ... #> $ V15: int 6 2140 0 242 33 1 0 2 1 58 ... # \\donttest{ # (2) import, check, normalization and transformation of a sample # of one of those datasets # d <- Zhou_kidney_pce[1:501, ] (o <- RNAseqdata(d)) #> Just wait, the transformation using regularized logarithm (rlog) may #> take a few minutes. #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 500 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: rlog plot(o) # (3) analysis of the whole dataset (for kidney and PCE) # (may be long to run) d <- Zhou_kidney_pce (o <- RNAseqdata(d)) #> Just wait, the transformation using regularized logarithm (rlog) may #> take a few minutes. #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 33394 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: rlog plot(o) (s <- itemselect(o, select.method = \"quadratic\", FDR = 0.01)) #> converting counts to integer mode #> the design formula contains one or more numeric variables with integer values, #> specifying a model with increasing fold change for higher values. #> did you mean for this to be a factor? if so, first convert #> this variable to a factor using the factor() function #> the design formula contains one or more numeric variables that have mean or #> standard deviation larger than 5 (an arbitrary threshold to trigger this message). #> Including numeric variables with large mean can induce collinearity with the intercept. #> Users should center and scale numeric variables in the design to improve GLM convergence. #> estimating size factors #> estimating dispersions #> gene-wise dispersion estimates #> mean-dispersion relationship #> final dispersion estimates #> fitting model and testing #> Number of selected items using a quadratic trend test with an FDR of 0.01: 930 #> Identifiers of the first 20 most responsive items: #> [1] \"NM_012055\" \"NM_026929\" \"NM_134188\" \"NM_175093\" \"NM_008638\" #> [6] \"NM_180678\" \"NM_012006\" \"NM_011076\" \"NM_001302163\" \"NM_007918\" #> [11] \"NM_028994\" \"NM_172015\" \"NM_146200\" \"NM_001081318\" \"NM_011704\" #> [16] \"NM_017399\" \"NM_007822\" \"NM_010011\" \"NM_144869\" \"NM_146230\" (f <- drcfit(s, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | 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|======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 25 dose-response curves out of 930 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 905 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 1 769 26 86 #> log-Gauss-probit #> 23 #> Distribution of the trends (curve shapes) among the 905 fitted dose-response curves: #> #> U bell dec inc #> 67 41 363 434 head(f$fitres) #> id irow adjpvalue model nbpar b c d #> 1 NM_012055 22032 6.756994e-42 Hill 4 2.70352170 9.81098 7.546171 #> 2 NM_026929 14409 4.585837e-37 linear 2 0.31249103 NA 5.726443 #> 3 NM_134188 986 5.247894e-36 linear 2 0.23123224 NA 10.418319 #> 4 NM_175093 26225 3.843143e-33 exponential 3 0.23781018 NA 5.356573 #> 5 NM_008638 30943 1.187148e-32 linear 2 0.27254307 NA 7.543362 #> 6 NM_180678 14173 9.154882e-29 linear 2 0.08641914 NA 11.552080 #> e f SDres typology trend y0 yatdosemax yrange #> 1 1.885806 NA 0.21185102 H.inc inc 7.546171 9.716030 2.1698591 #> 2 NA NA 0.25622487 L.inc inc 5.726443 7.601389 1.8749462 #> 3 NA NA 0.17560544 L.inc inc 10.418319 11.805713 1.3873934 #> 4 2.906675 NA 0.16172244 E.inc.convex inc 5.356573 6.992494 1.6359211 #> 5 NA NA 0.23350538 L.inc inc 7.543362 9.178620 1.6352584 #> 6 NA NA 0.04747107 L.inc inc 11.552080 12.070595 0.5185148 #> maxychange xextrem yextrem #> 1 2.1698591 NA NA #> 2 1.8749462 NA NA #> 3 1.3873934 NA NA #> 4 1.6359211 NA NA #> 5 1.6352584 NA NA #> 6 0.5185148 NA NA plot(f) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. plot(f, dose_log_trans = TRUE) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. plot(f, plot.type = \"dose_residuals\") #> Warning: log-10 transformation introduced infinite values. r <- bmdcalc(f, z = 1) plot(r) plot(r, by = \"trend\") head(r$res) #> id irow adjpvalue model nbpar b c d #> 1 NM_012055 22032 6.756994e-42 Hill 4 2.70352170 9.81098 7.546171 #> 2 NM_026929 14409 4.585837e-37 linear 2 0.31249103 NA 5.726443 #> 3 NM_134188 986 5.247894e-36 linear 2 0.23123224 NA 10.418319 #> 4 NM_175093 26225 3.843143e-33 exponential 3 0.23781018 NA 5.356573 #> 5 NM_008638 30943 1.187148e-32 linear 2 0.27254307 NA 7.543362 #> 6 NM_180678 14173 9.154882e-29 linear 2 0.08641914 NA 11.552080 #> e f SDres typology trend y0 yatdosemax yrange #> 1 1.885806 NA 0.21185102 H.inc inc 7.546171 9.716030 2.1698591 #> 2 NA NA 0.25622487 L.inc inc 5.726443 7.601389 1.8749462 #> 3 NA NA 0.17560544 L.inc inc 10.418319 11.805713 1.3873934 #> 4 2.906675 NA 0.16172244 E.inc.convex inc 5.356573 6.992494 1.6359211 #> 5 NA NA 0.23350538 L.inc inc 7.543362 9.178620 1.6352584 #> 6 NA NA 0.04747107 L.inc inc 11.552080 12.070595 0.5185148 #> maxychange xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold #> 1 2.1698591 NA NA 0.8140636 7.758022 1.458975 8.300788 #> 2 1.8749462 NA NA 0.8199431 5.982668 1.832514 6.299087 #> 3 1.3873934 NA NA 0.7594332 10.593925 4.505565 11.460151 #> 4 1.6359211 NA NA 1.5080487 5.518295 3.428164 5.892230 #> 5 1.6352584 NA NA 0.8567651 7.776867 2.767769 8.297698 #> 6 0.5185148 NA NA 0.5493120 11.599551 NA 12.707288 # }"},{"path":"/reference/bmdboot.html","id":null,"dir":"Reference","previous_headings":"","what":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"Computes 95 percent confidence intervals x-fold z-SD benchmark doses bootstrap.","code":""},{"path":"/reference/bmdboot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"","code":"bmdboot(r, items = r$res$id, niter = 1000, conf.level = 0.95, tol = 0.5, progressbar = TRUE, parallel = c(\"no\", \"snow\", \"multicore\"), ncpus) # S3 method for class 'bmdboot' print(x, ...) # S3 method for class 'bmdboot' plot(x, BMDtype = c(\"zSD\", \"xfold\"), remove.infinite = TRUE, by = c(\"none\", \"trend\", \"model\", \"typology\"), CI.col = \"blue\", BMD_log_transfo = TRUE, ...)"},{"path":"/reference/bmdboot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"r object class \"bmdcalc\" returned function bmdcalc. items character vector specifying identifiers items want computation confidence intervals. omitted computation done items. niter number samples drawn bootstrap. conf.level Confidence level intervals. tol tolerance term proportion bootstrap samples fit model successful (proportion tolerance, NA values given limits confidence interval. progressbar TRUE progress bar used follow bootstrap process. parallel type parallel operation used, \"snow\" \"multicore\" (second one available Windows), \"\" parallel operation. ncpus Number processes used parallel operation : typically one fix number available CPUs. x object class \"bmdboot\". BMDtype type BMD plot, \"zSD\" (default choice) \"xfold\". remove.infinite TRUE confidence intervals non finite upper bound plotted. \"none\" plot split indicated factor (\"trend\", \"model\" \"typology\"). CI.col color draw confidence intervals. BMD_log_transfo TRUE, default option, log transformation BMD used plot. ... arguments passed graphical print functions.","code":""},{"path":"/reference/bmdboot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"Non-parametric bootstrapping used, mean centered residuals bootstrapped. item, bootstrapped parameter estimates obtained fitting model resampled data sets. fitting procedure fails converge tol*100% cases, NA values given confidence interval. Otherwise, bootstraped BMD computed bootstrapped parameter estimates using method bmdcalc. Confidence intervals BMD computed using percentiles bootstrapped BMDs. example 95 percent confidence intervals computed using 2.5 97.5 percentiles bootstrapped BMDs. cases bootstrapped BMD estimated reached highest tested dose reachable due model asymptotes, given infinite value Inf, enable computation lower limit BMD confidence interval sufficient number bootstrapped BMD values estimated finite values.","code":""},{"path":"/reference/bmdboot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"bmdboot returns object class \"bmdboot\", list 3 components: res data frame reporting results fit, BMD computation bootstrap specified item sorted ascending order adjusted p-values. different columns correspond identifier item (id), row number item initial data set (irow), adjusted p-value selection step (adjpvalue), name best fit model (model), number fitted parameters (nbpar), values parameters b, c, d, e f, (NA non used parameters), residual standard deviation (SDres), typology curve (typology, (16 class typology described help drcfit function)), rough trend curve (trend) defined four classes (U, bell, increasing decreasing shape), theoretical y value control (y0), theoretical y value maximal dose yatdosemax), theoretical y range x within range tested doses (yrange), maximal absolute y change () control(maxychange) biphasic curves x value extremum reached (xextrem) corresponding y value (yextrem), BMD-zSD value (BMD.zSD) corresponding BMR-zSD value (reached , BMR.zSD) BMD-xfold value (BMD.xfold) corresponding BMR-xfold value (reached , BMR.xfold), BMD.zSD.lower BMD.zSD.upper lower upper bounds confidence intervals BMD-zSD value, BMD.xfold.lower BMD.xfold.upper lower upper bounds confidence intervals BMD-xfold value nboot.successful number successful fits bootstrapped samples item. z Value z given input define BMD-zSD. x Value x given input percentage define BMD-xfold. tol tolerance given input term tolerated proportion failures fit bootstrapped samples. niter number samples drawn bootstrap (given input).","code":""},{"path":[]},{"path":"/reference/bmdboot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"Huet S, Bouvier , Poursat M-, Jolivet E (2003) Statistical tools nonlinear regression: practical guide S-PLUS R examples. Springer, Berlin, Heidelberg, New York.","code":""},{"path":"/reference/bmdboot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/bmdboot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"","code":"# (1) a toy example (a very small subsample of a microarray data set) # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package = \"DRomics\") # to test the package on a small but not very small data set # use the following commented line # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package = \"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.001) #> Removing intercept from test coefficients f <- drcfit(s_quad, progressbar = TRUE) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |======= | 10% | |============== | 20% | |===================== | 30% | |============================ | 40% | |=================================== | 50% | |========================================== | 60% | |================================================= | 70% | |======================================================== | 80% | |=============================================================== | 90% | |======================================================================| 100% r <- bmdcalc(f) set.seed(1234) # to get reproducible results with a so small number of iterations (b <- bmdboot(r, niter = 5)) # with a non reasonable value for niter #> Warning: #> A small number of iterations (less than 1000) may not be sufficient to #> ensure a good quality of bootstrap confidence intervals. #> The bootstrap may be long if the number of items and the number of #> bootstrap iterations is high. #> | | | 0% | |======= | 10% | |============== | 20% | |===================== | 30% | |============================ | 40% | |=================================== | 50% | |========================================== | 60% | |================================================= | 70% | |======================================================== | 80% | |=============================================================== | 90% | |======================================================================| 100% #> Bootstrap confidence interval computation was successful on 10 items among10. #> For 0 BMD.zSD values and 4 BMD.xfold values among 10 at least one bound #> of the 95 percent confidence interval could not be computed due to some #> bootstrapped BMD values not reachable due to model asymptotes or #> reached outside the range of tested doses (bounds coded Inf)). # !!!! TO GET CORRECT RESULTS # !!!! niter SHOULD BE FIXED FAR LARGER , e.g. to 1000 # !!!! but the run will be longer b$res #> id irow adjpvalue model nbpar b c d #> 1 15 15 1.546048e-05 exponential 3 0.071422368 NA 7.740153 #> 2 12.1 12 2.869315e-05 Gauss-probit 5 0.414151082 8.903415 7.564374 #> 3 27.1 27 3.087292e-05 linear 2 -0.108446801 NA 15.608419 #> 4 25.1 25 1.597308e-04 exponential 3 -0.128807120 NA 15.142111 #> 5 4 4 2.302448e-04 Gauss-probit 4 3.079188189 9.851210 9.851210 #> 6 70 70 2.323292e-04 exponential 3 -0.007515088 NA 6.682254 #> 7 7.1 7 2.712029e-04 Gauss-probit 4 2.384260578 9.122630 9.122630 #> 8 88.1 88 4.566344e-04 Gauss-probit 4 2.103260654 11.157946 11.157946 #> 9 92 92 4.566344e-04 Gauss-probit 4 8.381660245 -27.802234 -27.802234 #> 10 81 81 6.448977e-04 exponential 3 -0.025717119 NA 6.713592 #> e f SDres typology trend y0 yatdosemax #> 1 2.276377 NA 0.3183292 E.inc.convex inc 7.740153 8.983706 #> 2 1.131878 0.7204105 0.3802228 GP.bell bell 7.585780 8.903415 #> 3 NA NA 0.1648041 L.dec dec 15.608419 14.889308 #> 4 3.404150 NA 0.2142472 E.dec.concave dec 15.142111 14.367457 #> 5 1.959459 -1.6121603 0.2994936 GP.U U 8.534547 9.341173 #> 6 1.074077 NA 1.1263294 E.dec.concave dec 6.682254 3.082935 #> 7 1.735801 -0.9436037 0.2594717 GP.U U 8.398699 9.007963 #> 8 1.755034 0.7172904 0.2131311 GP.bell bell 11.664352 11.206771 #> 9 2.557094 37.5298696 1.1492863 GP.bell bell 8.021106 5.546334 #> 10 1.357555 NA 1.1925115 E.dec.concave dec 6.713592 3.338828 #> yrange maxychange xextrem yextrem BMD.zSD BMR.zSD BMD.xfold #> 1 1.2435537 1.2435537 NA NA 3.8627796 8.058482 5.6254804 #> 2 1.5579862 1.5579862 1.438980 9.143766 0.5682965 7.966002 0.7867277 #> 3 0.7191107 0.7191107 NA NA 1.5196769 15.443615 NA #> 4 0.7746535 0.7746535 NA NA 3.3346126 14.927864 NA #> 5 1.1021236 0.8066266 1.959459 8.239050 4.9146813 8.834040 NA #> 6 3.5993187 3.5993187 NA NA 5.3880607 5.555924 4.8321611 #> 7 0.8289366 0.6092640 1.735801 8.179027 4.5746396 8.658171 NA #> 8 0.6684651 0.4575814 1.755034 11.875236 4.5680110 11.451221 NA #> 9 4.1813022 2.4747723 2.557094 9.727636 1.1073035 9.170392 0.7057605 #> 10 3.3747644 3.3747644 NA NA 5.2374434 5.521081 4.4795770 #> BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper #> 1 8.514168 2.5419700 5.4286722 5.2096765 6.3162984 #> 2 8.344358 0.3962742 0.6113321 0.7585065 0.8436521 #> 3 14.047577 1.2880859 1.8703357 Inf Inf #> 4 13.627900 1.7779710 4.3860469 Inf Inf #> 5 9.388001 0.8764847 4.6482205 6.2736049 6.5002416 #> 6 6.014028 5.5427871 5.8066178 4.8002579 5.2522007 #> 7 9.238569 0.8306627 5.0191965 Inf Inf #> 8 10.497917 0.8761450 1.3426819 Inf Inf #> 9 8.823216 0.6833626 1.7755504 0.5927639 0.9811746 #> 10 6.042233 4.3970380 5.6776339 3.2853517 5.1984168 #> nboot.successful #> 1 5 #> 2 3 #> 3 5 #> 4 5 #> 5 3 #> 6 3 #> 7 4 #> 8 5 #> 9 3 #> 10 4 plot(b) # plot of BMD.zSD after removing of BMDs with infinite upper bounds # \\donttest{ # same plot in raw scale (without log transformation of BMD values) plot(b, BMD_log_transfo = FALSE) # plot of BMD.zSD without removing of BMDs # with infinite upper bounds plot(b, remove.infinite = FALSE) # } # bootstrap on only a subsample of items # with a greater number of iterations # \\donttest{ chosenitems <- r$res$id[1:5] (b.95 <- bmdboot(r, items = chosenitems, niter = 1000, progressbar = TRUE)) #> The bootstrap may be long if the number of items and the number of #> bootstrap iterations is high. #> | | | 0% | |============== | 20% | |============================ | 40% | |========================================== | 60% | |======================================================== | 80% | |======================================================================| 100% #> Bootstrap confidence interval computation was successful on 5 items among5. #> For 0 BMD.zSD values and 3 BMD.xfold values among 5 at least one bound #> of the 95 percent confidence interval could not be computed due to some #> bootstrapped BMD values not reachable due to model asymptotes or #> reached outside the range of tested doses (bounds coded Inf)). b.95$res #> id irow adjpvalue model nbpar b c d #> 1 15 15 1.546048e-05 exponential 3 0.07142237 NA 7.740153 #> 2 12.1 12 2.869315e-05 Gauss-probit 5 0.41415108 8.903415 7.564374 #> 3 27.1 27 3.087292e-05 linear 2 -0.10844680 NA 15.608419 #> 4 25.1 25 1.597308e-04 exponential 3 -0.12880712 NA 15.142111 #> 5 4 4 2.302448e-04 Gauss-probit 4 3.07918819 9.851210 9.851210 #> e f SDres typology trend y0 yatdosemax #> 1 2.276377 NA 0.3183292 E.inc.convex inc 7.740153 8.983706 #> 2 1.131878 0.7204105 0.3802228 GP.bell bell 7.585780 8.903415 #> 3 NA NA 0.1648041 L.dec dec 15.608419 14.889308 #> 4 3.404150 NA 0.2142472 E.dec.concave dec 15.142111 14.367457 #> 5 1.959459 -1.6121603 0.2994936 GP.U U 8.534547 9.341173 #> yrange maxychange xextrem yextrem BMD.zSD BMR.zSD BMD.xfold #> 1 1.2435537 1.2435537 NA NA 3.8627796 8.058482 5.6254804 #> 2 1.5579862 1.5579862 1.438980 9.143766 0.5682965 7.966002 0.7867277 #> 3 0.7191107 0.7191107 NA NA 1.5196769 15.443615 NA #> 4 0.7746535 0.7746535 NA NA 3.3346126 14.927864 NA #> 5 1.1021236 0.8066266 1.959459 8.239050 4.9146813 8.834040 NA #> BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper #> 1 8.514168 1.9234542 5.3597201 4.288446 6.313835 #> 2 8.344358 0.2467196 0.8196903 0.507739 1.068040 #> 3 14.047577 0.9228184 2.1008927 Inf Inf #> 4 13.627900 1.5729443 5.0433579 Inf Inf #> 5 9.388001 0.5446487 5.1515178 5.792727 Inf #> nboot.successful #> 1 932 #> 2 559 #> 3 1000 #> 4 931 #> 5 745 # Plot of fits with BMD values and confidence intervals # with the default BMD.zSD plot(f, items = chosenitems, BMDoutput = b.95, BMDtype = \"zSD\") #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # with the default BMD.xfold plot(f, items = chosenitems, BMDoutput = b.95, BMDtype = \"xfold\") #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: Removed 3 rows containing missing values or values outside the scale range #> (`geom_vline()`). # same bootstrap but changing the default confidence level (0.95) to 0.90 (b.90 <- bmdboot(r, items = chosenitems, niter = 1000, conf.level = 0.9, progressbar = TRUE)) #> The bootstrap may be long if the number of items and the number of #> bootstrap iterations is high. #> | | | 0% | |============== | 20% | |============================ | 40% | |========================================== | 60% | |======================================================== | 80% | |======================================================================| 100% #> Bootstrap confidence interval computation was successful on 5 items among5. #> For 0 BMD.zSD values and 3 BMD.xfold values among 5 at least one bound #> of the 95 percent confidence interval could not be computed due to some #> bootstrapped BMD values not reachable due to model asymptotes or #> reached outside the range of tested doses (bounds coded Inf)). b.90$res #> id irow adjpvalue model nbpar b c d #> 1 15 15 1.546048e-05 exponential 3 0.07142237 NA 7.740153 #> 2 12.1 12 2.869315e-05 Gauss-probit 5 0.41415108 8.903415 7.564374 #> 3 27.1 27 3.087292e-05 linear 2 -0.10844680 NA 15.608419 #> 4 25.1 25 1.597308e-04 exponential 3 -0.12880712 NA 15.142111 #> 5 4 4 2.302448e-04 Gauss-probit 4 3.07918819 9.851210 9.851210 #> e f SDres typology trend y0 yatdosemax #> 1 2.276377 NA 0.3183292 E.inc.convex inc 7.740153 8.983706 #> 2 1.131878 0.7204105 0.3802228 GP.bell bell 7.585780 8.903415 #> 3 NA NA 0.1648041 L.dec dec 15.608419 14.889308 #> 4 3.404150 NA 0.2142472 E.dec.concave dec 15.142111 14.367457 #> 5 1.959459 -1.6121603 0.2994936 GP.U U 8.534547 9.341173 #> yrange maxychange xextrem yextrem BMD.zSD BMR.zSD BMD.xfold #> 1 1.2435537 1.2435537 NA NA 3.8627796 8.058482 5.6254804 #> 2 1.5579862 1.5579862 1.438980 9.143766 0.5682965 7.966002 0.7867277 #> 3 0.7191107 0.7191107 NA NA 1.5196769 15.443615 NA #> 4 0.7746535 0.7746535 NA NA 3.3346126 14.927864 NA #> 5 1.1021236 0.8066266 1.959459 8.239050 4.9146813 8.834040 NA #> BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper #> 1 8.514168 2.1636242 5.1282797 4.4988439 6.218120 #> 2 8.344358 0.2862136 0.7639272 0.5873191 1.026054 #> 3 14.047577 0.9791835 2.0226321 Inf Inf #> 4 13.627900 1.8299433 4.8873535 Inf Inf #> 5 9.388001 0.6371109 4.9854763 5.9397256 Inf #> nboot.successful #> 1 942 #> 2 580 #> 3 1000 #> 4 949 #> 5 769 # } # (2) an example on a microarray data set (a subsample of a greater data set) # # \\donttest{ datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 1000 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" #> [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" #> Data were normalized between arrays using the following method: cyclicloess (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.001)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.001: 78 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |= | 1% | |== | 3% | |=== | 4% | |==== | 5% | |==== | 6% | |===== | 8% | |====== | 9% | |======= | 10% | |======== | 12% | |========= | 13% | |========== | 14% | |=========== | 15% | |============ | 17% | |============= | 18% | |============= | 19% | |============== | 21% | |=============== | 22% | |================ | 23% | |================= | 24% | |================== | 26% | |=================== | 27% | |==================== | 28% | |===================== | 29% | |====================== | 31% | |====================== | 32% | |======================= | 33% | |======================== | 35% | |========================= | 36% | |========================== | 37% | |=========================== | 38% | |============================ | 40% | |============================= | 41% | |============================== | 42% | |=============================== | 44% | 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|=============================================================== | 90% | |================================================================ | 91% | |================================================================= | 92% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |===================================================================== | 99% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 11 dose-response curves out of 78 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 67 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 0 11 30 23 #> log-Gauss-probit #> 3 #> Distribution of the trends (curve shapes) among the 67 fitted dose-response curves: #> #> U bell dec inc #> 6 20 22 19 (r <- bmdcalc(f)) #> 1 BMD-xfold values and 0 BMD-zSD values are not defined (coded NaN as #> the BMR stands outside the range of response values defined by the model). #> 28 BMD-xfold values and 0 BMD-zSD values could not be calculated (coded #> NA as the BMR stands within the range of response values defined by the #> model but outside the range of tested doses). (b <- bmdboot(r, niter = 100)) # niter to put at 1000 for a better precision #> Warning: #> A small number of iterations (less than 1000) may not be sufficient to #> ensure a good quality of bootstrap confidence intervals. #> The bootstrap may be long if the number of items and the number of #> bootstrap iterations is high. #> | | | 0% | |= | 1% | |== | 3% | |=== | 4% | |==== | 6% | |===== | 7% | |====== | 9% | |======= | 10% | |======== | 12% | |========= | 13% | |========== | 15% | |=========== | 16% | |============= | 18% | |============== | 19% | |=============== | 21% | |================ | 22% | |================= | 24% | |================== | 25% | |=================== | 27% | |==================== | 28% | |===================== | 30% | |====================== | 31% | |======================= | 33% | |======================== | 34% | |========================= | 36% | |========================== | 37% | |=========================== | 39% | |============================ | 40% | |============================= | 42% | |============================== | 43% | |=============================== | 45% | |================================ | 46% | |================================= | 48% | |================================== | 49% | |==================================== | 51% | |===================================== | 52% | |====================================== | 54% | |======================================= | 55% | |======================================== | 57% | |========================================= | 58% | |=========================================== | 61% | |============================================ | 63% | |============================================= | 64% | |============================================== | 66% | |=============================================== | 67% | |================================================ | 69% | |================================================= | 70% | |================================================== | 72% | |=================================================== | 73% | |==================================================== | 75% | |===================================================== | 76% | |====================================================== | 78% | |======================================================== | 81% | |========================================================= | 82% | |============================================================ | 85% | |============================================================= | 87% | |============================================================== | 88% | |=============================================================== | 90% | |================================================================ | 91% | |================================================================= | 93% | |================================================================== | 94% | |==================================================================== | 97% | |===================================================================== | 99% | |======================================================================| 100% #> Bootstrap confidence interval computation failed on 4 items among 67 #> due to lack of convergence of the model fit for a fraction of the #> bootstrapped samples greater than 0.5. #> For 0 BMD.zSD values and 36 BMD.xfold values among 67 at least one #> bound of the 95 percent confidence interval could not be computed due #> to some bootstrapped BMD values not reachable due to model asymptotes #> or reached outside the range of tested doses (bounds coded Inf)). # different plots of BMD-zSD plot(b) #> Warning: #> 4 BMD values for which lower and upper bounds were coded NA or with #> lower or upper infinite bounds were removed before plotting. plot(b, by = \"trend\") #> Warning: #> 4 BMD values for which lower and upper bounds were coded NA or with #> lower or upper infinite bounds were removed before plotting. plot(b, by = \"model\") #> Warning: #> 4 BMD values for which lower and upper bounds were coded NA or with #> lower or upper infinite bounds were removed before plotting. plot(b, by = \"typology\") #> Warning: #> 4 BMD values for which lower and upper bounds were coded NA or with #> lower or upper infinite bounds were removed before plotting. # a plot of BMD-xfold (by default BMD-zSD is plotted) plot(b, BMDtype = \"xfold\") #> Warning: #> 40 BMD values for which lower and upper bounds were coded NA or with #> lower or upper infinite bounds were removed before plotting. # } # (3) Comparison of parallel and non parallel implementations # # \\donttest{ # to be tested with a greater number of iterations if(!requireNamespace(\"parallel\", quietly = TRUE)) { if(parallel::detectCores() > 1) { system.time(b1 <- bmdboot(r, niter = 100, progressbar = TRUE)) system.time(b2 <- bmdboot(r, niter = 100, progressbar = FALSE, parallel = \"snow\", ncpus = 2)) }} # }"},{"path":"/reference/bmdcalc.html","id":null,"dir":"Reference","previous_headings":"","what":"Computation of benchmark doses for responsive items — bmdcalc","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"Computes x-fold z-SD benchmark doses responsive item using best fit dose-reponse model.","code":""},{"path":"/reference/bmdcalc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"","code":"bmdcalc(f, z = 1, x = 10, minBMD, ratio2switchinlog = 100) # S3 method for class 'bmdcalc' print(x, ...) # S3 method for class 'bmdcalc' plot(x, BMDtype = c(\"zSD\", \"xfold\"), plottype = c(\"ecdf\", \"hist\", \"density\"), by = c(\"none\", \"trend\", \"model\", \"typology\"), hist.bins = 30, BMD_log_transfo = TRUE, ...)"},{"path":"/reference/bmdcalc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"f object class \"drcfit\" returned function drcfit. z Value z defining BMD-zSD dose response reaching y0 +/- z * SD, y0 level control given dose-response fitted model SD residual standard deviation dose-response fitted model. x Value x given percentage defining BMD-xfold dose response reaching y0 +/- (x/100) * y0, y0 level control given dose-response fitted model. print plot functions, object class \"bmdcalc\". minBMD minimal value calculated BMDs, value considered negligible compared tested doses. given user argument fixed minimal non null tested dose divided 100. ratio2switchinlog ratio maximal minimal tested doses numerical computation (use uniroot necessary) BMD performed log scale dose. BMDtype type BMD plot, \"zSD\" (default choice) \"xfold\". plottype type plot, \"ecdf\" empirical cumulative distribution plot (default choice), \"hist\" histogram \"density\" density plot. different \"none\" plot split trend (\"trend\"), model (\"model\") typology (\"typology\"). hist.bins number bins, used histogram(s). BMD_log_transfo TRUE, default option, log transformation BMD used plot. ... arguments passed graphical print functions.","code":""},{"path":"/reference/bmdcalc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"two types benchmark doses (BMD) proposed EFSA (2017) computed responsive item using best fit dose-reponse model previously obtained using drcfit function (see Larras et al. 2018 details): BMD-zSD defined dose response reaching y0 +/- z * SD, y0 level control given dose-response model, SD residual standard deviation dose response model fit z given input (z fixed 1 default), BMD-xfold defined dose response reaching y0 +/- (x/100) * y0, y0 level control given dose-response fitted model x percentage given input (x fixed 10 default.) analytical solution BMD, numerically searched along fitted curve using uniroot function. cases BMD reached due asymptote high doses, NaN returned. cases BMD reached highest tested dose, NA returned. low BMD values obtained extrapolation 0 smallest non null tested dose, correspond sensitive items (want exclude), thresholded minBMD, argument default fixed smallest non null tested dose divided 100, can fixed user considers negligible dose.","code":""},{"path":"/reference/bmdcalc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"bmdcalc returns object class \"bmdcalc\", list 4 components: res data frame reporting results fit BMD computation selected item sorted ascending order adjusted p-values returned function itemselect. different columns correspond identifier item (id), row number item initial data set (irow), adjusted p-value selection step (adjpvalue), name best fit model (model), number fitted parameters (nbpar), values parameters b, c, d, e f, (NA non used parameters), residual standard deviation (SDres), typology curve (typology, (16 class typology described help drcfit function)), rough trend curve (trend) defined four classes (U, bell, increasing decreasing shape), theoretical y value control (y0), theoretical y value maximal dose yatdosemax), theoretical y range x within range tested doses (yrange), maximal absolute y change () control(maxychange) biphasic curves x value extremum reached (xextrem) corresponding y value (yextrem), BMD-zSD value (BMD.zSD) corresponding BMR-zSD value (reached , BMR.zSD) BMD-xfold value (BMD.xfold) corresponding BMR-xfold value (reached , BMR.xfold). z Value z given input define BMD-zSD. x Value x given input percentage define BMD-xfold. minBMD minimal value calculated BMDs given input fixed minimal non null tested dose divided 100. ratio2switchinlog ratio maximal minimal tested doses numerical computations performed log scale (given input). omicdata corresponding object given input (component itemselect).","code":""},{"path":[]},{"path":"/reference/bmdcalc.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"EFSA Scientific Committee, Hardy , Benford D, Halldorsson T, Jeger MJ, Knutsen KH, ... & Schlatter JR (2017). Update: use benchmark dose approach risk assessment. EFSA Journal, 15(1), e04658. Larras F, Billoir E, Baillard V, Siberchicot , Scholz S, Wubet T, Tarkka M, Schmitt-Jansen M Delignette-Muller ML (2018). DRomics: turnkey tool support use dose-response framework omics data ecological risk assessment. Environmental science & technology.doi:10.1021/acs.est.8b04752","code":""},{"path":"/reference/bmdcalc.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"Marie-Laure Delignette-Muller Elise Billoir","code":""},{"path":"/reference/bmdcalc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"","code":"# (1) a toy example (a very small subsample of a microarray data set) # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package=\"DRomics\") # to test the package on a small (for a quick calculation) but not very small data set # use the following commented line # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 100 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"6.1\" \"7.1\" \"8.1\" \"9.1\" \"10.1\" #> [11] \"11.1\" \"12.1\" \"13.1\" \"14.1\" \"15\" \"16.1\" \"17.1\" \"18.1\" \"19.1\" \"20.1\" #> Data were normalized between arrays using the following method: cyclicloess (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.01)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.01: 17 #> Identifiers of the responsive items: #> [1] \"15\" \"12.1\" \"27.1\" \"25.1\" \"4\" \"70\" \"7.1\" \"88.1\" \"92\" \"81\" #> [11] \"13.1\" \"74.1\" \"83.1\" \"84.1\" \"54.1\" \"85.1\" \"67.1\" (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |==== | 6% | |======== | 12% | |============ | 18% | |================ | 24% | |===================== | 29% | |========================= | 35% | |============================= | 41% | |================================= | 47% | |===================================== | 53% | |========================================= | 59% | |============================================= | 65% | |================================================= | 71% | |====================================================== | 76% | |========================================================== | 82% | |============================================================== | 88% | |================================================================== | 94% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> Distribution of the chosen models among the 17 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 0 4 6 6 #> log-Gauss-probit #> 1 #> Distribution of the trends (curve shapes) among the 17 fitted dose-response curves: #> #> U bell dec inc #> 4 3 6 4 (r <- bmdcalc(f)) #> 9 BMD-xfold values and 0 BMD-zSD values could not be calculated (coded #> NA as the BMR stands within the range of response values defined by the #> model but outside the range of tested doses). plot(r) # \\donttest{ # same plot in raw scale of BMD (without log transformation of BMD values) plot(r, BMD_log_transfo = FALSE) # changing the values of z and x for BMD calculation (rb <- bmdcalc(f, z = 2, x = 50)) #> 2 BMD-xfold values and 0 BMD-zSD values are not defined (coded NaN as #> the BMR stands outside the range of response values defined by the model). #> 13 BMD-xfold values and 2 BMD-zSD values could not be calculated (coded #> NA as the BMR stands within the range of response values defined by the #> model but outside the range of tested doses). plot(rb) #> Warning: #> 2 BMD coded NA or NaN were removed before plotting. # } # Plot of fits with BMD values # \\donttest{ # example with the BMD-1SD plot(f, BMDoutput = r, BMDtype = \"zSD\") #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # example with the BMD-2SD plot(f, BMDoutput = rb, BMDtype = \"zSD\") #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: Removed 2 rows containing missing values or values outside the scale range #> (`geom_vline()`). # example with the BMD-xfold with x = 10 percent plot(f, BMDoutput = r, BMDtype = \"xfold\") #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: Removed 9 rows containing missing values or values outside the scale range #> (`geom_vline()`). # } # (2) an example on a microarray data set (a subsample of a greater data set) # # \\donttest{ datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") # to test the package on a small (for a quick calculation) but not very small data set # use the following commented line # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 1000 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" #> [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" #> Data were normalized between arrays using the following method: cyclicloess (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.01)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.01: 183 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | 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93% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 30 dose-response curves out of 183 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 153 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 1 44 44 59 #> log-Gauss-probit #> 5 #> Distribution of the trends (curve shapes) among the 153 fitted dose-response curves: #> #> U bell dec inc #> 29 35 40 49 (r <- bmdcalc(f)) #> 2 BMD-xfold values and 0 BMD-zSD values are not defined (coded NaN as #> the BMR stands outside the range of response values defined by the model). #> 82 BMD-xfold values and 0 BMD-zSD values could not be calculated (coded #> NA as the BMR stands within the range of response values defined by the #> model but outside the range of tested doses). plot(r) # different plots of BMD-zSD plot(r, plottype = \"hist\") plot(r, plottype = \"density\") plot(r, plottype = \"density\", by = \"trend\") plot(r, plottype = \"ecdf\", by = \"trend\") plot(r, plottype = \"ecdf\", by = \"model\") plot(r, plottype = \"ecdf\", by = \"typology\") # a plot of BMD-xfold (by default BMD-zSD is plotted) plot(r, BMDtype = \"xfold\", plottype = \"hist\", by = \"typology\", hist.bins = 10) #> Warning: #> 84 BMD coded NA or NaN were removed before plotting. # }"},{"path":"/reference/bmdfilter.html","id":null,"dir":"Reference","previous_headings":"","what":"Filtering BMDs according to estimation quality — bmdfilter","title":"Filtering BMDs according to estimation quality — bmdfilter","text":"Filtering BMDs DRomics workflow output according estimation quality, retain best estimated BMDs subsequent biological annotation interpretation.","code":""},{"path":"/reference/bmdfilter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filtering BMDs according to estimation quality — bmdfilter","text":"","code":"bmdfilter(res, BMDfilter = c(\"definedCI\", \"finiteCI\", \"definedBMD\", \"none\"), BMDtype = c(\"zSD\", \"xfold\"))"},{"path":"/reference/bmdfilter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filtering BMDs according to estimation quality — bmdfilter","text":"res dataframe results provided bmdboot bmdcalc (res) subset data frame. Even function intended used just calculation BMD values, biological annotation, can also used within interpretation workflow, extended dataframe additional columns coming example biological annotation items, lines replicated items one annotation. case dataframe must least contain column giving BMD values (BMD.zSD BMD.xfold depending chosen BMDtype), identification curve (id), BMDfilter set \"CIdefined\" \"CIfinite\", columns BMD.zSD.lower, BMD.zSD.upper BMD.xfold.lower, BMD.xfold.upper depending argument BMDtype. BMDfilter \"none\", type filter applied, based BMD estimation. \"definedCI\" (default choice), items point interval estimates BMD successfully calculated retained (items bootstrap procedure failed excluded). \"finiteCI\", items point interval estimates BMD successfully calculated gave values within range tested/observed doses retained. \"definedBMD\", items point estimate BMD estimated value within range tested/observed doses retained. BMDtype type BMD used previously described filtering procedure, \"zSD\" (default choice) \"xfold\".","code":""},{"path":"/reference/bmdfilter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filtering BMDs according to estimation quality — bmdfilter","text":"Using argument BMDfilter three filters proposed retain items associated best estimated BMD values. default recommend retain items BMD confidence interval defined (using \"CIdefined\") (excluding items bootstrap procedure failed). One can even restrictive retaining items BMD confidence interval within range tested/observed doses (using \"CIfinite\"), less restrictive (using \"BMDIdefined\") requiring BMD point estimate must defined within range tested/observed doses (let us recall bmdcalc output, case BMD coded NA). propose option \"none\" case, future, add filters based BMD.","code":""},{"path":"/reference/bmdfilter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filtering BMDs according to estimation quality — bmdfilter","text":"dataframe corresponding subset res given input, can used biological annotation exploration.","code":""},{"path":[]},{"path":"/reference/bmdfilter.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Filtering BMDs according to estimation quality — bmdfilter","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/bmdfilter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filtering BMDs according to estimation quality — bmdfilter","text":"","code":"# (1) a toy example # on a very small subsample of a microarray data set # and a very smal number of bootstrap iterations # (clearly not sufficient, but it is just for illustration) # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package = \"DRomics\") # to test the package on a small but not very small data set # use the following commented line # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package = \"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.05) #> Removing intercept from test coefficients f <- drcfit(s_quad, progressbar = TRUE) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% r <- bmdcalc(f) set.seed(1234) # to get reproducible results with a so small number of iterations (b <- bmdboot(r, niter = 10)) # with a non reasonable value for niter #> Warning: #> A small number of iterations (less than 1000) may not be sufficient to #> ensure a good quality of bootstrap confidence intervals. #> The bootstrap may be long if the number of items and the number of #> bootstrap iterations is high. #> | | | 0% | |==== | 5% | |======= | 10% | |========== | 15% | |============== | 20% | |================== | 25% | |===================== | 30% | |======================== | 35% | |============================ | 40% | |=================================== | 50% | |====================================== | 55% | |========================================== | 60% | |============================================== | 65% | |==================================================== | 75% | |======================================================== | 80% | |============================================================ | 85% | |=============================================================== | 90% | |================================================================== | 95% | |======================================================================| 100% #> Bootstrap confidence interval computation failed on 2 items among 20 #> due to lack of convergence of the model fit for a fraction of the #> bootstrapped samples greater than 0.5. #> For 3 BMD.zSD values and 12 BMD.xfold values among 20 at least one #> bound of the 95 percent confidence interval could not be computed due #> to some bootstrapped BMD values not reachable due to model asymptotes #> or reached outside the range of tested doses (bounds coded Inf)). # !!!! TO GET CORRECT RESULTS # !!!! niter SHOULD BE FIXED FAR LARGER , e.g. to 1000 # !!!! but the run will be longer ### (1.a) Examples on BMD.xfold (with some undefined BMD.xfold values) # Plot of BMDs with no filtering subres <- bmdfilter(b$res, BMDfilter = \"none\") bmdplot(subres, BMDtype = \"xfold\", point.size = 3, add.CI = TRUE) #> Warning: Removed 10 rows containing missing values or values outside the scale range #> (`geom_point()`). #> Warning: Removed 2 rows containing missing values or values outside the scale range #> (`geom_errorbarh()`). # Plot of items with defined BMD point estimate subres <- bmdfilter(b$res, BMDtype = \"xfold\", BMDfilter = \"definedBMD\") bmdplot(subres, BMDtype = \"xfold\", point.size = 3, add.CI = TRUE) #> Warning: Removed 1 row containing missing values or values outside the scale range #> (`geom_errorbarh()`). # Plot of items with defined BMD point estimate and CI bounds subres <- bmdfilter(b$res, BMDtype = \"xfold\", BMDfilter = \"definedCI\") bmdplot(subres, BMDtype = \"xfold\", point.size = 3, add.CI = TRUE) # Plot of items with finite BMD point estimate and CI bounds subres <- bmdfilter(b$res, BMDtype = \"xfold\", BMDfilter = \"finiteCI\") bmdplot(subres, BMDtype = \"xfold\", point.size = 3, add.CI = TRUE) # \\donttest{ ### (1.b) Examples on BMD.zSD (with no undefined BMD.zSD values) # Plot of BMDs with no filtering subres <- bmdfilter(b$res, BMDfilter = \"none\") bmdplot(subres, BMDtype = \"zSD\", point.size = 3, add.CI = TRUE) #> Warning: Removed 2 rows containing missing values or values outside the scale range #> (`geom_errorbarh()`). # Plot items with defined BMD point estimate (the same on this ex.) subres <- bmdfilter(b$res, BMDtype = \"zSD\", BMDfilter = \"definedBMD\") bmdplot(subres, BMDtype = \"zSD\", point.size = 3, add.CI = TRUE) #> Warning: Removed 2 rows containing missing values or values outside the scale range #> (`geom_errorbarh()`). # Plot of items with defined BMD point estimate and CI bounds subres <- bmdfilter(b$res, BMDtype = \"zSD\", BMDfilter = \"definedCI\") bmdplot(subres, BMDtype = \"zSD\", point.size = 3, add.CI = TRUE) # Plot of items with finite BMD point estimate and CI bounds subres <- bmdfilter(b$res, BMDtype = \"zSD\", BMDfilter = \"finiteCI\") bmdplot(subres, BMDtype = \"zSD\", point.size = 3, add.CI = TRUE) # }"},{"path":"/reference/bmdplot.html","id":null,"dir":"Reference","previous_headings":"","what":"BMD plot optionally with confidence intervals on BMD — bmdplot","title":"BMD plot optionally with confidence intervals on BMD — bmdplot","text":"Provides ECDF plot BMD values optionally confidence intervals BMD value /labels items.","code":""},{"path":"/reference/bmdplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BMD plot optionally with confidence intervals on BMD — bmdplot","text":"","code":"bmdplot(extendedres, BMDtype = c(\"zSD\", \"xfold\"), add.CI = FALSE, facetby, facetby2, shapeby, colorby, point.size = 1.5, point.alpha = 0.8, line.size = 0.5, line.alpha = 0.8, ncol4faceting, add.label = FALSE, label.size = 2, BMD_log_transfo = TRUE)"},{"path":"/reference/bmdplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BMD plot optionally with confidence intervals on BMD — bmdplot","text":"extendedres dataframe results provided plot.bmdcalc plot.bmdboot (res) subset data frame (selected lines). dataframe can extended additional columns coming example functional annotation items, lines can replicated corresponding item one annotation. dataframe must least contain column giving BMD values (BMD.zSD BMD.xfold depending chosen BMDtype), identification curve (id), add.CI TRUE, columns BMD.zSD.lower, BMD.zSD.upper BMD.xfold.lower, BMD.xfold.upper depending argument BMDtype. BMDtype type BMD plot, \"zSD\" (default choice) \"xfold\". add.CI TRUE (default choice FALSE) item confidence interval added. facetby optional argument naming column extendedres chosen split plot facets using ggplot2::facet_wrap (split omitted). facetby2 optional argument naming column extendedres chosen additional argument split plot facets using ggplot2::facet_grid, columns defined facetby rows defined facetby2 (split omitted). shapeby optional argument naming column extendedres chosen shape BMD points (difference shapeby omitted). colorby optional argument naming column extendedres chosen color BMD points (difference colorby omitted). point.size Size BMD points. point.alpha Transparency points. line.size Width lines. line.alpha Transparency lines. ncol4faceting Number columns facetting (used facetby2 also provided. add.label Points replaced labels items TRUE. label.size Size labels add.label TRUE. BMD_log_transfo TRUE, default option, log transformation BMD used plot.","code":""},{"path":"/reference/bmdplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BMD plot optionally with confidence intervals on BMD — bmdplot","text":"BMD values plotted ECDF plot, plot.bmdcalc using \"ecdf\" plottype confidence intervals BMD value /labels items requested. optional use columns code shape /facets item particularly intended give view dose-response per group (e.g. metabolic pathways). groups must coded column extendedres. case one item allocated one group annotation process, line item must replicated extendedres many times number annotation groups allocated.","code":""},{"path":"/reference/bmdplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"BMD plot optionally with confidence intervals on BMD — bmdplot","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/bmdplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BMD plot optionally with confidence intervals on BMD — bmdplot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/bmdplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BMD plot optionally with confidence intervals on BMD — bmdplot","text":"","code":"# (1) # Plot of BMD values with color dose-response gradient # faceted by metabolic pathway (from annotation of the selected items) # and shaped by dose-response trend # An example from the paper published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # A example of plot obtained with this function is in Figure 5 in Larras et al. 2020 # the dataframe with metabolomic results (output $res of bmdcalc() or bmdboot() functions) resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # the dataframe with annotation of each item identified in the previous file # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe metabextendedres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(metabextendedres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport ### (1.a) BMDplot by pathway shaped by trend bmdplot(metabextendedres, BMDtype = \"zSD\", facetby = \"path_class\", shapeby = \"trend\") # \\donttest{ ### (1.b) BMDplot by pathway with items labels bmdplot(metabextendedres, BMDtype = \"zSD\", facetby = \"path_class\", add.label = TRUE, label.size = 2) ### (1.c) BMDplot by pathway with confidence intervals bmdplot(metabextendedres, BMDtype = \"zSD\", facetby = \"path_class\", add.CI = TRUE) ### (1.d) BMDplot by pathway with confidence intervals # in BMD raw scale (not default log scale) bmdplot(metabextendedres, BMDtype = \"zSD\", facetby = \"path_class\", add.CI = TRUE, BMD_log_transfo = FALSE) ### (1.e) BMDplot by pathway with confidence intervals # colored by trend and playing with graphical parameters bmdplot(metabextendedres, BMDtype = \"zSD\", facetby = \"path_class\", add.CI = TRUE, colorby = \"trend\", point.size = 2, point.alpha = 0.5, line.size = 0.8, line.alpha = 0.5) # (2) # An example with two molecular levels # # Import the dataframe with transcriptomic results contigresfilename <- system.file(\"extdata\", \"triclosanSVcontigres.txt\", package = \"DRomics\") contigres <- read.table(contigresfilename, header = TRUE, stringsAsFactors = TRUE) str(contigres) #> 'data.frame':\t447 obs. of 27 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... # Import the dataframe with functional annotation (or any other descriptor/category # you want to use, here KEGG pathway classes) contigannotfilename <- system.file(\"extdata\", \"triclosanSVcontigannot.txt\", package = \"DRomics\") contigannot <- read.table(contigannotfilename, header = TRUE, stringsAsFactors = TRUE) str(contigannot) #> 'data.frame':\t562 obs. of 2 variables: #> $ contig : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ path_class: Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... # Merging of both previous dataframes contigextendedres <- merge(x = contigres, y = contigannot, by.x = \"id\", by.y = \"contig\") # to see the structure of this dataframe str(contigextendedres) #> 'data.frame':\t562 obs. of 28 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... #> $ path_class : Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... ### Merge metabolomic and transcriptomic results extendedres <- rbind(metabextendedres, contigextendedres) extendedres$molecular.level <- factor(c(rep(\"metabolites\", nrow(metabextendedres)), rep(\"contigs\", nrow(contigextendedres)))) str(extendedres) #> 'data.frame':\t646 obs. of 29 variables: #> $ id : Factor w/ 478 levels \"NAP47_51\",\"NAP_2\",..: 1 2 3 4 4 4 4 5 6 7 ... #> $ irow : int 46 2 21 28 28 28 28 34 38 47 ... #> $ adjpvalue : num 7.16e-04 6.23e-05 1.11e-05 1.03e-05 1.03e-05 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 3 3 3 3 3 2 2 4 ... #> $ nbpar : int 2 3 2 2 2 2 2 3 3 5 ... #> $ b : num -0.056 0.4598 -0.0595 -0.0451 -0.0451 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 7.34 5.94 5.39 7.86 7.86 ... #> $ e : num NA -1.65 NA NA NA ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.1245 0.126 0.0793 0.052 0.052 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 7 7 7 7 7 2 2 9 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 3 3 3 3 1 ... #> $ y0 : num 7.34 5.94 5.39 7.86 7.86 ... #> $ yrange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ maxychange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 2.224 0.528 1.333 1.154 1.154 ... #> $ BMR.zSD : num 7.22 5.82 5.31 7.81 7.81 ... #> $ BMD.xfold : num NA NA NA NA NA ... #> $ BMR.xfold : num 6.61 5.35 4.85 7.07 7.07 ... #> $ BMD.zSD.lower : num 0.979 0.2 0.853 0.752 0.752 ... #> $ BMD.zSD.upper : num 4.07 1.11 1.75 1.46 1.46 ... #> $ BMD.xfold.lower : num Inf Inf 7.61 Inf Inf ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 1000 957 1000 1000 1000 1000 1000 648 620 872 ... #> $ path_class : Factor w/ 18 levels \"Amino acid metabolism\",..: 5 5 3 3 2 6 8 5 5 5 ... #> $ molecular.level : Factor w/ 2 levels \"contigs\",\"metabolites\": 2 2 2 2 2 2 2 2 2 2 ... ### BMD plot per pathway with molecular level coding for color bmdplot(extendedres, BMDtype = \"zSD\", facetby = \"path_class\", colorby = \"molecular.level\", point.alpha = 0.3) ### BMD plot per pathway and per molecular level # for a selection of pathways chosen_path_class <- c(\"Membrane transport\", \"Lipid metabolism\") ischosen <- is.element(extendedres$path_class, chosen_path_class) bmdplot(extendedres[ischosen, ], BMDtype = \"zSD\", facetby = \"path_class\", facetby2 = \"molecular.level\", colorby = \"trend\", point.size = 2, add.CI = TRUE) # }"},{"path":"/reference/bmdplotwithgradient.html","id":null,"dir":"Reference","previous_headings":"","what":"BMD plot with color gradient — bmdplotwithgradient","title":"BMD plot with color gradient — bmdplotwithgradient","text":"Provides ECDF plot BMD values horizontal color gradient coding, item, theoretical signal function dose (concentration). idea display amplitude intensity response item BMD ECDF plot, addition BMD ordered values. plot interest especially much items presented. maximize lisibility plot, one can manually pre-select items based criteria (e.g. functional group interest).","code":""},{"path":"/reference/bmdplotwithgradient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BMD plot with color gradient — bmdplotwithgradient","text":"","code":"bmdplotwithgradient(extendedres, BMDtype = c(\"zSD\", \"xfold\"), xmin, xmax, y0shift = TRUE, scaling = TRUE, facetby, facetby2, shapeby, npoints = 50, line.size, point.size = 1, ncol4faceting, limits4colgradient, lowercol = \"darkblue\", uppercol = \"darkred\", add.label, label.size = 2, BMD_log_transfo = TRUE)"},{"path":"/reference/bmdplotwithgradient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BMD plot with color gradient — bmdplotwithgradient","text":"extendedres dataframe results provided bmdcalc (res) subset data frame (selected lines). dataframe can extended additional columns coming example functional annotation items, lines can replicated corresponding item one annotation. extended dataframe must least contain column giving BMD values (BMD.zSD BMD.xfold depending chosen BMDtype), identification curve (id), column model naming fitted model values parameters (columns b, c, d, e, f). BMDtype type BMD plot, \"zSD\" (default choice) \"xfold\". xmin Optional minimal dose/concentration definition x range. xmax Optional maximal dose/concentration definition x range (can defined max(f$omicdata$dose) f output drcfit() example). y0shift TRUE (default choice) item signal shifted theoretical signal control 0. scaling TRUE, default choice, item signal shifted theoretical signal control 0 scaled dividing maximal absolute signal change () signal control maxychange. facetby optional argument naming column extendedres chosen split plot facets using ggplot2::facet_wrap (split omitted). facetby2 optional argument naming column extendedres chosen additional argument split plot facets using ggplot2::facet_grid, columns defined facetby rows defined facetby2 (split omitted). shapeby optional argument naming column extendedres chosen shape BMD points (difference shapeby omitted). npoints Number points computed curve order define signal color gradient (= number doses concentrations theoretical signal computed fitted model item). line.size Size horizontal lines plotting signal color gradient. point.size Size BMD points. ncol4faceting Number columns facetting (used facetby2 also provided. limits4colgradient Optional vector giving minimal maximal value signal color gradient. lowercol Chosen color lower values signal. uppercol Chosen color upper values signal. add.label Points replaced labels items TRUE. label.size Size labels add.label TRUE. BMD_log_transfo TRUE, default option, log transformation BMD used plot. option used null value xmin input.","code":""},{"path":"/reference/bmdplotwithgradient.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BMD plot with color gradient — bmdplotwithgradient","text":"BMD values plotted ECDF plot, plot.bmdcalc using \"ecdf\" plottype. addition plotted horizontal color gradient item coding signal level dose (concentration). optional use columns code shape /facets item particularly intended give view dose-response per group (e.g. metabolic pathways). groups must coded column extendedres. case one item allocated one group annotation process, line item must replicated extendedres many times number annotation groups allocated. item extended dataframe, name model (column model) values parameters (columns b, c, d, e, f) used compute theoretical dose-response curves, corresponding signal color gradient, range [xmin ; xmax].","code":""},{"path":"/reference/bmdplotwithgradient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"BMD plot with color gradient — bmdplotwithgradient","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/bmdplotwithgradient.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BMD plot with color gradient — bmdplotwithgradient","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/bmdplotwithgradient.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BMD plot with color gradient — bmdplotwithgradient","text":"","code":"# (1) # A toy example on a very small subsample of a microarray data set datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package=\"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.01) #> Removing intercept from test coefficients f <- drcfit(s_quad, progressbar = TRUE) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |==== | 6% | |======== | 12% | |============ | 18% | |================ | 24% | |===================== | 29% | |========================= | 35% | |============================= | 41% | |================================= | 47% | |===================================== | 53% | |========================================= | 59% | |============================================= | 65% | |================================================= | 71% | |====================================================== | 76% | |========================================================== | 82% | |============================================================== | 88% | |================================================================== | 94% | |======================================================================| 100% r <- bmdcalc(f) # Plot of all the BMD values with color dose-response gradient # bmdplotwithgradient(r$res, BMDtype = \"zSD\") #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # \\donttest{ # Same plot without signal scaling # bmdplotwithgradient(r$res, BMDtype = \"zSD\", scaling = FALSE) #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # Plot of all the BMD values with color dose-response gradient # with definition of xmax from the maximal tested dose # bmdplotwithgradient(r$res, BMDtype = \"zSD\", xmax = max(f$omicdata$dose)) # Add of item labels bmdplotwithgradient(r$res, BMDtype = \"zSD\", xmax = max(f$omicdata$dose), add.label = TRUE) # The same plot in raw scale (we can fix xmin at 0 in this case) # bmdplotwithgradient(r$res, BMDtype = \"zSD\", xmin = 0, BMD_log_transfo = FALSE) #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # The same plot in log scale with defining xmin and xmax at a chosen values # bmdplotwithgradient(r$res, BMDtype = \"zSD\", xmin = min(f$omicdata$dose[f$omicdata$dose != 0] / 2), xmax = max(f$omicdata$dose), BMD_log_transfo = TRUE) # Plot of all the BMD values with color dose-response gradient # faceted by response trend and shaped by model # bmdplotwithgradient(r$res, BMDtype = \"zSD\", facetby = \"trend\", shapeby = \"model\") #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # same plot changing the names of the facets levels(r$res$trend) #> [1] \"U\" \"bell\" \"dec\" \"inc\" levels(r$res$trend) <- c(\"bell shape\", \"decreasing\", \"increasing\", \"U shape\") bmdplotwithgradient(r$res, BMDtype = \"zSD\", facetby = \"trend\", shapeby = \"model\") #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # same plot changing the labels of the legends # and inversing the two guides if (require(ggplot2)) bmdplotwithgradient(r$res, BMDtype = \"zSD\", facetby = \"trend\", shapeby = \"model\") + labs(col = \"signal value\", shape = \"model\") + guides(colour = guide_colourbar(order = 1), shape = guide_legend(order = 2)) #> Loading required package: ggplot2 #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # (2) # Plot of BMD values with color dose-response gradient # faceted by metabolic pathway (from annotation of the selected items) # and shaped by dose-response trend # An example from the paper published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # A example of plot obtained with this function is in Figure 5 in Larras et al. 2020 # the dataframe with metabolomic results (output $res of bmdcalc() or bmdboot() functions) resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # the dataframe with annotation of each item identified in the previous file # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe extendedres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(extendedres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport ### (2.a) BMDplot with gradient by pathway bmdplotwithgradient(extendedres, BMDtype = \"zSD\", facetby = \"path_class\", xmax = 7.76, # maximal tested dose in those data shapeby = \"trend\") ### (2.a) BMDplot with gradient by pathway without scaling bmdplotwithgradient(extendedres, BMDtype = \"zSD\", facetby = \"path_class\", xmax = 7.76, shapeby = \"trend\", scaling = FALSE) # (2.b) BMDplot with gradient by pathway # forcing the limits of the colour gradient at other # values than observed minimal and maximal values of the signal bmdplotwithgradient(extendedres, BMDtype = \"zSD\", facetby = \"path_class\", shapeby = \"trend\", limits4colgradient = c(-1, 1)) #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # (2.c) The same example changing the gradient colors and the line size bmdplotwithgradient(extendedres, BMDtype = \"zSD\", facetby = \"path_class\", shapeby = \"trend\", line.size = 3, lowercol = \"darkgreen\", uppercol = \"orange\") #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # (2.d) The same example with only lipid metabolism pathclass # and identification of the metabolites LMres <- extendedres[extendedres$path_class == \"Lipid metabolism\", ] bmdplotwithgradient(LMres, BMDtype = \"zSD\", line.size = 3, add.label = TRUE, label.size = 3) #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # (3) # An example on a microarray data set (a subsample of a greater data set) # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 1000 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" #> [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" #> Data were normalized between arrays using the following method: cyclicloess (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.001)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.001: 78 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |= | 1% | |== | 3% | |=== | 4% | |==== | 5% | |==== | 6% | |===== | 8% | |====== | 9% | |======= | 10% | |======== | 12% | |========= | 13% | |========== | 14% | |=========== | 15% | |============ | 17% | |============= | 18% | |============= | 19% | |============== | 21% | |=============== | 22% | |================ | 23% | |================= | 24% | |================== | 26% | |=================== | 27% | |==================== | 28% | |===================== | 29% | |====================== | 31% | |====================== | 32% | |======================= | 33% | |======================== | 35% | |========================= | 36% | |========================== | 37% | |=========================== | 38% | |============================ | 40% | |============================= | 41% | |============================== | 42% | |=============================== | 44% | 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|=============================================================== | 90% | |================================================================ | 91% | |================================================================= | 92% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |===================================================================== | 99% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 11 dose-response curves out of 78 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 67 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 0 11 30 23 #> log-Gauss-probit #> 3 #> Distribution of the trends (curve shapes) among the 67 fitted dose-response curves: #> #> U bell dec inc #> 6 20 22 19 (r <- bmdcalc(f)) #> 1 BMD-xfold values and 0 BMD-zSD values are not defined (coded NaN as #> the BMR stands outside the range of response values defined by the model). #> 28 BMD-xfold values and 0 BMD-zSD values could not be calculated (coded #> NA as the BMR stands within the range of response values defined by the #> model but outside the range of tested doses). bmdplotwithgradient(r$res, BMDtype = \"zSD\", facetby = \"trend\", shapeby = \"model\") #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # without scaling bmdplotwithgradient(r$res, BMDtype = \"zSD\", scaling = FALSE, facetby = \"trend\", shapeby = \"model\") #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # }"},{"path":"/reference/continuousanchoringdata.html","id":null,"dir":"Reference","previous_headings":"","what":"Import and check of continuous anchoring apical data — continuousanchoringdata","title":"Import and check of continuous anchoring apical data — continuousanchoringdata","text":"Continuous anchoring apical data imported .txt file (internally imported using function read.table) checked R object class data.frame (see description argument file required format data). transformation provided function. needed pretreatment data must done importation data, can directly modelled using normal error model. strong hypothesis required selection responsive endpoints dose-reponse modelling.","code":""},{"path":"/reference/continuousanchoringdata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import and check of continuous anchoring apical data — continuousanchoringdata","text":"","code":"continuousanchoringdata(file, backgrounddose, check = TRUE) # S3 method for class 'continuousanchoringdata' print(x, ...) # S3 method for class 'continuousanchoringdata' plot(x, dose_log_transfo = TRUE, ...)"},{"path":"/reference/continuousanchoringdata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import and check of continuous anchoring apical data — continuousanchoringdata","text":"file name .txt file (e.g. \"mydata.txt\") containing one row per endpoint, first column corresponding identifier endpoint, columns giving measured values endpoint replicate dose concentration. first line, name endpoint column, must tested doses concentrations numeric format corresponding replicate (example, triplicates treatment, first line \"endpoint\", 0, 0, 0, 0.1, 0.1, 0.1, etc.). file imported within function using function read.table default field separator (sep argument) default decimal separator (dec argument \".\"). Alternatively R object class data.frame can directly given input, corresponding output read.table(file, header = FALSE) file described . two alternatives illustrated examples. backgrounddose argument must used dose zero data, prevent calculation BMD extrapolation. doses equal value given backgrounddose fixed 0, considered background level exposition. check TRUE format input file checked. x object class \"continuousanchoringdata\". dose_log_transfo TRUE log transformation dose used plot. ... arguments passed print plot functions.","code":""},{"path":"/reference/continuousanchoringdata.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Import and check of continuous anchoring apical data — continuousanchoringdata","text":"function imports data, checks format (see description argument file required format data) gives print information help user check coding data correct : tested doses (concentrations) number replicates dose, number endpoints.","code":""},{"path":"/reference/continuousanchoringdata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import and check of continuous anchoring apical data — continuousanchoringdata","text":"continuousanchoringdata returns object class \"continuousanchoringdata\", list 7 components: data numeric matrix responses item replicate (one line per item, one column per replicate) dose numeric vector tested doses concentrations corresponding column data item character vector identifiers endpoints, corresponding line data design table experimental design (tested doses number replicates dose) control user data.mean numeric matrix mean responses item per dose (mean corresponding replicates) (one line per item, one column per unique value dose data.sd numeric matrix standard deviations response item per dose (sd corresponding replicates, NA replicate) (one line per item, one column per unique value dose) containsNA TRUE data set contains NA values print continuousanchoringdata object gives tested doses (concentrations) number replicates dose, number items, identifiers first 20 items (check good coding data) normalization method. plot continuousanchoringdata object shows data distribution dose concentration replicate.","code":""},{"path":[]},{"path":"/reference/continuousanchoringdata.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Import and check of continuous anchoring apical data — continuousanchoringdata","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/continuousanchoringdata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import and check of continuous anchoring apical data — continuousanchoringdata","text":"","code":"# (1) import and check of continuous anchoring data # (an example with two apical endpoints of an example given in the package (see ?Scenedesmus)) # datafilename <- system.file(\"extdata\", \"apical_anchoring.txt\", package = \"DRomics\") o <- continuousanchoringdata(datafilename, backgrounddose = 0.1, check = TRUE) #> Warning: #> We recommend you to check that your anchoring data are continuous and #> defined in a scale that enable the use of a normal error model (needed #> at each step of the workflow including the selection step). # It is here necessary to define the background dose as there is no dose at 0 in the data # The BMD cannot be computed without defining the background level print(o) #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 2.4 3.8 6.2 10.1 16.5 26.8 43.5 70.7 #> 12 6 2 2 2 6 2 2 2 #> Number of endpoints: 2 #> Names of the endpoints: #> [1] \"growth\" \"photosynthesis\" # \\donttest{ plot(o) #> Warning: log-10 transformation introduced infinite values. # } # If you want to use your own data set just replace datafilename, # the first argument of continuousanchoringdata(), # by the name of your data file (e.g. \"mydata.txt\") # # You should take care that the field separator of this data file is one # of the default field separators recognised by the read.table() function # when it is used with its default field separator (sep argument) # Tabs are recommended. # Use of an R object of class data.frame # on the same example (see ?Scenedesmus for details) data(Scenedesmus_apical) o <- continuousanchoringdata(Scenedesmus_apical, backgrounddose = 0.1) #> Warning: #> We recommend you to check that your anchoring data are continuous and #> defined in a scale that enable the use of a normal error model (needed #> at each step of the workflow including the selection step). print(o) #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 2.4 3.8 6.2 10.1 16.5 26.8 43.5 70.7 #> 12 6 2 2 2 6 2 2 2 #> Number of endpoints: 2 #> Names of the endpoints: #> [1] \"growth\" \"photosynthesis\" # \\donttest{ plot(o) #> Warning: log-10 transformation introduced infinite values. # }"},{"path":"/reference/curvesplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of fitted curves — curvesplot","title":"Plot of fitted curves — curvesplot","text":"Provides plot fitted curves dataframe main workflow results, possibly extended additional information (e.g. groups functional annotation) used color /split curves.","code":""},{"path":"/reference/curvesplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of fitted curves — curvesplot","text":"","code":"curvesplot(extendedres, xmin, xmax, y0shift = TRUE, scaling = TRUE, facetby, facetby2, free.y.scales = FALSE, ncol4faceting, colorby, removelegend = FALSE, npoints = 500, line.size = 0.5, line.alpha = 0.8, dose_log_transfo = TRUE, addBMD = TRUE, BMDtype = c(\"zSD\", \"xfold\"), point.size = 1, point.alpha = 0.8)"},{"path":"/reference/curvesplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of fitted curves — curvesplot","text":"extendedres dataframe results provided bmdcalc (res) subset data frame (selected lines). dataframe can extended additional columns coming example annotation items, lines can replicated corresponding item one annotation. extended dataframe must least contain column giving identification curve (id), column model naming fitted model values parameters (columns b, c, d, e, f) column coding chosen BMD, default BMD.zSD BMD.xfold BMDtype \"xfold\". xmin defined, value just maximum BMD values fixed x dose scale log, 0 otherwise. xmax Maximal dose/concentration definition x range (can defined max(f$omicdata$dose) f output drcfit()). defined, value just maximum BMD values taken. y0shift TRUE (default choice) curves shifted theoretical signal control 0. scaling TRUE, default choice, curves shifted theoretical signal control 0 y0 scaled dividing maximal absolute signal change () signal control maxychange. facetby optional argument naming column extendedres chosen split plot facets (split omitted). facetby2 optional argument naming column extendedres chosen additional argument split plot facets using ggplot2::facet_grid, columns defined facetby rows defined facetby2 (split omitted). free.y.scales TRUE y scales free different facets. ncol4faceting Number columns facetting (used facetby2 also provided. colorby optional argument naming column extendedres chosen color curves (color omitted). removelegend TRUE color legend removed (useful number colors great). npoints Number points computed curve plot . line.size Width lines plotting curves. line.alpha Transparency lines plotting curves. dose_log_transfo TRUE log transformation dose used plot. option needs definition strictly positive value xmin input. addBMD TRUE points added curve BMD-BMR values (requires BMD BMD values first argument extendedres). BMDtype type BMD add, \"zSD\" (default choice) \"xfold\". point.size Size BMD-BMR points added curves. point.alpha Transparency BMD-BMR points added curves.","code":""},{"path":"/reference/curvesplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot of fitted curves — curvesplot","text":"item extended dataframe, name model (column model) values parameters (columns b, c, d, e, f) used compute theoretical dose-response curves range [xmin ; xmax].","code":""},{"path":"/reference/curvesplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot of fitted curves — curvesplot","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/curvesplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot of fitted curves — curvesplot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/curvesplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot of fitted curves — curvesplot","text":"","code":"# (1) A toy example on a very small subsample of a microarray data set) # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package = \"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.01) #> Removing intercept from test coefficients f <- drcfit(s_quad, progressbar = TRUE) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |==== | 6% | |======== | 12% | |============ | 18% | |================ | 24% | |===================== | 29% | |========================= | 35% | |============================= | 41% | |================================= | 47% | |===================================== | 53% | |========================================= | 59% | |============================================= | 65% | |================================================= | 71% | |====================================================== | 76% | |========================================================== | 82% | |============================================================== | 88% | |================================================================== | 94% | |======================================================================| 100% r <- bmdcalc(f) # (1.a) # Default plot of all the curves with BMD values added as points on the curve # curvesplot(r$res, xmax = max(f$omicdata$dose)) # \\donttest{ # use of line size, point size, transparency curvesplot(r$res, xmax = max(f$omicdata$dose), line.alpha = 0.2, line.size = 1, point.alpha = 0.3, point.size = 1.8) # the same plot with dose not in log scale # fixing xmin and xmax curvesplot(r$res, xmin = 0.1, xmax = max(f$omicdata$dose), dose_log_transfo = FALSE, addBMD = TRUE) # or not curvesplot(r$res, dose_log_transfo = FALSE, addBMD = TRUE) # plot of curves colored by models curvesplot(r$res, xmax = max(f$omicdata$dose), colorby = \"model\") # plot of curves facetted by item curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"id\") # plot of curves facetted by trends curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"trend\") # the same plot with free y scales curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"trend\", free.y.scales = TRUE) # (1.b) # Plot of all the curves without shifting y0 values to 0 # and without scaling curvesplot(r$res, xmax = max(f$omicdata$dose), scaling = FALSE, y0shift = FALSE) # (1.c) # Plot of all the curves colored by model, with one facet per trend # curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"trend\", colorby = \"model\") # changing the number of columns curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"trend\", colorby = \"model\", ncol4faceting = 4) # playing with size and transparency of lines curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"trend\", colorby = \"model\", line.size = 0.5, line.alpha = 0.8) curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"trend\", colorby = \"model\", line.size = 0.8, line.alpha = 0.2) curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"trend\", line.size = 1, line.alpha = 0.2) # (2) an example on a microarray data set (a subsample of a greater data set) # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 1000 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" #> [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" #> Data were normalized between arrays using the following method: cyclicloess (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.001)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.001: 78 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |= | 1% | |== | 3% | |=== | 4% | |==== | 5% | |==== | 6% | |===== | 8% | |====== | 9% | |======= | 10% | |======== | 12% | |========= | 13% 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|================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |===================================================================== | 99% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 11 dose-response curves out of 78 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 67 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 0 11 30 23 #> log-Gauss-probit #> 3 #> Distribution of the trends (curve shapes) among the 67 fitted dose-response curves: #> #> U bell dec inc #> 6 20 22 19 (r <- bmdcalc(f)) #> 1 BMD-xfold values and 0 BMD-zSD values are not defined (coded NaN as #> the BMR stands outside the range of response values defined by the model). #> 28 BMD-xfold values and 0 BMD-zSD values could not be calculated (coded #> NA as the BMR stands within the range of response values defined by the #> model but outside the range of tested doses). # plot split by trend and model with BMR-BMD points added on curves # adding transparency curvesplot(r$res, xmax = max(f$omicdata$dose), line.alpha = 0.2, line.size = 0.8, addBMD = TRUE, point.alpha = 0.2, point.size = 1.5, facetby = \"trend\", facetby2 = \"model\") # same plot without scaling and not in log dose scale curvesplot(r$res, xmax = max(f$omicdata$dose), line.alpha = 0.2, line.size = 0.8, dose_log_transfo = FALSE, addBMD = TRUE, point.alpha = 0.2, point.size = 1.5, scaling = FALSE, facetby = \"trend\", facetby2 = \"model\") # (3) An example from data published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # a dataframe with metabolomic results (output $res of bmdcalc() or bmdboot() functions) resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # a dataframe with annotation of each item identified in the previous file # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe # bootstrap results and annotation extendedres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(extendedres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport # Plot of the dose-response curves by pathway colored by trend # with BMR-BMD points added on curves curvesplot(extendedres, facetby = \"path_class\", npoints = 100, line.size = 0.5, colorby = \"trend\", xmax = 7, addBMD = TRUE) # The same plot not in log scale curvesplot(extendedres, facetby = \"path_class\", npoints = 100, line.size = 0.5, dose_log_transfo = FALSE, colorby = \"trend\", xmin = 0, xmax = 7) # The same plot in log scale without scaling curvesplot(extendedres, facetby = \"path_class\", npoints = 100, line.size = 0.5, colorby = \"trend\", scaling = FALSE, xmax = 7) # Plot of the dose-response curves split by pathway and by trend # for a selection pathway chosen_path_class <- c(\"Membrane transport\", \"Lipid metabolism\") ischosen <- is.element(extendedres$path_class, chosen_path_class) curvesplot(extendedres[ischosen, ], facetby = \"trend\", facetby2 = \"path_class\", npoints = 100, line.size = 0.5, xmax = 7) # Plot of the dose-response curves for a specific pathway # in this example the \"lipid metabolism\" pathclass LMres <- extendedres[extendedres$path_class == \"Lipid metabolism\", ] curvesplot(LMres, facetby = \"id\", npoints = 100, line.size = 0.8, point.size = 2, colorby = \"trend\", xmax = 7) # }"},{"path":"/reference/drcfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Dose response modelling for responsive items — drcfit","title":"Dose response modelling for responsive items — drcfit","text":"Fits dose reponse models responsive items.","code":""},{"path":"/reference/drcfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dose response modelling for responsive items — drcfit","text":"","code":"drcfit(itemselect, information.criterion = c(\"AICc\", \"BIC\", \"AIC\"), deltaAICminfromnullmodel = 2, postfitfilter = TRUE, preventsfitsoutofrange = TRUE, enablesfequal0inGP = TRUE, enablesfequal0inLGP = TRUE, progressbar = TRUE, parallel = c(\"no\", \"snow\", \"multicore\"), ncpus) # S3 method for class 'drcfit' print(x, ...) # S3 method for class 'drcfit' plot(x, items, plot.type = c(\"dose_fitted\", \"dose_residuals\",\"fitted_residuals\"), dose_log_transfo = TRUE, BMDoutput, BMDtype = c(\"zSD\", \"xfold\"), ...) plotfit2pdf(x, items, plot.type = c(\"dose_fitted\", \"dose_residuals\", \"fitted_residuals\"), dose_log_transfo = TRUE, BMDoutput, BMDtype = c(\"zSD\", \"xfold\"), nrowperpage = 6, ncolperpage = 4, path2figs = getwd())"},{"path":"/reference/drcfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dose response modelling for responsive items — drcfit","text":"itemselect object class \"itemselect\" returned function itemselect. information.criterion information criterion used select best fit model, \"AICc\" recommended default choice (corrected version AIC recommended small samples (see Burnham Anderson 2004), \"BIC\" \"AIC\". deltaAICminfromnullmodel minimal difference chosen information criterion (AICc, AIC BIC) null model best fit model, requested accept fit bestfit model. default fixed 2 keep models fit data clearly better null model, can fixed 0 less stringent. postfitfilter TRUE fits significant trends residuals (showing global significant quadratic trend residuals function dose (rank-scale)) considered failures eliminated. strongly recommended let TRUE, default value. preventsfitsoutofrange TRUE fits Gaussian log-Gaussian models give extremum value outside range observed signal item eliminated candidate models item, choice best. strongly recommended let TRUE, default value. enablesfequal0inGP TRUE fit Gauss-probit model 5 parameters successful, simplified version f = 0 also fitted included candidate models. submodel log-Gauss-probit model corresponds probit model. recommend let argument TRUE, default value, order prevent overfitting, prefer description monotonic curve parameter f necessary model data according information criterion. enablesfequal0inLGP TRUE fit log-Gauss-probit model 5 parameters successful, simplified version f = 0 also fitted included candidate models. submodel log-Gauss-probit model corresponds log-probit model. recommend let argument TRUE, default value, order prevent overfitting prefer description monotonic curve parameter f necessary model data according information criterion. progressbar TRUE progress bar used follow fitting process. parallel type parallel operation used, \"snow\" \"multicore\" (second one available Windows), \"\" parallel operation. ncpus Number processes used parallel operation : typically one fix number available CPUs. x object class \"drcfit\". items Argument plot.drcfit function : number first fits plot (20 items max) character vector specifying identifiers items plot (20 items max). plot.type type plot, default \"dose_fitted\" plot fitted curves observed points added plot observed means dose added black plain circles, \"dose_residuals\" plot residuals function dose, \"fitted_residuals\" plot residuals function fitted value. dose_log_transfo default TRUE use log transformation dose axis (used dose x-axis, plot.type \"fitted_residuals\"). BMDoutput Argument can used add BMD values optionally confidence intervals plot type \"dose_fitted\". must previously apply bmdcalc optionally bmdboot x class drcfit give argument output bmdcalc bmdboot. BMDtype type BMD add plot, \"zSD\" (default choice) \"xfold\" (used BMDoutput missing). nrowperpage Number rows plots plots saved pdf file using plotfit2pdf() (passed facet_wrap()). ncolperpage Number columns plots plots saved pdf file using plotfit2pdf() (passed facet_wrap()). path2figs File path plots saved pdf file using plotfit2pdf() ... arguments passed graphical print functions.","code":""},{"path":"/reference/drcfit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dose response modelling for responsive items — drcfit","text":"selected item, five dose-response models (linear, Hill, exponential, Gauss-probit log-Gauss-probit, see Larras et al. 2018 definition) fitted non linear regression, using nls function. fit biphasic model gives extremum value range observed signal, eliminated (may happen rare cases, especially observational data number samples high dose uncontrolled, doses distributed along dose range). best fit chosen one giving lowest AICc (BIC AIC) value. use AICc (second-order Akaike criterion) instead AIC strongly recommended prevent overfitting may occur dose-response designs small number data points (Hurvich Tsai, 1989; Burnham Anderson DR, 2004). Note extremely rare cases number data points great, AIC converge AICc procedures equivalent. Items best AICc value lower AICc value null model (constant model) minus 2 eliminated. Items best fit showing global significant quadratic trend residuals function dose (rank-scale) also eliminated (best fit considered reliable cases). retained item classified four classes global trend, can used roughly describe shape dose-response curve: inc increasing curves, dec decreasing curves , U U-shape curves, bell bell-shape curves. curves fitted Gauss-probit model can classified increasing decreasing dose value extremum reached zero simplified version f = 0 retained (corresponding probit model). curves fitted log-Gauss-probit model can classified increasing decreasing simplified version f = 0 retained (corresponding log-probit model). retained item thus classified 16 class typology depending chosen model parameter values : H.inc increasing Hill curves, H.dec decreasing Hill curves, L.inc increasing linear curves, L.dec decreasing linear curves, E.inc.convex increasing convex exponential curves, E.dec.concave decreasing concave exponential curves, E.inc.concave increasing concave exponential curves, E.dec.convex decreasing convex exponential curves, GP.U U-shape Gauss-probit curves, GP.bell bell-shape Gauss-probit curves, GP.inc increasing Gauss-probit curves, GP.dec decreasing Gauss-probit curves, lGP.U U-shape log-Gauss-probit curves, lGP.bell bell-shape log-Gauss-probit curves. lGP.inc increasing log-Gauss-probit curves, lGP.dec decreasing log-Gauss-probit curves,","code":""},{"path":"/reference/drcfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dose response modelling for responsive items — drcfit","text":"drcfit returns object class \"drcfit\", list 4 components: fitres data frame reporting results fit selected item successful fit reached (one line per item) sorted ascending order adjusted p-values returned function itemselect. different columns correspond identifier item (id), row number item initial data set (irow), adjusted p-value selection step (adjpvalue), name best fit model (model), number fitted parameters (nbpar), values parameters b, c, d, e f, (NA non used parameters), residual standard deviation (SDres), typology curve (typology), rough trend curve (trend) defined four classes (U, bell, increasing decreasing shape), theoretical y value control y0), theoretical y value maximal dose yatdosemax), theoretical y range x within range tested doses (yrange), maximal absolute y change () control(maxychange) biphasic curves x value extremum reached (xextrem) corresponding y value (yextrem). omicdata object containing data, given input itemselect() also component output itemselect(). information.criterion information criterion used select best fit model given input. information.criterion.val data frame reporting IC values (AICc, BIC AIC) values selected item (one line per item) fitted model (one colum per model IC value fixed Inf fit failed). n.failure number previously selected items workflow failed fit acceptable model. unfitres data frame reporting results selected item successful fit reached (one line per item) sorted ascending order adjusted p-values returned function itemselect. different columns correspond identifier item (id), row number item initial data set (irow), adjusted p-value selection step (adjpvalue), code reason fitting failure (cause, equal \"constant.model\" best fit model constant model \"trend..residuals\" best fit model rejected due quadratic trend residuals.) residualtests data frame P-values tests performed residuals, mean trend (resimeantrendP ) variance trend (resivartrendP). first one tests global significant quadratic trend residuals function dose rank-scale (used eliminate unreliable fits) second one global significant quadratic trend residuals absolute value function dose rank-scale (used alert case heteroscedasticity).","code":""},{"path":[]},{"path":"/reference/drcfit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Dose response modelling for responsive items — drcfit","text":"Burnham, KP, Anderson DR (2004). Multimodel inference: understanding AIC BIC model selection. Sociological methods & research, 33(2), 261-304. Hurvich, CM, Tsai, CL (1989). Regression time series model selection small samples. Biometrika, 76(2), 297-307. Larras F, Billoir E, Baillard V, Siberchicot , Scholz S, Wubet T, Tarkka M, Schmitt-Jansen M Delignette-Muller ML (2018). DRomics: turnkey tool support use dose-response framework omics data ecological risk assessment. Environmental science & technology.doi:10.1021/acs.est.8b04752","code":""},{"path":"/reference/drcfit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dose response modelling for responsive items — drcfit","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/drcfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dose response modelling for responsive items — drcfit","text":"","code":"# (1) a toy example (a very small subsample of a microarray data set) # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package = \"DRomics\") # to test the package on a small (for a quick calculation) but not very small data set # use the following commented line # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package = \"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.05) #> Removing intercept from test coefficients (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 1 dose-response curves out of 21 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 20 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 0 6 6 7 #> log-Gauss-probit #> 1 #> Distribution of the trends (curve shapes) among the 20 fitted dose-response curves: #> #> U bell dec inc #> 4 4 6 6 # Default plot plot(f) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # \\donttest{ # The same plot without log transformation of the doses # (in raw scale of doses) plot(f, dose_log_transfo = FALSE) # The same plot in x log scale choosing x limits for plot if (require(ggplot2)) plot(f, dose_log_transfo = TRUE) + scale_x_log10(limits = c(0.1, 10)) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Scale for x is already present. #> Adding another scale for x, which will replace the existing scale. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # Plot of residuals as function of the dose plot(f, plot.type = \"dose_residuals\") #> Warning: log-10 transformation introduced infinite values. # Same plot of residuals without log transformation of the doses plot(f, plot.type = \"dose_residuals\", dose_log_transfo = FALSE) # plot of residuals as function of the fitted value plot(f, plot.type = \"fitted_residuals\") # (2) an example on a microarray data set (a subsample of a greater data set) # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package = \"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 1000 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" #> [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" #> Data were normalized between arrays using the following method: cyclicloess (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.05)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.05: 318 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | 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|==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 60 dose-response curves out of 318 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 258 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 2 85 64 89 #> log-Gauss-probit #> 18 #> Distribution of the trends (curve shapes) among the 258 fitted dose-response curves: #> #> U bell dec inc #> 55 52 58 93 # Default plot plot(f) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # save all plots to pdf using plotfit2pdf() plotfit2pdf(f, path2figs = tempdir()) #> #> Figures are stored in /tmp/RtmpLuxTDf. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> pdf #> 2 plotfit2pdf(f, plot.type = \"fitted_residuals\", nrowperpage = 9, ncolperpage = 6, path2figs = tempdir()) #> #> Figures are stored in /tmp/RtmpLuxTDf. #> pdf #> 2 # Plot of the fit of the first 12 most responsive items plot(f, items = 12) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # Plot of the chosen items in the chosen order plot(f, items = c(\"301.2\", \"363.1\", \"383.1\")) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # Look at the table of results for successful fits head(f$fitres) #> id irow adjpvalue model nbpar b c d #> 1 384.2 727 2.519524e-07 Gauss-probit 4 8.39021007 6.160174 6.160174 #> 2 383.1 724 6.558388e-07 Gauss-probit 4 3.81611448 10.480252 10.480252 #> 3 383.2 725 8.234946e-07 Gauss-probit 4 6.27817663 8.505693 8.505693 #> 4 384.1 726 2.804671e-06 Gauss-probit 4 8.59581518 5.684089 5.684089 #> 5 301.1 569 6.932747e-06 exponential 3 2.02444957 NA 12.846110 #> 6 363.1 686 7.084800e-06 exponential 3 -0.06029708 NA 9.026630 #> e f SDres typology trend y0 yatdosemax #> 1 1.538640 6.077561 0.1126233 GP.bell bell 12.13639 11.215361 #> 2 1.833579 1.861385 0.1411563 GP.bell bell 12.13871 11.324854 #> 3 1.751051 3.683404 0.1335847 GP.bell bell 12.04858 11.228734 #> 4 1.874867 6.568752 0.1379656 GP.bell bell 12.09844 11.320509 #> 5 -1.404111 NA 0.4905313 E.dec.convex dec 12.84611 10.839662 #> 6 2.064800 NA 0.2526946 E.dec.concave dec 9.02663 7.590655 #> yrange maxychange xextrem yextrem #> 1 1.0223735 0.9210335 1.538640 12.23774 #> 2 1.0167830 0.8138568 1.833579 12.34164 #> 3 0.9603629 0.8198451 1.751051 12.18910 #> 4 0.9323327 0.7779265 1.874867 12.25284 #> 5 2.0064474 2.0064474 NA NA #> 6 1.4359752 1.4359752 NA NA # Look at the table of results for unsuccessful fits head(f$unfitres) #> id irow adjpvalue cause #> 25 368.1 696 5.865601e-05 trend.in.residuals #> 38 367.1 694 2.748644e-04 trend.in.residuals #> 51 360.2 681 3.964071e-04 trend.in.residuals #> 57 162.2 305 4.818750e-04 trend.in.residuals #> 59 161.1 302 5.387711e-04 trend.in.residuals #> 60 275.1 519 5.387711e-04 trend.in.residuals # count the number of unsuccessful fits for each cause table(f$unfitres$cause) #> #> constant.model trend.in.residuals #> 30 30 # (3) Comparison of parallel and non paralell implementations on a larger selection of items # if(!requireNamespace(\"parallel\", quietly = TRUE)) { if(parallel::detectCores() > 1) { s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.05) system.time(f1 <- drcfit(s_quad, progressbar = TRUE)) system.time(f2 <- drcfit(s_quad, progressbar = FALSE, parallel = \"snow\", ncpus = 2)) }} # }"},{"path":"/reference/ecdfplotwithCI.html","id":null,"dir":"Reference","previous_headings":"","what":"ECDF plot of a variable with given confidence intervals on this variable — ecdfplotwithCI","title":"ECDF plot of a variable with given confidence intervals on this variable — ecdfplotwithCI","text":"Provides ECDF plot variable, x-error bars given confidence intervals variable, possibly partitioned groups. context package function intended used BMD variable groups defined user functional annotation.","code":""},{"path":"/reference/ecdfplotwithCI.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ECDF plot of a variable with given confidence intervals on this variable — ecdfplotwithCI","text":"","code":"ecdfplotwithCI(variable, CI.lower, CI.upper, by, CI.col = \"blue\", CI.alpha = 1, add.point = TRUE, point.size = 1, point.type = 16)"},{"path":"/reference/ecdfplotwithCI.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ECDF plot of a variable with given confidence intervals on this variable — ecdfplotwithCI","text":"variable numeric vector variable plot. context package variable may BMD. CI.lower corresponding numeric vector (length) lower bounds confidence intervals. CI.upper corresponding numeric vector (length) upper bounds confidence intervals. factor length split plot factor (split omitted). context package factor may code groups defined user functional annotation. CI.col color draw confidence intervals (unique color) factor coding color. CI.alpha Optional transparency lines used draw confidence intervals. add.point TRUE points added confidence intervals. point.size Size added points case add.point TRUE. point.type Shape added points case add.point TRUE defined integer coding unique common shape factor coding shape.","code":""},{"path":"/reference/ecdfplotwithCI.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ECDF plot of a variable with given confidence intervals on this variable — ecdfplotwithCI","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/ecdfplotwithCI.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"ECDF plot of a variable with given confidence intervals on this variable — ecdfplotwithCI","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/ecdfplotwithCI.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ECDF plot of a variable with given confidence intervals on this variable — ecdfplotwithCI","text":"","code":"# (1) a toy example (a very small subsample of a microarray data set) # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package=\"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.001) #> Removing intercept from test coefficients f <- drcfit(s_quad, progressbar = TRUE) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |======= | 10% | |============== | 20% | |===================== | 30% | |============================ | 40% | |=================================== | 50% | |========================================== | 60% | |================================================= | 70% | |======================================================== | 80% | |=============================================================== | 90% | |======================================================================| 100% r <- bmdcalc(f) set.seed(1) # to get reproducible results with a so small number of iterations b <- bmdboot(r, niter = 5) # with a non reasonable value for niter #> Warning: #> A small number of iterations (less than 1000) may not be sufficient to #> ensure a good quality of bootstrap confidence intervals. #> The bootstrap may be long if the number of items and the number of #> bootstrap iterations is high. #> | | | 0% | |======= | 10% | |============== | 20% | |===================== | 30% | |============================ | 40% | |========================================== | 60% | |================================================= | 70% | |======================================================== | 80% | |=============================================================== | 90% | |======================================================================| 100% # !!!! TO GET CORRECT RESULTS # !!!! niter SHOULD BE FIXED FAR LARGER , e.g. to 1000 # !!!! but the run will be longer # manual ecdf plot of the bootstrap results as an ecdf distribution # on BMD, plot that could also be obtained with plot(b) # in this simple case # a <- b$res[is.finite(b$res$BMD.zSD.upper), ] ecdfplotwithCI(variable = a$BMD.zSD, CI.lower = a$BMD.zSD.lower, CI.upper = a$BMD.zSD.upper, CI.col = \"red\") # \\donttest{ # (2) An example from data published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # This function can also be used to go deeper in the exploration of the biological # meaning of the responses. Here is an example linking the DRomics outputs # with the functional annotation of the responding metabolites of the microalgae # Scenedesmus vacuolatus to the biocide triclosan. # This extra step uses a dataframe previously built by the user which links the items # to the biological information of interest (e.g. KEGG pathways). # importation of a dataframe with metabolomic results # (output $res of bmdcalc() or bmdboot() functions) resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # importation of a dataframe with annotation of each item # identified in the previous file (this dataframe must be previously built by the user) # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe # bootstrap results and annotation annotres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(annotres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport ### an ECDFplot with confidence intervals by pathway # with color coding for dose-response trend ecdfplotwithCI(variable = annotres$BMD.zSD, CI.lower = annotres$BMD.zSD.lower, CI.upper = annotres$BMD.zSD.upper, by = annotres$path_class, CI.col = annotres$trend) # (3) an example on a microarray data set (a subsample of a greater data set) # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 1000 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" #> [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" #> Data were normalized between arrays using the following method: cyclicloess (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.001)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.001: 78 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |= | 1% | |== | 3% | |=== | 4% | |==== | 5% | |==== | 6% | |===== | 8% | |====== | 9% | |======= | 10% | |======== | 12% | |========= | 13% | |========== | 14% | |=========== | 15% | |============ | 17% | |============= | 18% | |============= | 19% | |============== | 21% | |=============== | 22% | |================ | 23% | |================= | 24% | |================== | 26% | |=================== | 27% | |==================== | 28% | |===================== | 29% | |====================== | 31% | |====================== | 32% | |======================= | 33% | |======================== | 35% | |========================= | 36% | |========================== | 37% | |=========================== | 38% | 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|=============================================== | 67% | |================================================ | 68% | |================================================ | 69% | |================================================= | 71% | |================================================== | 72% | |=================================================== | 73% | |==================================================== | 74% | |===================================================== | 76% | |====================================================== | 77% | |======================================================= | 78% | |======================================================== | 79% | |========================================================= | 81% | |========================================================= | 82% | |========================================================== | 83% | |=========================================================== | 85% | |============================================================ | 86% | |============================================================= | 87% | |============================================================== | 88% | |=============================================================== | 90% | |================================================================ | 91% | |================================================================= | 92% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |===================================================================== | 99% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 11 dose-response curves out of 78 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 67 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 0 11 30 23 #> log-Gauss-probit #> 3 #> Distribution of the trends (curve shapes) among the 67 fitted dose-response curves: #> #> U bell dec inc #> 6 20 22 19 (r <- bmdcalc(f)) #> 1 BMD-xfold values and 0 BMD-zSD values are not defined (coded NaN as #> the BMR stands outside the range of response values defined by the model). #> 28 BMD-xfold values and 0 BMD-zSD values could not be calculated (coded #> NA as the BMR stands within the range of response values defined by the #> model but outside the range of tested doses). (b <- bmdboot(r, niter = 100)) # niter to put at 1000 for a better precision #> Warning: #> A small number of iterations (less than 1000) may not be sufficient to #> ensure a good quality of bootstrap confidence intervals. #> The bootstrap may be long if the number of items and the number of #> bootstrap iterations is high. #> | | | 0% | |= | 1% | |== | 3% | |=== | 4% | |==== | 6% | |===== | 7% | |====== | 9% | |======= | 10% | |======== | 12% | |========= | 13% | |========== | 15% | |=========== | 16% | |============= | 18% | |============== | 19% | |=============== | 21% | |================ | 22% | |================= | 24% | |================== | 25% | |=================== | 27% | |==================== | 28% | |===================== | 30% | |====================== | 31% | |======================= | 33% | |======================== | 34% | |========================= | 36% | |========================== | 37% | |=========================== | 39% | |============================ | 40% | |============================= | 42% | |============================== | 43% | |=============================== | 45% | |================================ | 46% | |================================= | 48% | |================================== | 49% | |==================================== | 51% | |===================================== | 52% | |====================================== | 54% | |======================================= | 55% | |======================================== | 57% | |========================================= | 58% | |=========================================== | 61% | |============================================ | 63% | |============================================= | 64% | |============================================== | 66% | |=============================================== | 67% | |================================================ | 69% | |================================================= | 70% | |================================================== | 72% | |=================================================== | 73% | |==================================================== | 75% | |===================================================== | 76% | |====================================================== | 78% | |========================================================= | 82% | |=========================================================== | 84% | |============================================================ | 85% | |============================================================= | 87% | |============================================================== | 88% | |=============================================================== | 90% | |================================================================ | 91% | |================================================================= | 93% | |=================================================================== | 96% | |==================================================================== | 97% | |===================================================================== | 99% | |======================================================================| 100% #> Bootstrap confidence interval computation failed on 4 items among 67 #> due to lack of convergence of the model fit for a fraction of the #> bootstrapped samples greater than 0.5. #> For 0 BMD.zSD values and 35 BMD.xfold values among 67 at least one #> bound of the 95 percent confidence interval could not be computed due #> to some bootstrapped BMD values not reachable due to model asymptotes #> or reached outside the range of tested doses (bounds coded Inf)). # (3.a) # manual ecdf plot of the bootstrap results as an ecdf distribution # on BMD for each trend # plot that could also be obtained with plot(b, by = \"trend\") # in this simple case # a <- b$res[is.finite(b$res$BMD.zSD.upper), ] ecdfplotwithCI(variable = a$BMD.zSD, CI.lower = a$BMD.zSD.lower, CI.upper = a$BMD.zSD.upper, by = a$trend, CI.col = \"red\") # (3.b) # ecdf plot of the bootstrap results as an ecdf distribution # on BMD for each model # with the color of the confidence intervals coding for the trend # ecdfplotwithCI(variable = a$BMD.zSD, CI.lower = a$BMD.zSD.lower, CI.upper = a$BMD.zSD.upper, by = a$model, CI.col = a$trend) # changing the size of the points and the transparency of CI lines ecdfplotwithCI(variable = a$BMD.zSD, CI.lower = a$BMD.zSD.lower, CI.upper = a$BMD.zSD.upper, by = a$model, CI.col = a$trend, CI.alpha = 0.5, point.size = 0.5) # with the model coding for the type of points ecdfplotwithCI(variable = a$BMD.zSD, CI.lower = a$BMD.zSD.lower, CI.upper = a$BMD.zSD.upper, CI.col = a$trend, CI.alpha = 0.5, point.size = 0.5, point.type = a$model) # (3.c) # ecdf plot of the bootstrap results as an ecdf distribution on # on BMD_L (lower value of the confidence interval) for each trend # ecdfplotwithCI(variable = a$BMD.zSD.lower, CI.lower = a$BMD.zSD.lower, CI.upper = a$BMD.zSD.upper, by = a$model, CI.col = a$trend, add.point = FALSE) # }"},{"path":"/reference/ecdfquantileplot.html","id":null,"dir":"Reference","previous_headings":"","what":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","title":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","text":"Plots given quantile variable calculated group ECDF plot points sized numbers items per group. context package function intended used BMD variable groups defined user functional annotation.","code":""},{"path":"/reference/ecdfquantileplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","text":"","code":"ecdfquantileplot(variable, by, quantile.prob = 0.5, title)"},{"path":"/reference/ecdfquantileplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","text":"variable numeric vector corresponding variable want calculate given quantile group. context package variable may BMD. factor length defining groups. context package factor may code groups defined user functional annotation. quantile.prob probability (]0, 1[) defining quantile calculate group. title optional title plot.","code":""},{"path":"/reference/ecdfquantileplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","text":"given quantile calculated group (e.g.items metabolic pathway) using function quantile plotted ECDF plot. ECDF plot quantiles point sized according number items corresponding group (e.g. metabolic pathway). recommend use new function sensitivityplot may convenient offers options.","code":""},{"path":"/reference/ecdfquantileplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/ecdfquantileplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/ecdfquantileplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","text":"","code":"# (1) An example from data published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # a dataframe with metabolomic results (output $res of bmdcalc() or bmdboot() functions) resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # a dataframe with annotation of each item identified in the previous file # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe # bootstrap results and annotation annotres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(annotres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport ### an ECDFplot of quantiles of BMD-zSD calculated by pathway ecdfquantileplot(variable = annotres$BMD.zSD, by = annotres$path_class, quantile.prob = 0.25) # same plot in log10 dose scale (not interesting on this example # but could be on another one) if (require(ggplot2)) ecdfquantileplot(variable = annotres$BMD.zSD, by = annotres$path_class, quantile.prob = 0.25) + scale_y_log10()"},{"path":"/reference/formatdata4DRomics.html","id":null,"dir":"Reference","previous_headings":"","what":"Build an R object that can be used as data input in DRomics — formatdata4DRomics","title":"Build an R object that can be used as data input in DRomics — formatdata4DRomics","text":"Build R object can used data input data importation function two inputs: nitems x nsamples matrix coding signal nsamples vector doses","code":""},{"path":"/reference/formatdata4DRomics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build an R object that can be used as data input in DRomics — formatdata4DRomics","text":"","code":"formatdata4DRomics(signalmatrix, dose, samplenames)"},{"path":"/reference/formatdata4DRomics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build an R object that can be used as data input in DRomics — formatdata4DRomics","text":"signalmatrix matrix data one row item one column sample. row names matrix taken identify items. Depending type measured signal, look help corresponding importation function especially check use good scale data RNAseqdata, microarraydata, continuousomicdata continuousanchoringdata. dose numeric vector giving dose sample. samplenames character vector giving names samples (optional argument - given, col names signalmatrix taken sample names).","code":""},{"path":"/reference/formatdata4DRomics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build an R object that can be used as data input in DRomics — formatdata4DRomics","text":"R object corresponds dataframe can passed input first argument data importation functions RNAseqdata, microarraydata, continuousomicdata continuousanchoringdata.","code":""},{"path":[]},{"path":"/reference/formatdata4DRomics.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Build an R object that can be used as data input in DRomics — formatdata4DRomics","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/formatdata4DRomics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Build an R object that can be used as data input in DRomics — formatdata4DRomics","text":"","code":"# (1) load of data # data(zebraf) str(zebraf) #> List of 3 #> $ counts: int [1:1000, 1:16] 453 331 897 12 326 533 1948 904 583 154 ... #> ..- attr(*, \"dimnames\")=List of 2 #> .. ..$ : chr [1:1000] \"ENSDARG00000102141\" \"ENSDARG00000102123\" \"ENSDARG00000114503\" \"ENSDARG00000115971\" ... #> .. ..$ : chr [1:16] \"I10_05mG_E5\" \"I10_05mG_E6\" \"I10_05mG_E7\" \"I10_C5\" ... #> $ dose : num [1:16] 500 500 500 0 0 0 0 50000 50000 50000 ... #> $ batch : Factor w/ 2 levels \"I10\",\"I17\": 1 1 1 1 1 1 1 2 2 2 ... # (2) formating of data for use in DRomics # data4DRomics <- formatdata4DRomics(signalmatrix = zebraf$counts, dose = zebraf$dose) # \\donttest{ # (3) Normalization and transformation of data # o <- RNAseqdata(data4DRomics) #> Just wait, the transformation using regularized logarithm (rlog) may #> take a few minutes. #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. plot(o) # }"},{"path":"/reference/itemselect.html","id":null,"dir":"Reference","previous_headings":"","what":"Selection of significantly responsive items — itemselect","title":"Selection of significantly responsive items — itemselect","text":"Significantly responsive items selected using one three proposed methods: quadratic trend test, linear trend test ANOVA-based test.","code":""},{"path":"/reference/itemselect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selection of significantly responsive items — itemselect","text":"","code":"itemselect(omicdata, select.method = c(\"quadratic\", \"linear\", \"ANOVA\"), FDR = 0.05, max.ties.prop = 0.2) # S3 method for class 'itemselect' print(x, nfirstitems = 20, ...)"},{"path":"/reference/itemselect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Selection of significantly responsive items — itemselect","text":"omicdata object class \"microarraydata\", \"RNAseqdata\", \"metabolomicdata\" \"continuousanchoringdata\" respectively returned functions microarraydata, RNAseqdata, metabolomicdata continuousanchoringdata. select.method \"quadratic\" quadratic trend test dose ranks, \"linear\" linear trend test dose ranks \"ANOVA\" ANOVA-type test (see details explaination). FDR threshold term FDR (False Discovery Rate) selecting responsive items. max.ties.prop maximal tolerated proportion tied values item, item selected (must ]0, 0.5], default fixed 0.2 - see details description filtering step). x object class \"itemselect\". nfirstitems maximum number selected items print. ... arguments passed print function.","code":""},{"path":"/reference/itemselect.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Selection of significantly responsive items — itemselect","text":"selection responsive items performed using limma package microarray continuous omics data (metabolomics), DESeq2 package RNAseq data lm function continuous anchoring data. Three methods proposed (described ). Within limma methods implemented using functions lmFit, eBayes topTable p-values ajusted multiple testing using Benjamini-Hochberg method (also called q-values), false discovery rate given input (argument FDR). Within DESeq2 methods implemented using functions DESeqDataSetFromMatrix, DESeq results p-values ajusted multiple testing using Benjamini-Hochberg method (also called q-values), false discovery rate given input (argument FDR). continuous anchoring data, lm anova functions used fit model compare null model, pvalues corrected using function p.adjust Benjamini-Hochberg method. ANOVA_based test (\"ANOVA\") classically used selection omics data general case requires many replicates per dose efficient, thus really suited dose-response design. linear trend test (\"linear\") aims detecting monotonic trends dose-response designs, whatever number replicates per dose. proposed Tukey (1985), tests global significance linear model describing response function dose rank-scale. quadratic trend test (\"quadratic\") tests global significance quadratic model describing response function dose rank-scale. variant linear trend method aims detecting monotonic non monotonic trends dose-response designs, whatever number replicates per dose (default chosen method). use one previously described tests, filter based proportion tied values also performed whatever type data, assuming tied values correspond minimal common value non detections imputed. items proportion tied minimal values input argument max.ties.prop eliminated selection.","code":""},{"path":"/reference/itemselect.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Selection of significantly responsive items — itemselect","text":"itemselect returns object class \"itemselect\", list 5 components: adjpvalue vector p-values adjusted Benjamini-Hochberg method (also called q-values) selected items (adjpvalue inferior FDR) sorted ascending order selectindex corresponding vector row indices selected items object omicdata omicdata corresponding object class \"microarraydata\", \"RNAseqdata\", \"continuousomicdata\" \"continuousanchoringdata\" given input. select.method selection method given input. FDR threshold term FDR given input. print \"itemselect\" object gives number selected items identifiers 20 responsive items.","code":""},{"path":[]},{"path":"/reference/itemselect.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Selection of significantly responsive items — itemselect","text":"Tukey JW, Ciminera JL Heyse JF (1985), Testing statistical certainty response increasing doses drug. Biometrics, 295-301. Ritchie , Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth, GK (2015), limma powers differential expression analyses RNA-sequencing microarray studies. Nucleic Acids Research 43, e47. Love MI, Huber W, Anders S (2014), Moderated estimation fold change dispersion RNA-seq data DESeq2. Genome biology, 15(12), 550.","code":""},{"path":"/reference/itemselect.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Selection of significantly responsive items — itemselect","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/itemselect.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Selection of significantly responsive items — itemselect","text":"","code":"# (1) an example on a microarray data set (a subsample of a greater data set) # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 1000 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" #> [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" #> Data were normalized between arrays using the following method: cyclicloess # 1.a using the quadratic trend test # (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.05)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.05: 318 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" print(s_quad, nfirstitems = 30) #> Number of selected items using a quadratic trend test with an FDR of 0.05: 318 #> Identifiers of the first 30 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" \"351.2\" \"12.1\" \"7.2\" \"239.2\" \"368.1\" \"263.2\" \"264.1\" #> [28] \"138.1\" \"233.2\" \"334.1\" # to get the names of all the selected items (selecteditems <- s_quad$omicdata$item[s_quad$selectindex]) #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" \"351.2\" \"12.1\" \"7.2\" \"239.2\" \"368.1\" \"263.2\" \"264.1\" #> [28] \"138.1\" \"233.2\" \"334.1\" \"353.1\" \"359\" \"136.1\" \"167.1\" \"138.2\" \"27.2\" #> [37] \"312.1\" \"367.1\" \"264.2\" \"334.2\" \"168.2\" \"247.2\" \"336\" \"168.1\" \"25.2\" #> [46] \"7.1\" \"136.2\" \"167.2\" \"233.1\" \"353.2\" \"360.2\" \"13.2\" \"12.2\" \"88.1\" #> [55] \"263.1\" \"352.2\" \"162.2\" \"70\" \"161.1\" \"275.1\" \"358.2\" \"88.2\" \"320.1\" #> [64] \"352.1\" \"311.1\" \"27.1\" \"4\" \"358.1\" \"42.2\" \"55.2\" \"92\" \"249.2\" #> [73] \"360.1\" \"103\" \"162.1\" \"320.2\" \"198.1\" \"229.2\" \"371.2\" \"225\" \"321.2\" #> [82] \"329.1\" \"438.2\" \"229.1\" \"25.1\" \"348\" \"228.1\" \"247.1\" \"371.1\" \"512.1\" #> [91] \"81\" \"13.1\" \"148\" \"311.2\" \"113.2\" \"83.2\" \"268.2\" \"268.1\" \"84.1\" #> [100] \"321.1\" \"467.1\" \"228.2\" \"113.1\" \"489.1\" \"330.2\" \"249.1\" \"83.1\" \"439.2\" #> [109] \"369.2\" \"330.1\" \"295.1\" \"294.1\" \"295.2\" \"490.2\" \"117.2\" \"337\" \"267.2\" #> [118] \"274.1\" \"367.2\" \"468.1\" \"116.1\" \"368.2\" \"401.2\" \"116.2\" \"137\" \"341.1\" #> [127] \"195.2\" \"490.1\" \"467.2\" \"404.2\" \"446.1\" \"329.2\" \"85.2\" \"118.1\" \"118.2\" #> [136] \"126\" \"267.1\" \"115\" \"132.1\" \"401.1\" \"402.1\" \"449.1\" \"404.1\" \"199.2\" #> [145] \"312.2\" \"84.2\" \"74.1\" \"275.2\" \"446.2\" \"373.1\" \"449.2\" \"465.2\" \"117.1\" #> [154] \"294.2\" \"74.2\" \"194.2\" \"195.1\" \"40.2\" \"497.2\" \"169.2\" \"5.2\" \"194.1\" #> [163] \"372.2\" \"274.2\" \"410.1\" \"169.1\" \"170\" \"171.1\" \"202.2\" \"207.1\" \"373.2\" #> [172] \"402.2\" \"496.1\" \"372.1\" \"298.2\" \"67.1\" \"144.1\" \"154.1\" \"199.1\" \"369.1\" #> [181] \"396.2\" \"232.1\" \"177.2\" \"41.2\" \"85.1\" \"159\" \"171.2\" \"245.2\" \"396.1\" #> [190] \"87.2\" \"299.1\" \"496.2\" \"161.2\" \"266.1\" \"512.2\" \"464.1\" \"356.1\" \"397.1\" #> [199] \"8.1\" \"39.2\" \"464.2\" \"61.2\" \"385.1\" \"131.2\" \"176.1\" \"497.1\" \"245.1\" #> [208] \"528.1\" \"131.1\" \"465.1\" \"54.1\" \"204.2\" \"451.1\" \"54.2\" \"441.1\" \"260.1\" #> [217] \"299.2\" \"397.2\" \"400.2\" \"436\" \"177.1\" \"232.2\" \"386.2\" \"260.2\" \"256.1\" #> [226] \"87.1\" \"265.1\" \"204.1\" \"258.2\" \"67.2\" \"198.2\" \"439.1\" \"40.1\" \"206.2\" #> [235] \"66.1\" \"385.2\" \"356.2\" \"293.2\" \"527.1\" \"258.1\" \"129.1\" \"68.1\" \"191.1\" #> [244] \"511.1\" \"286.1\" \"75.2\" \"128.2\" \"428.1\" \"122.1\" \"144.2\" \"282.1\" \"523.2\" #> [253] \"377.2\" \"386.1\" \"128.1\" \"440.1\" \"523.1\" \"461.2\" \"293.1\" \"342.2\" \"433.1\" #> [262] \"68.2\" \"86.2\" \"451.2\" \"298.1\" \"291.1\" \"342.1\" \"43.1\" \"499.1\" \"333.2\" #> [271] \"227.1\" \"121.1\" \"208.2\" \"412.2\" \"438.1\" \"282.2\" \"376.2\" \"75.1\" \"155.1\" #> [280] \"526.2\" \"291.2\" \"482.2\" \"129.2\" \"526.1\" \"121.2\" \"440.2\" \"515.2\" \"65.2\" #> [289] \"341.2\" \"122.2\" \"461.1\" \"224.1\" \"419.1\" \"482.1\" \"466.1\" \"524.2\" \"524.1\" #> [298] \"112.1\" \"41.1\" \"284.1\" \"187.1\" \"513.2\" \"110.1\" \"101.2\" \"419.2\" \"210.1\" #> [307] \"205.1\" \"498.2\" \"382.1\" \"133.2\" \"390.1\" \"243.1\" \"452.1\" \"110.2\" \"513.1\" #> [316] \"468.2\" \"511.2\" \"65.1\" # \\donttest{ # 1.b using the linear trend test # (s_lin <- itemselect(o, select.method = \"linear\", FDR = 0.05)) #> Removing intercept from test coefficients #> Number of selected items using a linear trend test with an FDR of 0.05: 90 #> Identifiers of the first 20 most responsive items: #> [1] \"300.2\" \"301.1\" \"239.1\" \"300.1\" \"240.1\" \"301.2\" \"240.2\" \"239.2\" \"364.2\" #> [10] \"363.1\" \"364.1\" \"136.1\" \"363.2\" \"27.2\" \"138.1\" \"336\" \"233.2\" \"25.2\" #> [19] \"27.1\" \"136.2\" # 1.c using the ANOVA-based test # (s_ANOVA <- itemselect(o, select.method = \"ANOVA\", FDR = 0.05)) #> Removing intercept from test coefficients #> Number of selected items using an ANOVA type test with an FDR of 0.05: 203 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"363.2\" \"367.1\" \"383.1\" \"383.2\" \"363.1\" \"364.1\" \"364.2\" \"384.1\" #> [10] \"368.1\" \"300.2\" \"351.1\" \"301.1\" \"320.1\" \"350.2\" \"300.1\" \"351.2\" \"353.1\" #> [19] \"353.2\" \"350.1\" # 1.d using the quadratic trend test with a smaller false discovery rate # (s_quad.2 <- itemselect(o, select.method = \"quadratic\", FDR = 0.001)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.001: 78 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" # }"},{"path":"/reference/metabolomicdata.html","id":null,"dir":"Reference","previous_headings":"","what":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","title":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","text":"Metabolomic continuous omics data imported .txt file (internally imported using function read.table) checked R object class data.frame (see description argument file required format data). normalization transformation provided function. pretreatment continuous omic data data must done importation data, data must imported log scale needed (imperative example metabolomic data), can directly modelled using normal error model. strong hypothesis required selection items dose-reponse modelling. example, basic procedure pre-treatment metabolomic data follow three steps described thereafter: ) removing metabolites proportion missing data (non detections) across samples high (20 50 percents according tolerance level); ii) retrieving missing values data using half minimum method (.e. half minimum value found metabolite across samples); iii) log-transformation values. scaling total intensity (normalization sum signals sample) another normalization necessary pertinent, recommend three previously decribed steps.","code":""},{"path":"/reference/metabolomicdata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","text":"","code":"continuousomicdata(file, backgrounddose, check = TRUE) metabolomicdata(file, backgrounddose, check = TRUE) # S3 method for class 'continuousomicdata' print(x, ...) # S3 method for class 'continuousomicdata' plot(x, range4boxplot = 0, ...)"},{"path":"/reference/metabolomicdata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","text":"file name .txt file (e.g. \"mydata.txt\") containing one row per item, first column corresponding identifier item, columns giving responses item replicate dose concentration. first line, name identifier column, must tested doses concentrations numeric format corresponding replicate (example, triplicates treatment, first line \"item\", 0, 0, 0, 0.1, 0.1, 0.1, etc.). file imported within function using function read.table default field separator (sep argument)default decimal separator (dec argument \".\"). Alternatively R object class data.frame can directly given input, corresponding output read.table(file, header = FALSE) file described . two alternatives illustrated examples. backgrounddose argument must used dose zero data, prevent calculation BMD extrapolation. doses equal value given backgrounddose fixed 0, considered background level exposition. check TRUE format input file checked. x object class \"continuousomicdata\". range4boxplot argument passed boxplot(), fixed default 0 prevent producing large plot files due many outliers. Can put 1.5 obtain classical boxplots. ... arguments passed print plot functions.","code":""},{"path":"/reference/metabolomicdata.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","text":"function imports data, checks format (see description argument file required format data) gives print information help user check coding data correct : tested doses (concentrations), number replicates dose, number items identifiers first 20 items. metabolomicdata() first name gave function. renamed continuousomicdata (keeping first name available) offer use continuous omic data proteomics data RT-QPCR data. Nevertheless one take care scale data imported DRomics. transformation may needed enable use normal error model step DRomics workflow (selection items modelling BMD calculation)","code":""},{"path":"/reference/metabolomicdata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","text":"continuousomicdata() returns object class \"continuousomicdata\", list 7 components: data numeric matrix responses item replicate (one line per item, one column per replicate) dose numeric vector tested doses concentrations corresponding column data item character vector identifiers items, corresponding line data design table experimental design (tested doses number replicates dose) control user data.mean numeric matrix mean responses item per dose (mean corresponding replicates) (one line per item, one column per unique value dose data.sd numeric matrix standard deviations response item per dose (sd corresponding replicates, NA replicate) (one line per item, one column per unique value dose) containsNA TRUE data set contains NA values print continuousomicdata object gives tested doses (concentrations) number replicates dose, number items, identifiers first 20 items (check good coding data) normalization method. plot continuousomicdata object shows data distribution dose concentration replicate.","code":""},{"path":[]},{"path":"/reference/metabolomicdata.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/metabolomicdata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","text":"","code":"# (1) import and check of metabolomic data # (an example on a subsample of a greater data set given in the package (see ?Scenedesmus)) # datafilename <- system.file(\"extdata\", \"metabolo_sample.txt\", package = \"DRomics\") o <- continuousomicdata(datafilename) #> Warning: #> We recommend you to check that your omic data were correctly pretreated #> before importation. In particular data (e.g. metabolomic signal) should #> have been log-transformed, without replacing 0 values by NA values #> (consider using the half minimum method instead for example). print(o) #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.1 1.79 2.92 4.78 7.76 #> 10 6 2 2 2 6 2 #> Number of items: 109 #> Identifiers of the first 20 items: #> #> [1] \"P_2\" \"P_4\" \"P_5\" \"P_6\" \"P_7\" \"P_10\" \"P_11\" \"P_12\" \"P_14\" \"P_16\" #> [11] \"P_19\" \"P_21\" \"P_22\" \"P_26\" \"P_32\" \"P_34\" \"P_35\" \"P_36\" \"P_37\" \"P_38\" plot(o) PCAdataplot(o) # if you want to skip the check of data o <- continuousomicdata(datafilename, check = FALSE) # If you want to use your own data set just replace datafilename, # the first argument of metabolomicdata(), # by the name of your data file (e.g. \"mydata.txt\") # # You should take care that the field separator of this data file is one # of the default field separators recognised by the read.table() function # when it is used with its default field separator (sep argument) # Tabs are recommended. # Use of an R object of class data.frame # An example using the complete data set # Scenedesmus_metab (see ?Scenedesmus for details) data(Scenedesmus_metab) (o <- continuousomicdata(Scenedesmus_metab)) #> Warning: #> We recommend you to check that your omic data were correctly pretreated #> before importation. In particular data (e.g. metabolomic signal) should #> have been log-transformed, without replacing 0 values by NA values #> (consider using the half minimum method instead for example). #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.1 1.79 2.92 4.78 7.76 #> 6 3 3 3 3 3 3 #> Number of items: 224 #> Identifiers of the first 20 items: #> #> [1] \"NAP_1\" \"NAP_2\" \"NAP_3\" \"NAP_4\" \"NAP_5\" \"NAP_6\" \"NAP_7\" \"NAP_8\" #> [9] \"NAP_9\" \"NAP_11\" \"NAP_13\" \"NAP_14\" \"NAP_15\" \"NAP_16\" \"NAP_17\" \"NAP_18\" #> [17] \"NAP_19\" \"NAP_20\" \"NAP_21\" \"NAP_22\" plot(o)"},{"path":"/reference/microarraydata.html","id":null,"dir":"Reference","previous_headings":"","what":"Import, check and normalization of single-channel microarray data — microarraydata","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"Single-channel microarray data log2 imported .txt file (internally imported using function read.table), checked R object class data.frame (see description argument file required format data)normalized (arrays normalization). omicdata deprecated version microarraydata.","code":""},{"path":"/reference/microarraydata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"","code":"microarraydata(file, backgrounddose, check = TRUE, norm.method = c(\"cyclicloess\", \"quantile\", \"scale\", \"none\")) omicdata(file, backgrounddose, check = TRUE, norm.method = c(\"cyclicloess\", \"quantile\", \"scale\", \"none\")) # S3 method for class 'microarraydata' print(x, ...) # S3 method for class 'microarraydata' plot(x, range4boxplot = 0, ...)"},{"path":"/reference/microarraydata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"file name .txt file (e.g. \"mydata.txt\") containing one row per item, first column corresponding identifier item, columns giving responses item replicate dose concentration. first line, name identifier column, must tested doses concentrations numeric format corresponding replicate (example, triplicates treatment, first line \"item\", 0, 0, 0, 0.1, 0.1, 0.1, etc.). file imported within function using function read.table default field separator (sep argument) default decimal separator (dec argument \".\"). Alternatively R object class data.frame can directly given input, corresponding output read.table(file, header = FALSE) file described . backgrounddose argument must used dose zero data, prevent calculation BMD extrapolation. doses equal value given backgrounddose fixed 0, considered background level exposition. check TRUE format input file checked. norm.method \"none\" normalization performed, else normalization performed using function normalizeBetweenArrays limma package using specified method. x object class \"microarraydata\". range4boxplot argument passed boxplot(), fixed default 0 prevent producing large plot files due many outliers. Can put 1.5 obtain classical boxplots. ... arguments passed print plot functions.","code":""},{"path":"/reference/microarraydata.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"function imports data, checks format (see description argument file required format data) gives print information help user check coding data correct : tested doses (concentrations) number replicates dose, number items, identifiers first 20 items. argument norm.method \"none\", data normalized using function normalizeBetweenArrays limma package using specified method : \"cyclicloess\" (default choice), \"quantile\" \"scale\".","code":""},{"path":"/reference/microarraydata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"microarraydata returns object class \"microarraydata\", list 9 components: data numeric matrix normalized responses item replicate (one line per item, one column per replicate) dose numeric vector tested doses concentrations corresponding column data item character vector identifiers items, corresponding line data design table experimental design (tested doses number replicates dose) control user data.mean numeric matrix mean responses item per dose (mean corresponding replicates) (one line per item, one column per unique value dose data.sd numeric matrix standard deviations response item per dose (sd corresponding replicates, NA replicate) (one line per item, one column per unique value dose) norm.method normalization method specified input data.beforenorm numeric matrix responses item replicate (one line per item, one column per replicate) normalization containsNA always FALSE microarray data allowed contain NA values print microarraydata object gives tested doses (concentrations) number replicates dose, number items, identifiers first 20 items (check good coding data) normalization method. plot microarraydata object shows data distribution dose concentration replicate normalization.","code":""},{"path":[]},{"path":"/reference/microarraydata.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"Ritchie , Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth, GK (2015), limma powers differential expression analyses RNA-sequencing microarray studies. Nucleic Acids Research 43, e47.","code":""},{"path":"/reference/microarraydata.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/microarraydata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"","code":"# (1) import, check and normalization of microarray data # (an example on a subsample of a greater data set published in Larras et al. 2018 # Transcriptomic effect of triclosan in the chlorophyte Scenedesmus vacuolatus) datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package=\"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. print(o) #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 100 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"6.1\" \"7.1\" \"8.1\" \"9.1\" \"10.1\" #> [11] \"11.1\" \"12.1\" \"13.1\" \"14.1\" \"15\" \"16.1\" \"17.1\" \"18.1\" \"19.1\" \"20.1\" #> Data were normalized between arrays using the following method: cyclicloess plot(o) PCAdataplot(o) PCAdataplot(o, label = TRUE) # If you want to use your own data set just replace datafilename, # the first argument of microarraydata(), # by the name of your data file (e.g. \"mydata.txt\") # # You should take care that the field separator of this data file is one # of the default field separators recognised by the read.table() function # when it is used with its default field separator (sep argument) # Tabs are recommended. # \\donttest{ # (2) normalization with other methods (o.2 <- microarraydata(datafilename, check = TRUE, norm.method = \"quantile\")) #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 100 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"6.1\" \"7.1\" \"8.1\" \"9.1\" \"10.1\" #> [11] \"11.1\" \"12.1\" \"13.1\" \"14.1\" \"15\" \"16.1\" \"17.1\" \"18.1\" \"19.1\" \"20.1\" #> Data were normalized between arrays using the following method: quantile plot(o.2) (o.3 <- microarraydata(datafilename, check = TRUE, norm.method = \"scale\")) #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 100 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"6.1\" \"7.1\" \"8.1\" \"9.1\" \"10.1\" #> [11] \"11.1\" \"12.1\" \"13.1\" \"14.1\" \"15\" \"16.1\" \"17.1\" \"18.1\" \"19.1\" \"20.1\" #> Data were normalized between arrays using the following method: scale plot(o.3) # }"},{"path":"/reference/selectgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Selection of groups on which to focus — selectgroups","title":"Selection of groups on which to focus — selectgroups","text":"Selection groups (e.g. corresponding different biological annotations) focus, based sensitivity (BMD summary value) representativeness (number items group).","code":""},{"path":"/reference/selectgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selection of groups on which to focus — selectgroups","text":"","code":"selectgroups(extendedres, group, explev, BMDmax, BMDtype = c(\"zSD\", \"xfold\"), BMDsummary = c(\"first.quartile\", \"median\" ), nitemsmin = 3, keepallexplev = FALSE)"},{"path":"/reference/selectgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Selection of groups on which to focus — selectgroups","text":"extendedres dataframe results provided drcfit (fitres) bmdcalc (res) subset data frame (selected lines). dataframe extended additional columns coming group (example biological annotation items) optionnally another experimental level (example molecular level), lines can replicated corresponding item one annotation. group name column extendedres coding groups. explev optional argument naming column extendedres coding experimental level. BMDmax maximum BMD summary value used limit groups sensitive (optional input : missing selection based BMD). BMDtype type BMD used selection BMD, \"zSD\" (default choice) \"xfold\". BMDsummary type summary used selection based BMD, \"first.quartile\" (default choice first quartile BMD values per group) \"median\" (choice median BMD values per group). nitemsmin minimum number items per group limit groups represented (can put 1 want select number: recommended. keepallexplev TRUE (default value FALSE), group selected least one experimental level, kept selection experimental levels.","code":""},{"path":"/reference/selectgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Selection of groups on which to focus — selectgroups","text":"function provide subset input extendedres corresponding groups number items representing group greater equal nitemsmin BMDmax secified, BMD summary value less equal BMDmax. one experimental level (explev specified), selection groups made separately experimental level: group may selected one experimental level removed another one. function eliminates rows NA values chosen BMD (BMDtype) performing selection.","code":""},{"path":"/reference/selectgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Selection of groups on which to focus — selectgroups","text":"dataframe corresponding subset extendedres given input, can used exploration using example bmdplot, bmdplotwithgradient, trendplot sensitivityplot.","code":""},{"path":[]},{"path":"/reference/selectgroups.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Selection of groups on which to focus — selectgroups","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/selectgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Selection of groups on which to focus — selectgroups","text":"","code":"# (1) # An example from the paper published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # the dataframe with metabolomic results resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # the dataframe with annotation of each item identified in the previous file # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe extendedres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(extendedres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport # (1) Sensitivity by pathway # (1.a) before selection sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\") # (1.b) after selection on representativeness extendedres.b <- selectgroups(extendedres, group = \"path_class\", nitemsmin = 10) sensitivityplot(extendedres.b, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\") # \\donttest{ # (1.c) after selection on sensitivity extendedres.c <- selectgroups(extendedres, group = \"path_class\", BMDmax = 1.25, BMDtype = \"zSD\", BMDsummary = \"first.quartile\", nitemsmin = 1) sensitivityplot(extendedres.c, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\") # (1.d) after selection on representativeness and sensitivity extendedres.d <- selectgroups(extendedres, group = \"path_class\", BMDmax = 1.25, BMDtype = \"zSD\", BMDsummary = \"first.quartile\", nitemsmin = 10) sensitivityplot(extendedres.d, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\") # (2) # An example with two molecular levels # ### Rename metabolomic results metabextendedres <- extendedres # Import the dataframe with transcriptomic results contigresfilename <- system.file(\"extdata\", \"triclosanSVcontigres.txt\", package = \"DRomics\") contigres <- read.table(contigresfilename, header = TRUE, stringsAsFactors = TRUE) str(contigres) #> 'data.frame':\t447 obs. of 27 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... # Import the dataframe with functional annotation (or any other descriptor/category # you want to use, here KEGG pathway classes) contigannotfilename <- system.file(\"extdata\", \"triclosanSVcontigannot.txt\", package = \"DRomics\") contigannot <- read.table(contigannotfilename, header = TRUE, stringsAsFactors = TRUE) str(contigannot) #> 'data.frame':\t562 obs. of 2 variables: #> $ contig : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ path_class: Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... # Merging of both previous dataframes contigextendedres <- merge(x = contigres, y = contigannot, by.x = \"id\", by.y = \"contig\") # to see the structure of this dataframe str(contigextendedres) #> 'data.frame':\t562 obs. of 28 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... #> $ path_class : Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... ### Merge metabolomic and transcriptomic results extendedres <- rbind(metabextendedres, contigextendedres) extendedres$molecular.level <- factor(c(rep(\"metabolites\", nrow(metabextendedres)), rep(\"contigs\", nrow(contigextendedres)))) str(extendedres) #> 'data.frame':\t646 obs. of 29 variables: #> $ id : Factor w/ 478 levels \"NAP47_51\",\"NAP_2\",..: 1 2 3 4 4 4 4 5 6 7 ... #> $ irow : int 46 2 21 28 28 28 28 34 38 47 ... #> $ adjpvalue : num 7.16e-04 6.23e-05 1.11e-05 1.03e-05 1.03e-05 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 3 3 3 3 3 2 2 4 ... #> $ nbpar : int 2 3 2 2 2 2 2 3 3 5 ... #> $ b : num -0.056 0.4598 -0.0595 -0.0451 -0.0451 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 7.34 5.94 5.39 7.86 7.86 ... #> $ e : num NA -1.65 NA NA NA ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.1245 0.126 0.0793 0.052 0.052 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 7 7 7 7 7 2 2 9 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 3 3 3 3 1 ... #> $ y0 : num 7.34 5.94 5.39 7.86 7.86 ... #> $ yrange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ maxychange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 2.224 0.528 1.333 1.154 1.154 ... #> $ BMR.zSD : num 7.22 5.82 5.31 7.81 7.81 ... #> $ BMD.xfold : num NA NA NA NA NA ... #> $ BMR.xfold : num 6.61 5.35 4.85 7.07 7.07 ... #> $ BMD.zSD.lower : num 0.979 0.2 0.853 0.752 0.752 ... #> $ BMD.zSD.upper : num 4.07 1.11 1.75 1.46 1.46 ... #> $ BMD.xfold.lower : num Inf Inf 7.61 Inf Inf ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 1000 957 1000 1000 1000 1000 1000 648 620 872 ... #> $ path_class : Factor w/ 18 levels \"Amino acid metabolism\",..: 5 5 3 3 2 6 8 5 5 5 ... #> $ molecular.level : Factor w/ 2 levels \"contigs\",\"metabolites\": 2 2 2 2 2 2 2 2 2 2 ... # optional inverse alphabetic ordering of groups for the plot extendedres$path_class <- factor(extendedres$path_class, levels = sort(levels(extendedres$path_class), decreasing = TRUE)) ### (2.1) sensitivity plot of both molecular levels before and after selection of # most sensitive groups sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", colorby = \"molecular.level\", BMDsummary = \"first.quartile\") extendedres.2 <- selectgroups(extendedres, group = \"path_class\", explev = \"molecular.level\", BMDmax = 1, BMDtype = \"zSD\", BMDsummary = \"first.quartile\", nitemsmin = 1) sensitivityplot(extendedres.2, BMDtype = \"zSD\", group = \"path_class\", , colorby = \"molecular.level\", BMDsummary = \"first.quartile\") ### (2.2) same selection but keeping all the experimental as soon # as the selection criterion is met for at least one experimental level extendedres.3 <- selectgroups(extendedres, group = \"path_class\", explev = \"molecular.level\", BMDmax = 1, BMDtype = \"zSD\", BMDsummary = \"first.quartile\", nitemsmin = 1, keepallexplev = TRUE) extendedres.2 #> id irow adjpvalue model nbpar b c #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.0560055901 NA #> 2 NAP_2 2 6.232579e-05 exponential 3 0.4598124176 NA #> 5 NAP_30 28 1.028343e-05 linear 2 -0.0450783225 NA #> 6 NAP_30 28 1.028343e-05 linear 2 -0.0450783225 NA #> 7 NAP_30 28 1.028343e-05 linear 2 -0.0450783225 NA #> 8 NAP_38 34 1.885047e-03 exponential 3 0.6010677009 NA #> 9 NAP_42 38 4.160193e-03 exponential 3 0.6721022679 NA #> 10 NAP_52 47 3.920169e-02 log-Gauss-probit 5 0.4500858981 7.202003 #> 11 NAP_54 49 3.767103e-04 exponential 3 0.4520654041 NA #> 12 NAP_56 51 1.489919e-03 exponential 3 0.4392508278 NA #> 13 NAP_58 53 2.834198e-02 linear 2 -0.0193812708 NA #> 14 NAP_73 67 3.767103e-04 linear 2 -0.0578784221 NA #> 16 NP_121 197 9.889460e-03 linear 2 0.0614947494 NA #> 17 NP_121 197 9.889460e-03 linear 2 0.0614947494 NA #> 18 NP_129 204 7.286216e-03 exponential 3 0.0077860286 NA #> 19 NP_129 204 7.286216e-03 exponential 3 0.0077860286 NA #> 20 NP_129 204 7.286216e-03 exponential 3 0.0077860286 NA #> 22 NP_140 214 8.550044e-03 Gauss-probit 4 0.6955280524 4.835774 #> 23 NP_140 214 8.550044e-03 Gauss-probit 4 0.6955280524 4.835774 #> 25 NP_147 221 1.061967e-03 exponential 3 -0.0319538831 NA #> 26 NP_33 113 5.599308e-02 log-Gauss-probit 4 0.6150169995 4.917345 #> 27 NP_33 113 5.599308e-02 log-Gauss-probit 4 0.6150169995 4.917345 #> 28 NP_33 113 5.599308e-02 log-Gauss-probit 4 0.6150169995 4.917345 #> 29 NP_33 113 5.599308e-02 log-Gauss-probit 4 0.6150169995 4.917345 #> 30 NP_33 113 5.599308e-02 log-Gauss-probit 4 0.6150169995 4.917345 #> 31 NP_35 115 3.238559e-03 Gauss-probit 4 1.5075787740 5.975143 #> 32 NP_35 115 3.238559e-03 Gauss-probit 4 1.5075787740 5.975143 #> 33 NP_35 115 3.238559e-03 Gauss-probit 4 1.5075787740 5.975143 #> 34 NP_35 115 3.238559e-03 Gauss-probit 4 1.5075787740 5.975143 #> 35 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 36 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 37 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 39 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 40 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 41 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 42 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 43 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 44 NP_55 135 1.119380e-06 Gauss-probit 4 2.3252734893 4.469882 #> 46 NP_56 136 5.289310e-03 exponential 3 -0.0043605275 NA #> 47 NP_56 136 5.289310e-03 exponential 3 -0.0043605275 NA #> 48 NP_59 139 1.293440e-02 exponential 3 -0.3667815344 NA #> 49 NP_59 139 1.293440e-02 exponential 3 -0.3667815344 NA #> 50 NP_59 139 1.293440e-02 exponential 3 -0.3667815344 NA #> 51 NP_59 139 1.293440e-02 exponential 3 -0.3667815344 NA #> 52 NP_60 140 6.939560e-03 exponential 3 -0.5150005401 NA #> 53 NP_60 140 6.939560e-03 exponential 3 -0.5150005401 NA #> 54 NP_60 140 6.939560e-03 exponential 3 -0.5150005401 NA #> 55 NP_60 140 6.939560e-03 exponential 3 -0.5150005401 NA #> 56 NP_60 140 6.939560e-03 exponential 3 -0.5150005401 NA #> 57 NP_60 140 6.939560e-03 exponential 3 -0.5150005401 NA #> 58 NP_68 147 3.449660e-04 Gauss-probit 4 2.4449502970 5.055577 #> 59 NP_69 148 1.156302e-03 linear 2 -0.0507632397 NA #> 60 NP_69 148 1.156302e-03 linear 2 -0.0507632397 NA #> 61 NP_74 153 7.029527e-02 log-Gauss-probit 5 0.2715410382 4.847403 #> 62 NP_74 153 7.029527e-02 log-Gauss-probit 5 0.2715410382 4.847403 #> 63 NP_74 153 7.029527e-02 log-Gauss-probit 5 0.2715410382 4.847403 #> 64 NP_74 153 7.029527e-02 log-Gauss-probit 5 0.2715410382 4.847403 #> 65 NP_74 153 7.029527e-02 log-Gauss-probit 5 0.2715410382 4.847403 #> 67 NP_90 168 3.072572e-02 exponential 3 -0.2686241513 NA #> 68 NP_90 168 3.072572e-02 exponential 3 -0.2686241513 NA #> 69 NP_90 168 3.072572e-02 exponential 3 -0.2686241513 NA #> 70 NP_90 168 3.072572e-02 exponential 3 -0.2686241513 NA #> 71 NP_92 170 2.278810e-03 exponential 3 -0.0045956887 NA #> 72 NP_92 170 2.278810e-03 exponential 3 -0.0045956887 NA #> 73 NP_92 170 2.278810e-03 exponential 3 -0.0045956887 NA #> 75 NP_94 172 1.798752e-05 Gauss-probit 4 3.0165756601 4.862842 #> 76 NP_94 172 1.798752e-05 Gauss-probit 4 3.0165756601 4.862842 #> 77 NP_94 172 1.798752e-05 Gauss-probit 4 3.0165756601 4.862842 #> 79 NP_96 174 5.128859e-04 Gauss-probit 4 2.4099064659 5.216540 #> 80 NP_96 174 5.128859e-04 Gauss-probit 4 2.4099064659 5.216540 #> 81 NP_98 176 1.544866e-04 Gauss-probit 4 1.8803246284 5.908317 #> 82 NP_98 176 1.544866e-04 Gauss-probit 4 1.8803246284 5.908317 #> 83 NP_98 176 1.544866e-04 Gauss-probit 4 1.8803246284 5.908317 #> 84 NP_98 176 1.544866e-04 Gauss-probit 4 1.8803246284 5.908317 #> 86 c00276 39331 9.401685e-07 exponential 3 1.4994358863 NA #> 87 c00281 41217 2.894669e-06 exponential 3 1.4081722202 NA #> 103 c00973 8280 6.803372e-03 exponential 3 -0.7727846601 NA #> 116 c01318 18587 9.761656e-03 exponential 3 -0.0254133606 NA #> 121 c01442 22830 8.398538e-07 exponential 3 -0.4594973812 NA #> 123 c01449 23118 3.283441e-05 Gauss-probit 4 4.4613014130 3.681397 #> 125 c01613 28185 5.354723e-03 linear 2 0.0799120939 NA #> 131 c01645 29447 4.923790e-08 Gauss-probit 4 2.7016585216 3.159101 #> 134 c01924 38335 2.843216e-04 Gauss-probit 4 2.4465374667 11.894336 #> 150 c02837 53881 7.971049e-04 linear 2 0.1575210301 NA #> 157 c02964 54187 3.684145e-04 exponential 3 -1.6864714649 NA #> 161 c03088 54664 3.084821e-03 Gauss-probit 4 2.1488658006 11.596818 #> 169 c03232 55298 2.548714e-06 linear 2 0.2143540812 NA #> 172 c03284 55434 1.365366e-03 linear 2 -0.3672796068 NA #> 178 c03358 55610 1.361311e-03 Gauss-probit 4 2.7322361908 10.205586 #> 180 c03440 55810 2.679169e-03 linear 2 -0.1138782478 NA #> 181 c03440 55810 2.679169e-03 linear 2 -0.1138782478 NA #> 183 c03526 56019 2.223268e-04 exponential 3 -1.2492729382 NA #> 184 c03540 56053 3.430925e-03 linear 2 0.1965028128 NA #> 186 c03544 56063 6.386500e-03 exponential 3 -0.0014175944 NA #> 188 c03571 56127 9.413418e-03 log-Gauss-probit 4 0.5983299493 4.168006 #> 193 c03661 56344 7.587641e-03 log-Gauss-probit 4 0.6923539914 3.041593 #> 196 c03724 56499 4.297972e-05 log-Gauss-probit 4 0.5840913112 12.278610 #> 198 c03760 56585 9.777594e-03 linear 2 -0.1351427617 NA #> 202 c03784 56645 5.006652e-03 log-Gauss-probit 4 0.6898227977 9.654095 #> 228 c04342 58826 1.308910e-04 Gauss-probit 4 7.1974259415 7.723356 #> 233 c04434 59217 7.874407e-03 Gauss-probit 5 3.9082976255 2.896934 #> 235 c04513 59551 9.612303e-03 log-Gauss-probit 4 0.7506662658 11.849601 #> 238 c04553 59682 2.052935e-04 exponential 3 -0.0065668916 NA #> 243 c04613 59817 2.581335e-03 Gauss-probit 4 3.0320225840 11.908364 #> 245 c04619 59830 8.483282e-03 exponential 3 0.0232807480 NA #> 246 c04619 59830 8.483282e-03 exponential 3 0.0232807480 NA #> 252 c04647 59892 1.704160e-03 linear 2 0.0934490851 NA #> 253 c04655 59910 4.758216e-05 log-Gauss-probit 4 0.7297683681 11.552853 #> 256 c04683 59973 5.823021e-04 exponential 3 0.8026136129 NA #> 266 c04883 60416 7.479393e-03 exponential 3 1.7011616772 NA #> 275 c05081 60856 1.998628e-04 exponential 3 0.0440643594 NA #> 276 c05100 60898 4.685588e-05 exponential 3 1.6262517332 NA #> 291 c05326 247 4.812259e-03 linear 2 -0.1628213963 NA #> 293 c05358 382 7.443184e-03 exponential 3 0.0209061611 NA #> 301 c05581 1323 4.187799e-03 linear 2 0.1169616348 NA #> 305 c05641 1567 2.764636e-03 exponential 3 -0.0008701043 NA #> 306 c05645 1576 7.113588e-04 linear 2 -0.1348525089 NA #> 312 c05903 2148 4.374165e-03 log-Gauss-probit 4 0.7532079225 12.168583 #> 327 c06059 2495 1.623735e-03 exponential 3 0.9866188485 NA #> 338 c06208 2970 8.742721e-04 linear 2 0.1440100625 NA #> 341 c06313 3413 6.508671e-04 Gauss-probit 4 2.5239792304 6.359865 #> 344 c06518 3887 4.434225e-04 exponential 3 1.2826794141 NA #> 346 c06548 3976 1.118557e-04 linear 2 0.2267279513 NA #> 354 c06762 4630 1.129124e-03 exponential 3 0.0257922735 NA #> 361 c06881 4900 9.898720e-04 exponential 3 0.0003893310 NA #> 364 c06943 5037 4.856986e-04 linear 2 0.1205146652 NA #> 370 c07072 5328 9.285630e-04 exponential 3 1.7509076084 NA #> 372 c07118 5428 1.069214e-06 Gauss-probit 4 3.2010176397 13.816221 #> 375 c07206 5768 1.331320e-04 exponential 3 0.0115070482 NA #> 389 c07529 7138 7.515694e-04 exponential 3 0.0281112604 NA #> 399 c07859 8092 1.007072e-03 Gauss-probit 4 2.1459634733 7.693951 #> 405 c08131 8701 1.673911e-03 linear 2 0.1826551851 NA #> 408 c08241 8948 1.777483e-04 Gauss-probit 4 4.3060858426 12.750947 #> 419 c08466 9451 1.593456e-04 exponential 3 0.0008887870 NA #> 430 c08762 10368 1.641885e-03 exponential 3 0.0030933767 NA #> 445 c09125 11677 1.569172e-03 exponential 3 0.8284745591 NA #> 458 c09562 12942 4.373374e-03 exponential 3 -0.0021611489 NA #> 460 c09598 13030 2.362131e-03 linear 2 -0.2586441199 NA #> 475 c10057 14495 3.390469e-03 exponential 3 0.0241537891 NA #> 476 c10066 14533 1.347692e-03 exponential 3 -0.9389331704 NA #> 488 c10238 15302 1.228501e-03 exponential 3 -1.7937748737 NA #> 490 c10269 15440 7.296658e-05 exponential 3 1.5924749054 NA #> 492 c10302 15585 4.225116e-03 exponential 3 0.0066174702 NA #> 494 c10311 15626 1.016928e-03 log-Gauss-probit 4 0.7845650593 3.521660 #> 499 c10413 16075 3.498590e-04 linear 2 0.1135548342 NA #> 501 c10499 16461 1.256216e-07 Gauss-probit 4 2.0625479766 11.831280 #> 513 c10934 17597 5.753046e-03 exponential 3 0.0078914200 NA #> 519 c11233 18523 4.606893e-03 exponential 3 0.0193438159 NA #> 534 c11906 20969 9.654346e-04 Gauss-probit 4 2.2924767928 11.794843 #> 540 c12260 22081 8.245828e-06 exponential 3 0.1353700658 NA #> 549 c12576 23487 9.267247e-03 log-Gauss-probit 4 1.0213517059 4.443206 #> 551 c12705 23819 9.933717e-03 exponential 3 -1.7515673870 NA #> 552 c12781 24007 1.047802e-03 linear 2 0.4825734514 NA #> 553 c12927 24362 7.217499e-04 linear 2 0.3109087577 NA #> 554 c13186 25095 5.598806e-03 exponential 3 -0.0003355018 NA #> 567 c13542 26650 7.852988e-04 exponential 3 0.0026006455 NA #> 571 c13596 26891 2.014638e-03 Gauss-probit 4 2.0943452876 2.661827 #> 577 c13825 27530 7.036682e-03 Gauss-probit 4 2.3751722909 6.820887 #> 579 c14005 27954 6.155794e-03 linear 2 0.4216192438 NA #> 586 c14431 29501 2.619248e-03 log-Gauss-probit 4 0.7955319244 5.947486 #> 587 c14431 29501 2.619248e-03 log-Gauss-probit 4 0.7955319244 5.947486 #> 593 c15572 33143 7.075275e-03 exponential 3 0.0464367552 NA #> 597 c15942 34598 4.718990e-05 exponential 3 -0.0445067510 NA #> 604 c16973 37787 2.825599e-04 Gauss-probit 4 8.3191178064 -4.668448 #> 608 c17497 39284 3.028115e-04 exponential 3 -0.0023128307 NA #> 610 c17517 39327 2.461490e-03 exponential 3 0.0005997141 NA #> 611 c17694 39843 2.019227e-05 exponential 3 -1.8263643423 NA #> 614 c17823 40393 6.238172e-04 exponential 3 0.0006779690 NA #> 620 c18315 42046 3.162528e-03 exponential 3 0.0037074842 NA #> 630 c19738 46332 6.573236e-03 exponential 3 -0.0002344367 NA #> 641 c21327 51498 7.255831e-06 exponential 3 1.4896819388 NA #> 645 c21452 51752 7.810285e-07 exponential 3 1.4272338376 NA #> d e f SDres typology trend y0 #> 1 7.343571 NA NA 0.12454183 L.dec dec 7.343571 #> 2 5.941896 -1.6479584 NA 0.12604568 E.dec.convex dec 5.941896 #> 5 7.859109 NA NA 0.05203245 L.dec dec 7.859109 #> 6 7.859109 NA NA 0.05203245 L.dec dec 7.859109 #> 7 7.859109 NA NA 0.05203245 L.dec dec 7.859109 #> 8 6.857909 -0.3213163 NA 0.23376392 E.dec.convex dec 6.857909 #> 9 6.209286 -0.3230281 NA 0.28968463 E.dec.convex dec 6.209286 #> 10 7.288883 1.3087220 -0.1436781 0.07085857 lGP.U U 7.288883 #> 11 6.868523 -0.6254549 NA 0.15031166 E.dec.convex dec 6.868523 #> 12 7.558481 -0.2649798 NA 0.15353807 E.dec.convex dec 7.558481 #> 13 6.467466 NA NA 0.05769085 L.dec dec 6.467466 #> 14 5.738302 NA NA 0.11726837 L.dec dec 5.738302 #> 16 5.330171 NA NA 0.18706049 L.inc inc 5.330171 #> 17 5.330171 NA NA 0.18706049 L.inc inc 5.330171 #> 18 4.968475 2.0243885 NA 0.14303183 E.inc.convex inc 4.968475 #> 19 4.968475 2.0243885 NA 0.14303183 E.inc.convex inc 4.968475 #> 20 4.968475 2.0243885 NA 0.14303183 E.inc.convex inc 4.968475 #> 22 4.835774 1.4069463 0.2455556 0.10615565 GP.bell bell 4.867514 #> 23 4.835774 1.4069463 0.2455556 0.10615565 GP.bell bell 4.867514 #> 25 5.402328 3.3895616 NA 0.06744390 E.dec.concave dec 5.402328 #> 26 4.917345 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 27 4.917345 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 28 4.917345 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 29 4.917345 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 30 4.917345 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 31 5.975143 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 32 5.975143 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 33 5.975143 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 34 5.975143 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 35 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 36 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 37 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 39 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 40 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 41 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 42 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 43 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 44 4.469882 1.8678530 0.8450245 0.12256991 GP.bell bell 5.081883 #> 46 7.018764 1.9509846 NA 0.05587260 E.dec.concave dec 7.018764 #> 47 7.018764 1.9509846 NA 0.05587260 E.dec.concave dec 7.018764 #> 48 4.491695 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 49 4.491695 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 50 4.491695 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 51 4.491695 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 52 5.215709 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 53 5.215709 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 54 5.215709 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 55 5.215709 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 56 5.215709 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 57 5.215709 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 58 5.055577 2.6269663 -0.6208164 0.12476292 GP.U U 4.707014 #> 59 5.513855 NA NA 0.11224716 L.dec dec 5.513855 #> 60 5.513855 NA NA 0.11224716 L.dec dec 5.513855 #> 61 4.689787 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 62 4.689787 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 63 4.689787 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 64 4.689787 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 65 4.689787 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 67 6.070523 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 68 6.070523 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 69 6.070523 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 70 6.070523 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 71 6.489151 1.5921459 NA 0.17066885 E.dec.concave dec 6.489151 #> 72 6.489151 1.5921459 NA 0.17066885 E.dec.concave dec 6.489151 #> 73 6.489151 1.5921459 NA 0.17066885 E.dec.concave dec 6.489151 #> 75 4.862842 1.3892912 0.7365937 0.14844069 GP.bell bell 5.525316 #> 76 4.862842 1.3892912 0.7365937 0.14844069 GP.bell bell 5.525316 #> 77 4.862842 1.3892912 0.7365937 0.14844069 GP.bell bell 5.525316 #> 79 5.216540 1.9279453 0.8281597 0.22659347 GP.bell bell 5.817903 #> 80 5.216540 1.9279453 0.8281597 0.22659347 GP.bell bell 5.817903 #> 81 5.908317 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 82 5.908317 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 83 5.908317 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 84 5.908317 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 86 12.428212 -2.1982296 NA 0.28684892 E.dec.convex dec 12.428212 #> 87 12.411870 -2.4052289 NA 0.28115971 E.dec.convex dec 12.411870 #> 103 7.236270 -2.3752014 NA 0.30192346 E.inc.concave inc 7.236270 #> 116 9.300591 1.9823323 NA 0.31949144 E.dec.concave dec 9.300591 #> 121 13.082606 5.0536325 NA 0.21425367 E.dec.concave dec 13.082606 #> 123 3.681397 2.5679456 7.7390108 0.55445078 GP.bell bell 10.238924 #> 125 10.764018 NA NA 0.23146795 L.inc inc 10.764018 #> 131 3.159101 1.7419187 3.3237027 0.41149782 GP.bell bell 5.859022 #> 134 11.894336 2.4191926 -1.3218368 0.24812481 GP.U U 11.083641 #> 150 5.662624 NA NA 0.32569343 L.inc inc 5.662624 #> 157 8.068158 -1.5870424 NA 0.57855035 E.inc.concave inc 8.068158 #> 161 11.596818 1.8275480 -0.8682280 0.29736729 GP.U U 10.992074 #> 169 8.329304 NA NA 0.31094519 L.inc inc 8.329304 #> 172 9.111936 NA NA 0.84138808 L.dec dec 9.111936 #> 178 10.205586 2.8555099 2.3244195 0.41255116 GP.bell bell 11.551851 #> 180 13.959092 NA NA 0.26918435 L.dec dec 13.959092 #> 181 13.959092 NA NA 0.26918435 L.dec dec 13.959092 #> 183 6.736177 -0.6498650 NA 0.38185313 E.inc.concave inc 6.736177 #> 184 6.408554 NA NA 0.52767310 L.inc inc 6.408554 #> 186 8.901037 1.0237053 NA 0.37429011 E.dec.concave dec 8.901037 #> 188 4.168006 2.3167014 1.3407136 0.64489184 lGP.bell bell 4.168006 #> 193 3.041593 1.8809478 1.7942520 0.87754767 lGP.bell bell 3.041593 #> 196 12.278610 1.7372724 -2.0216456 0.53171016 lGP.U U 12.278610 #> 198 10.350443 NA NA 0.39026969 L.dec dec 10.350443 #> 202 9.654095 1.9560222 -2.1370159 1.14939830 lGP.U U 9.654095 #> 228 7.723356 2.5397977 5.2069816 0.18596550 GP.bell bell 12.616033 #> 233 -2.630433 0.0000000 3.7693124 0.45467691 GP.bell bell 3.902563 #> 235 11.849601 1.9585039 0.8349853 0.42763624 lGP.bell bell 11.849601 #> 238 15.751380 1.2089428 NA 0.43153316 E.dec.concave dec 15.751380 #> 243 11.908364 2.7394740 1.2475547 0.17108221 GP.bell bell 12.737821 #> 245 8.309831 1.7832021 NA 0.47444618 E.inc.convex inc 8.309831 #> 246 8.309831 1.7832021 NA 0.47444618 E.inc.convex inc 8.309831 #> 252 17.926717 NA NA 0.19130163 L.inc inc 17.926717 #> 253 11.552853 1.7586717 0.9857557 0.27811862 lGP.bell bell 11.552853 #> 256 11.273898 -2.4815554 NA 0.24110024 E.dec.convex dec 11.273898 #> 266 5.055152 -0.5889109 NA 0.72440497 E.dec.convex dec 5.055152 #> 275 11.097343 2.0754574 NA 0.27234802 E.inc.convex inc 11.097343 #> 276 6.963048 -2.0722462 NA 0.44005180 E.dec.convex dec 6.963048 #> 291 4.628540 NA NA 0.44374643 L.dec dec 4.628540 #> 293 9.824353 1.9682607 NA 0.21543421 E.inc.convex inc 9.824353 #> 301 12.582904 NA NA 0.28459726 L.inc inc 12.582904 #> 305 12.978673 0.9062525 NA 0.48389149 E.dec.concave dec 12.978673 #> 306 12.920552 NA NA 0.29030980 L.dec dec 12.920552 #> 312 12.168583 2.0515078 0.7474307 0.31596860 lGP.bell bell 12.168583 #> 327 4.946831 -0.7537935 NA 0.38646039 E.dec.convex dec 4.946831 #> 338 10.397791 NA NA 0.31334720 L.inc inc 10.397791 #> 341 6.359865 2.1273579 -4.1079331 1.11545022 GP.U U 3.480083 #> 344 10.429183 -2.6796044 NA 0.37877170 E.dec.convex dec 10.429183 #> 346 9.672700 NA NA 0.45436857 L.inc inc 9.672700 #> 354 5.609934 1.8216559 NA 0.32521096 E.inc.convex inc 5.609934 #> 361 9.377940 0.8420820 NA 0.38873834 E.inc.convex inc 9.377940 #> 364 10.014326 NA NA 0.27962037 L.inc inc 10.014326 #> 370 6.118400 -2.3714972 NA 0.59801495 E.dec.convex dec 6.118400 #> 372 13.816221 1.9596785 -3.2185534 0.39523662 GP.U U 11.147673 #> 375 11.805578 1.3537714 NA 0.50658959 E.inc.convex inc 11.805578 #> 389 5.114821 1.4084038 NA 1.09855421 E.inc.convex inc 5.114821 #> 399 7.693951 1.5471002 0.6661478 0.19149575 GP.bell bell 8.207650 #> 405 4.872178 NA NA 0.43574679 L.inc inc 4.872178 #> 408 12.750947 2.2781947 -2.6699053 0.24834756 GP.U U 10.429737 #> 419 8.322289 0.8805994 NA 0.48194682 E.inc.convex inc 8.322289 #> 430 7.540803 1.2173378 NA 0.27550558 E.inc.convex inc 7.540803 #> 445 12.928749 -1.3776799 NA 0.29718879 E.dec.convex dec 12.928749 #> 458 9.916022 1.0820381 NA 0.43570777 E.dec.concave dec 9.916022 #> 460 6.195518 NA NA 0.64907510 L.dec dec 6.195518 #> 475 12.738419 1.6671166 NA 0.51466168 E.inc.convex inc 12.738419 #> 476 7.627804 -2.0128076 NA 0.32054437 E.inc.concave inc 7.627804 #> 488 4.475119 -1.3700499 NA 0.65855748 E.inc.concave inc 4.475119 #> 490 8.307995 -2.3133165 NA 0.42499412 E.dec.convex dec 8.307995 #> 492 10.373727 1.3213539 NA 0.46734668 E.inc.convex inc 10.373727 #> 494 3.521660 1.5925756 1.6525193 0.71149572 lGP.bell bell 3.521660 #> 499 8.667552 NA NA 0.23785120 L.inc inc 8.667552 #> 501 11.831280 2.0127634 -2.1716042 0.35429372 GP.U U 10.482349 #> 513 2.856807 1.2403251 NA 0.72920067 E.inc.convex inc 2.856807 #> 519 11.872955 1.6928649 NA 0.36955986 E.inc.convex inc 11.872955 #> 534 11.794843 1.8803822 -1.7229567 0.53837776 GP.U U 10.564068 #> 540 4.828430 2.0830008 NA 0.73289707 E.inc.convex inc 4.828430 #> 549 4.443206 1.5956017 -2.0801672 1.01087060 lGP.U U 4.443206 #> 551 3.922000 -1.4167347 NA 0.80658748 E.inc.concave inc 3.922000 #> 552 2.983728 NA NA 1.23243335 L.inc inc 2.983728 #> 553 3.293896 NA NA 0.78506590 L.inc inc 3.293896 #> 554 4.782034 0.7862608 NA 0.62138156 E.dec.concave dec 4.782034 #> 567 4.100345 0.9665863 NA 0.83033880 E.inc.convex inc 4.100345 #> 571 2.661827 1.8081921 1.5558322 0.59126123 GP.bell bell 3.733593 #> 577 6.820887 2.2045847 -1.9822962 0.65642859 GP.U U 5.532363 #> 579 2.353068 NA NA 1.29727813 L.inc inc 2.353068 #> 586 5.947486 2.1464196 -1.2818324 0.48811443 lGP.U U 5.947486 #> 587 5.947486 2.1464196 -1.2818324 0.48811443 lGP.U U 5.947486 #> 593 2.709461 1.8595387 NA 0.73835019 E.inc.convex inc 2.709461 #> 597 5.266716 1.7742492 NA 0.45397783 E.dec.concave dec 5.266716 #> 604 -4.668448 2.7306565 14.1086676 0.29870979 GP.bell bell 8.700290 #> 608 7.176469 1.0238480 NA 0.42350852 E.dec.concave dec 7.176469 #> 610 2.783684 0.8429907 NA 0.60237813 E.inc.convex inc 2.783684 #> 611 8.797682 -1.8935670 NA 0.47334309 E.inc.concave inc 8.797682 #> 614 9.580443 0.9456406 NA 0.19945603 E.inc.convex inc 9.580443 #> 620 2.604185 1.0531930 NA 0.75747748 E.inc.convex inc 2.604185 #> 630 8.262534 0.7222172 NA 0.96123411 E.dec.concave dec 8.262534 #> 641 13.349535 -1.8926198 NA 0.34219661 E.dec.convex dec 13.349535 #> 645 12.344658 -2.4650317 NA 0.25729560 E.dec.convex dec 12.344658 #> yrange maxychange xextrem yextrem BMD.zSD BMR.zSD BMD.xfold #> 1 0.4346034 0.4346034 NA NA 2.2237393 7.219029 NA #> 2 0.4556672 0.4556672 NA NA 0.5279668 5.815850 NA #> 5 0.3498078 0.3498078 NA NA 1.1542677 7.807077 NA #> 6 0.3498078 0.3498078 NA NA 1.1542677 7.807077 NA #> 7 0.3498078 0.3498078 NA NA 1.1542677 7.807077 NA #> 8 0.6010677 0.6010677 NA NA 0.1582542 6.624146 NA #> 9 0.6721023 0.6721023 NA NA 0.1821546 5.919602 0.8318574 #> 10 0.1912790 0.1912790 1.4588204 7.097604 0.7315304 7.218025 NA #> 11 0.4520636 0.4520636 NA NA 0.2528186 6.718211 NA #> 12 0.4392508 0.4392508 NA NA 0.1139635 7.404943 NA #> 13 0.1503987 0.1503987 NA NA 2.9766289 6.409775 NA #> 14 0.4491366 0.4491366 NA NA 2.0261156 5.621033 NA #> 16 0.4771993 0.4771993 NA NA 3.0418937 5.517232 NA #> 17 0.4771993 0.4771993 NA NA 3.0418937 5.517232 NA #> 18 0.3520280 0.3520280 NA NA 5.9997652 5.111507 NA #> 19 0.3520280 0.3520280 NA NA 5.9997652 5.111507 NA #> 20 0.3520280 0.3520280 NA NA 5.9997652 5.111507 NA #> 22 0.2455556 0.2138158 1.4069463 5.081330 0.6597819 4.973670 NA #> 23 0.2455556 0.2138158 1.4069463 5.081330 0.6597819 4.973670 NA #> 25 0.2833937 0.2833937 NA NA 3.8465968 5.334884 NA #> 26 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 27 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 28 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 29 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 30 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 31 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 32 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 33 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 34 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 35 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 36 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 37 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 39 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 40 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 41 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 42 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 43 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 44 0.8109383 0.5779147 1.8678530 5.314907 0.6370853 5.204453 6.6295891 #> 46 0.2284144 0.2284144 NA NA 5.1225625 6.962891 NA #> 47 0.2284144 0.2284144 NA NA 5.1225625 6.962891 NA #> 48 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 49 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 50 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 51 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 52 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 53 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 54 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 55 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 56 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 57 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 58 0.5522909 0.2800374 2.6269663 4.434761 0.8261462 4.582251 NA #> 59 0.3939227 0.3939227 NA NA 2.2111898 5.401608 NA #> 60 0.3939227 0.3939227 NA NA 2.2111898 5.401608 NA #> 61 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 62 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 63 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 64 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 65 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 67 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 68 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 69 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 70 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 71 0.5966741 0.5966741 NA NA 5.7972864 6.318483 NA #> 72 0.5966741 0.5966741 NA NA 5.7972864 6.318483 NA #> 73 0.5966741 0.5966741 NA NA 5.7972864 6.318483 NA #> 75 0.6573939 0.5832748 1.3892912 5.599435 3.9480418 5.376875 7.2728900 #> 76 0.6573939 0.5832748 1.3892912 5.599435 3.9480418 5.376875 7.2728900 #> 77 0.6573939 0.5832748 1.3892912 5.599435 3.9480418 5.376875 7.2728900 #> 79 0.7838623 0.5570660 1.9279453 6.044699 1.8746151 6.044497 NA #> 80 0.7838623 0.5570660 1.9279453 6.044699 1.8746151 6.044497 NA #> 81 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 82 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 83 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 84 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 86 1.4260064 1.4260064 NA NA 0.4667565 12.141363 3.8804658 #> 87 1.3187707 1.3187707 NA NA 0.5356979 12.130711 5.1282913 #> 103 0.7254029 0.7254029 NA NA 1.1767628 7.538193 6.5436013 #> 116 0.6953599 0.6953599 NA NA 5.1699097 8.981100 NA #> 121 1.2471032 1.2471032 NA NA 1.9341638 12.868353 NA #> 123 2.6271853 1.4457016 2.5679456 11.420407 0.7339797 10.793374 1.6629870 #> 125 0.5298971 0.5298971 NA NA 2.8965322 10.995486 NA #> 131 2.6773137 2.0535315 1.7419187 6.482804 0.7603447 6.270519 1.3328628 #> 134 1.0214920 0.5111417 2.4191926 10.572500 0.7894809 10.835516 NA #> 150 1.0445220 1.0445220 NA NA 2.0676187 5.988317 3.5948366 #> 157 1.6606248 1.6606248 NA NA 0.6668007 8.646709 1.0329475 #> 161 0.7968439 0.5333591 1.8275480 10.728590 4.9242758 11.289442 NA #> 169 1.4213819 1.4213819 NA NA 1.4506148 8.640249 3.8857687 #> 172 2.4354311 2.4354311 NA NA 2.2908652 8.270548 2.4809261 #> 178 1.4297124 0.9781543 2.8555099 12.530005 0.8151722 11.964402 NA #> 180 0.7551267 0.7551267 NA NA 2.3637907 13.689907 NA #> 181 0.7551267 0.7551267 NA NA 2.3637907 13.689907 NA #> 183 1.2492267 1.2492267 NA NA 0.2370668 7.118030 0.5035207 #> 184 1.3030102 1.3030102 NA NA 2.6853208 6.936227 3.2613039 #> 186 0.9204590 0.9204590 NA NA 5.7121222 8.526747 6.5967242 #> 188 1.3407136 1.3407136 2.3167014 5.508719 1.1232833 4.812898 0.9282272 #> 193 1.7942520 1.7942520 1.8809478 4.835845 0.8218053 3.919141 0.5103535 #> 196 2.0216456 2.0216456 1.7372724 10.256965 0.6687799 11.746900 0.9695193 #> 198 0.8961317 0.8961317 NA NA 2.8878327 9.960173 NA #> 202 2.1370159 2.1370159 1.9560222 7.517079 0.9072409 8.504697 0.8197933 #> 228 0.7767741 0.4624698 2.5397977 12.930338 0.9318105 12.801999 NA #> 233 0.9872534 0.6272225 2.2864089 4.529785 1.0599593 4.357240 0.8573274 #> 235 0.8349853 0.8349853 1.9585039 12.684587 0.8218319 12.277238 NA #> 238 1.5763081 1.5763081 NA NA 5.0780515 15.319847 6.6301060 #> 243 0.7000964 0.4180970 2.7394740 13.155918 0.7253125 12.908904 NA #> 245 0.9360331 0.9360331 NA NA 5.4609223 8.784277 6.4241875 #> 246 0.9360331 0.9360331 NA NA 5.4609223 8.784277 6.4241875 #> 252 0.6196609 0.6196609 NA NA 2.0471215 18.118018 NA #> 253 0.9857557 0.9857557 1.7586717 12.538608 0.5508093 11.830971 NA #> 256 0.7471482 0.7471482 NA NA 0.8865055 11.032798 NA #> 266 1.7011398 1.7011398 NA NA 0.3267449 4.330747 0.2076643 #> 275 1.0315095 1.0315095 NA NA 4.0915465 11.369691 NA #> 276 1.5599561 1.5599561 NA NA 0.6538412 6.522996 1.1581900 #> 291 1.0796687 1.0796687 NA NA 2.7253570 4.184793 2.8427097 #> 293 0.5863849 0.5863849 NA NA 4.7734831 10.039788 NA #> 301 0.7755726 0.7755726 NA NA 2.4332531 12.867502 NA #> 305 1.3091568 1.3091568 NA NA 5.7300529 12.494781 6.6231563 #> 306 0.8942070 0.8942070 NA NA 2.1527950 12.630242 NA #> 312 0.7474307 0.7474307 2.0515078 12.916014 0.7635263 12.484552 NA #> 327 0.9864697 0.9864697 NA NA 0.3747033 4.560371 0.5245918 #> 338 0.9549307 0.9549307 NA NA 2.1758702 10.711139 NA #> 341 3.2718424 2.0436913 2.1273579 2.251932 1.5320368 2.364633 0.3746104 #> 344 1.1746841 1.1746841 NA NA 0.9378058 10.050411 4.4938667 #> 346 1.5034330 1.5034330 NA NA 2.0040254 10.127068 4.2662140 #> 354 0.9567759 0.9567759 NA NA 4.7558343 5.935145 5.6919180 #> 361 1.0233370 1.0233370 NA NA 5.8164566 9.766679 6.5575207 #> 364 0.7991327 0.7991327 NA NA 2.3202186 10.293947 NA #> 370 1.6440213 1.6440213 NA NA 0.9909542 5.520385 1.0195641 #> 372 2.1088211 1.5588160 1.9596785 10.597668 0.9547622 10.752436 5.8228192 #> 375 1.5309438 1.5309438 NA NA 5.1540813 12.312167 6.2821526 #> 389 3.0879871 3.0879871 NA NA 5.1982036 6.213375 4.1613342 #> 399 0.6258898 0.4734413 1.5471002 8.360099 4.1335653 8.016154 NA #> 405 1.2111865 1.2111865 NA NA 2.3856251 5.307925 2.6674183 #> 408 1.0680948 0.7193997 2.2781947 10.081042 1.0862987 10.181390 NA #> 419 1.6551785 1.6551785 NA NA 5.5456395 8.804236 6.0260053 #> 430 0.7148534 0.7148534 NA NA 5.4786411 7.816309 NA #> 445 0.8217456 0.8217456 NA NA 0.6120840 12.631560 NA #> 458 0.9890126 0.9890126 NA NA 5.7470070 9.480314 NA #> 460 1.7150692 1.7150692 NA NA 2.5095297 5.546443 2.3953832 #> 475 1.2652916 1.2652916 NA NA 5.1762835 13.253081 NA #> 476 0.9041080 0.9041080 NA NA 0.8406026 7.948348 3.3682176 #> 488 1.7795910 1.7795910 NA NA 0.6267952 5.133676 0.3931901 #> 490 1.5018619 1.5018619 NA NA 0.7181485 7.883001 1.7061269 #> 492 0.9936782 0.9936782 NA NA 5.6440554 10.841074 NA #> 494 1.6525193 1.6525193 1.5925756 5.174180 0.5751369 4.233156 0.4008074 #> 499 0.7529821 0.7529821 NA NA 2.0945933 8.905403 NA #> 501 1.9945506 1.1718772 2.0127634 9.659675 0.5750563 10.128055 6.1142017 #> 513 1.6477686 1.6477686 NA NA 5.6272766 3.586008 4.4854464 #> 519 0.9527003 0.9527003 NA NA 5.0802181 12.242515 NA #> 534 1.5216846 1.0295037 1.8803822 10.071887 4.9758852 11.102445 NA #> 540 3.1308885 3.1308885 NA NA 3.8712302 5.561327 3.1637050 #> 549 2.0801672 2.0801672 1.5956017 2.363039 0.4677758 3.432336 0.2651867 #> 551 1.7353224 1.7353224 NA NA 0.8742707 4.728587 0.3591303 #> 552 3.1999446 3.1999446 NA NA 2.5538772 4.216161 0.6182950 #> 553 2.0616360 2.0616360 NA NA 2.5250685 4.078962 1.0594414 #> 554 1.5426292 1.5426292 NA NA 5.9163079 4.160652 5.7105061 #> 567 2.4773277 2.4773277 NA NA 5.5764311 4.930684 4.8975036 #> 571 1.4460633 0.9619972 1.8081921 4.217660 5.0186649 3.142332 1.0034930 #> 577 1.6331559 0.9393843 2.2045847 4.838591 1.7413609 4.875934 1.2937236 #> 579 2.7957572 2.7957572 NA NA 3.0768950 3.650346 0.5581026 #> 586 1.2818324 1.2818324 2.1464196 4.665653 0.7105856 5.459371 0.8008469 #> 587 1.2818324 1.2818324 2.1464196 4.665653 0.7105856 5.459371 0.8008469 #> 593 1.5961529 1.5961529 NA NA 5.2575129 3.447811 3.5740658 #> 597 1.8241896 1.8241896 NA NA 4.2864651 4.812738 4.5279917 #> 604 1.4684527 0.7399295 2.7306565 9.440219 0.6335929 8.998999 NA #> 608 1.5003885 1.5003885 NA NA 5.3399279 6.752960 5.8776276 #> 610 1.5629901 1.5629901 NA NA 5.8277486 3.386062 5.1779858 #> 611 1.7713152 1.7713152 NA NA 0.5680459 9.271025 1.2444712 #> 614 0.7519149 0.7519149 NA NA 5.3784638 9.779899 NA #> 620 2.0074095 2.0074095 NA NA 5.6077502 3.361662 4.4929978 #> 630 2.2773688 2.2773688 NA NA 6.0081475 7.301299 5.8988929 #> 641 1.4448595 1.4448595 NA NA 0.4939544 13.007338 4.2861144 #> 645 1.3303544 1.3303544 NA NA 0.4900168 12.087363 4.9350083 #> BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper #> 1 6.609214 0.97850954 4.0686985 Inf Inf #> 2 5.347706 0.20008806 1.1095586 Inf Inf #> 5 7.073198 0.75185882 1.4649978 Inf Inf #> 6 7.073198 0.75185882 1.4649978 Inf Inf #> 7 7.073198 0.75185882 1.4649978 Inf Inf #> 8 6.172119 0.05543773 0.6804425 0.56115437 Inf #> 9 5.588358 0.08095270 0.7936032 0.32929317 Inf #> 10 8.017772 0.42468408 1.0520363 Inf Inf #> 11 6.181671 0.07579775 0.7005182 Inf Inf #> 12 6.802633 0.03694799 0.4209217 Inf Inf #> 13 5.820719 1.67433198 5.3037292 Inf Inf #> 14 5.164472 1.25236329 2.8522870 7.56375893 Inf #> 16 5.863188 1.32631865 6.0595553 5.92523484 Inf #> 17 5.863188 1.32631865 6.0595553 5.92523484 Inf #> 18 5.465323 2.76071129 7.1933590 7.67906625 Inf #> 19 5.465323 2.76071129 7.1933590 7.67906625 Inf #> 20 5.465323 2.76071129 7.1933590 7.67906625 Inf #> 22 4.380763 0.36442365 2.2863213 Inf Inf #> 23 4.380763 0.36442365 2.2863213 Inf Inf #> 25 4.862095 1.87728956 5.7928776 Inf Inf #> 26 4.425610 0.63687870 2.6421628 1.60732079 Inf #> 27 4.425610 0.63687870 2.6421628 1.60732079 Inf #> 28 4.425610 0.63687870 2.6421628 1.60732079 Inf #> 29 4.425610 0.63687870 2.6421628 1.60732079 Inf #> 30 4.425610 0.63687870 2.6421628 1.60732079 Inf #> 31 5.860923 0.37773418 3.4329037 2.12091124 Inf #> 32 5.860923 0.37773418 3.4329037 2.12091124 Inf #> 33 5.860923 0.37773418 3.4329037 2.12091124 Inf #> 34 5.860923 0.37773418 3.4329037 2.12091124 Inf #> 35 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 36 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 37 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 39 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 40 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 41 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 42 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 43 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 44 4.573695 0.32686514 3.1332891 5.73891269 Inf #> 46 6.316887 2.43380228 6.3223929 Inf Inf #> 47 6.316887 2.43380228 6.3223929 Inf Inf #> 48 4.940865 0.35799216 4.2882933 2.32048362 Inf #> 49 4.940865 0.35799216 4.2882933 2.32048362 Inf #> 50 4.940865 0.35799216 4.2882933 2.32048362 Inf #> 51 4.940865 0.35799216 4.2882933 2.32048362 Inf #> 52 5.737279 0.30086446 3.1833272 1.30741047 Inf #> 53 5.737279 0.30086446 3.1833272 1.30741047 Inf #> 54 5.737279 0.30086446 3.1833272 1.30741047 Inf #> 55 5.737279 0.30086446 3.1833272 1.30741047 Inf #> 56 5.737279 0.30086446 3.1833272 1.30741047 Inf #> 57 5.737279 0.30086446 3.1833272 1.30741047 Inf #> 58 5.177716 0.46124416 1.4873687 Inf Inf #> 59 4.962470 1.03576992 3.6179409 Inf Inf #> 60 4.962470 1.03576992 3.6179409 Inf Inf #> 61 5.158766 0.48406738 1.2370211 0.80809797 Inf #> 62 5.158766 0.48406738 1.2370211 0.80809797 Inf #> 63 5.158766 0.48406738 1.2370211 0.80809797 Inf #> 64 5.158766 0.48406738 1.2370211 0.80809797 Inf #> 65 5.158766 0.48406738 1.2370211 0.80809797 Inf #> 67 6.677576 0.26644454 4.2871164 Inf Inf #> 68 6.677576 0.26644454 4.2871164 Inf Inf #> 69 6.677576 0.26644454 4.2871164 Inf Inf #> 70 6.677576 0.26644454 4.2871164 Inf Inf #> 71 5.840236 3.09334793 6.6162300 7.32335336 Inf #> 72 5.840236 3.09334793 6.6162300 7.32335336 Inf #> 73 5.840236 3.09334793 6.6162300 7.32335336 Inf #> 75 4.972785 0.63064808 4.7996149 5.94571062 Inf #> 76 4.972785 0.63064808 4.7996149 5.94571062 Inf #> 77 4.972785 0.63064808 4.7996149 5.94571062 Inf #> 79 5.236113 0.52106128 5.9739107 5.91975161 Inf #> 80 5.236113 0.52106128 5.9739107 5.91975161 Inf #> 81 5.980553 0.42901273 4.3080978 5.24829289 Inf #> 82 5.980553 0.42901273 4.3080978 5.24829289 Inf #> 83 5.980553 0.42901273 4.3080978 5.24829289 Inf #> 84 5.980553 0.42901273 4.3080978 5.24829289 Inf #> 86 11.185391 0.24296501 0.8254193 2.32365503 Inf #> 87 11.170683 0.28244583 0.9252443 2.78927718 Inf #> 103 7.959897 0.46760570 2.8942164 1.94401632 Inf #> 116 8.370532 2.62074447 6.1892385 6.39907958 Inf #> 121 11.774346 1.07799896 3.2324908 6.13410907 Inf #> 123 11.262816 0.39650460 1.0210325 0.93588481 6.2063487 #> 125 11.840420 1.64980574 4.7578654 Inf Inf #> 131 6.444924 0.37828212 3.9147635 0.64172080 4.6921648 #> 134 12.192005 0.45119793 1.3369379 Inf Inf #> 150 6.228886 1.30775683 3.0619063 2.63321348 5.1889529 #> 157 8.874974 0.27525816 1.6705374 0.44800424 2.7783783 #> 161 12.091282 0.60384236 5.6978525 Inf Inf #> 169 9.162234 0.96799087 1.9783744 3.14745914 5.1690481 #> 172 8.200742 1.50525257 3.4011695 1.95515289 3.7737373 #> 178 10.396666 0.53046359 1.1464625 1.68379792 Inf #> 180 12.563182 1.54875481 3.5028687 Inf Inf #> 181 12.563182 1.54875481 3.5028687 Inf Inf #> 183 7.409795 0.08183997 0.5980861 0.19125061 1.6257035 #> 184 7.049409 1.75885065 4.3783442 2.22896204 5.6185328 #> 186 8.010933 2.93461899 6.0719877 5.69970451 Inf #> 188 4.584806 0.50278548 1.8172104 0.39883826 1.5901895 #> 193 3.345752 0.35654678 1.4592305 0.16656157 1.1565046 #> 196 11.050749 0.43301683 0.8971572 0.74039512 1.2380754 #> 198 9.315398 1.73688931 5.1197523 5.38884008 Inf #> 202 8.688686 0.35183367 1.7316828 0.36262819 1.4953577 #> 228 11.354430 0.48380870 1.5400128 Inf Inf #> 233 4.292819 0.40044672 6.5720202 0.37251622 5.9756156 #> 235 10.664641 0.31820753 1.5828158 1.30158571 Inf #> 238 14.176242 2.74132244 5.6217506 6.05963970 Inf #> 243 11.464039 0.45259236 0.9852054 Inf Inf #> 245 9.140814 2.60802191 6.3443547 4.77393169 Inf #> 246 9.140814 2.60802191 6.3443547 4.77393169 Inf #> 252 19.719388 1.32526215 3.0171899 Inf Inf #> 253 10.397567 0.27487668 0.8684603 1.20202041 Inf #> 256 10.146508 0.34554032 1.8031346 Inf Inf #> 266 4.549637 0.11229115 1.1562790 0.08920198 0.6082146 #> 275 12.207078 2.02820057 5.2593819 6.17057748 Inf #> 276 6.266743 0.27957255 1.4053942 0.62385724 2.4264491 #> 291 4.165686 1.61304816 4.7440724 2.05493380 4.8155223 #> 293 10.806789 2.28426943 5.7598313 Inf Inf #> 301 13.841195 1.57349911 3.9836203 Inf Inf #> 305 11.680806 2.74748436 5.9480926 5.95909053 Inf #> 306 11.628496 1.31584676 3.3142276 Inf Inf #> 312 10.951725 0.32210163 1.4464539 Inf Inf #> 327 4.452148 0.11967646 1.0401929 0.19534852 1.6577848 #> 338 11.437571 1.50483906 3.0487558 5.43599051 Inf #> 341 3.132075 0.55429742 5.7718558 0.24775913 0.9522847 #> 344 9.386265 0.41050756 2.0533236 2.06051125 Inf #> 346 10.639970 1.12797148 3.0911075 3.10061531 6.0301429 #> 354 6.170927 2.27138538 5.7358531 3.72920786 6.3344473 #> 361 10.315734 2.77164549 6.0800988 5.52360080 Inf #> 364 11.015759 1.35631407 3.9482498 6.11649532 Inf #> 370 5.506560 0.38959091 2.2272543 0.50373257 2.2560210 #> 372 12.262440 0.43168938 4.3245839 5.06418512 Inf #> 375 12.986136 2.72596759 5.7854763 5.14744071 Inf #> 389 5.626303 2.38471036 5.6950050 1.25212089 5.0100759 #> 399 7.386885 0.58678810 4.9024458 Inf Inf #> 405 5.359396 1.48802589 3.7982782 1.85410611 4.4410965 #> 408 11.472711 0.49209232 4.7797975 Inf Inf #> 419 9.154518 3.01643160 5.7582422 4.45466791 6.2076045 #> 430 8.294884 3.11003882 5.9115909 5.95974847 Inf #> 445 11.635874 0.24885181 1.7730190 Inf Inf #> 458 8.924420 2.95397705 6.1224920 5.86209386 Inf #> 460 5.575966 1.51555559 4.1284671 1.76309457 3.8398589 #> 475 14.012261 2.57284026 6.0635216 5.59601377 Inf #> 476 8.390585 0.35181993 1.9120592 1.37283122 Inf #> 488 4.922630 0.20461711 1.6845815 0.13994566 1.2568975 #> 490 7.477196 0.32356830 1.4338426 0.97175675 3.0973018 #> 492 11.411100 2.93033518 6.1810802 5.55823991 Inf #> 494 3.873826 0.18220941 1.0969609 0.09169953 0.8486895 #> 499 9.534307 1.36558810 3.0277098 5.76250310 Inf #> 501 11.530584 0.33731937 0.9197975 1.50805897 Inf #> 513 3.142488 2.82669704 6.1106136 1.28010249 5.2509376 #> 519 13.060251 2.75999567 5.7929246 6.33809779 Inf #> 534 11.620474 0.57587075 5.6502320 1.73266615 Inf #> 540 5.311273 2.09802632 5.1109137 1.45839343 4.7290176 #> 549 3.998886 0.05399315 1.2442471 0.01432065 0.6768693 #> 551 4.314200 0.27996824 2.9680621 0.12373376 1.4896918 #> 552 3.282101 1.41586955 4.2784519 0.34784795 1.0906435 #> 553 3.623286 1.47301680 4.2390445 0.68741986 1.8230891 #> 554 4.303831 3.07905725 6.0763029 2.72472787 5.8528389 #> 567 4.510380 3.04662568 5.8599969 1.79850499 5.3875293 #> 571 4.106953 0.64467381 5.9696696 0.44860443 4.9243516 #> 577 4.979126 0.65520412 6.2161646 0.68872614 5.5777863 #> 579 2.588375 1.71979781 5.8334366 0.29269724 1.3384842 #> 586 5.352737 0.31331655 1.2981207 0.45448294 1.4204213 #> 587 5.352737 0.31331655 1.2981207 0.45448294 1.4204213 #> 593 2.980407 2.37205969 6.2112758 1.06163514 5.0651393 #> 597 4.740045 2.16859102 5.3652514 2.72959715 5.5368126 #> 604 7.830261 0.38533255 0.8633687 1.66508242 Inf #> 608 6.458822 2.75629900 5.7006541 4.24574070 6.1784483 #> 610 3.062052 3.04378438 6.0023852 1.53742008 5.4386620 #> 611 9.677450 0.25304437 1.2327338 0.63096978 2.7434432 #> 614 10.538488 3.21741886 5.6953260 6.60129169 Inf #> 620 2.864603 2.80741209 6.0552893 1.04401193 5.0611838 #> 630 7.436280 2.99251277 6.0940883 2.95949542 5.9721761 #> 641 12.014582 0.23273791 0.8898776 2.22713522 Inf #> 645 11.110192 0.27124408 0.8292255 2.91506706 Inf #> nboot.successful path_class #> 1 1000 Lipid metabolism #> 2 957 Lipid metabolism #> 5 1000 Biosynthesis of other secondary metabolites #> 6 1000 Membrane transport #> 7 1000 Signal transduction #> 8 648 Lipid metabolism #> 9 620 Lipid metabolism #> 10 872 Lipid metabolism #> 11 909 Lipid metabolism #> 12 565 Lipid metabolism #> 13 1000 Lipid metabolism #> 14 1000 Lipid metabolism #> 16 1000 Membrane transport #> 17 1000 Signal transduction #> 18 718 Amino acid metabolism #> 19 718 Biosynthesis of other secondary metabolites #> 20 718 Translation #> 22 975 Membrane transport #> 23 975 Signal transduction #> 25 938 Membrane transport #> 26 962 Amino acid metabolism #> 27 962 Metabolism of other amino acids #> 28 962 Biosynthesis of other secondary metabolites #> 29 962 Translation #> 30 962 Membrane transport #> 31 979 Amino acid metabolism #> 32 979 Biosynthesis of other secondary metabolites #> 33 979 Translation #> 34 979 Membrane transport #> 35 851 Amino acid metabolism #> 36 851 Metabolism of other amino acids #> 37 851 Lipid metabolism #> 39 851 Energy metabolism #> 40 851 Translation #> 41 851 Biosynthesis of other secondary metabolites #> 42 851 Membrane transport #> 43 851 Signal transduction #> 44 1000 Amino acid metabolism #> 46 859 Energy metabolism #> 47 859 Signal transduction #> 48 833 Amino acid metabolism #> 49 833 Metabolism of other amino acids #> 50 833 Biosynthesis of other secondary metabolites #> 51 833 Membrane transport #> 52 890 Amino acid metabolism #> 53 890 Metabolism of other amino acids #> 54 890 Energy metabolism #> 55 890 Translation #> 56 890 Membrane transport #> 57 890 Signal transduction #> 58 940 Amino acid metabolism #> 59 1000 Lipid metabolism #> 60 1000 Amino acid metabolism #> 61 635 Amino acid metabolism #> 62 635 Metabolism of other amino acids #> 63 635 Biosynthesis of other secondary metabolites #> 64 635 Translation #> 65 635 Membrane transport #> 67 820 Amino acid metabolism #> 68 820 Metabolism of other amino acids #> 69 820 Biosynthesis of other secondary metabolites #> 70 820 Membrane transport #> 71 722 Energy metabolism #> 72 722 Membrane transport #> 73 722 Signal transduction #> 75 953 Amino acid metabolism #> 76 953 Lipid metabolism #> 77 953 Energy metabolism #> 79 962 Amino acid metabolism #> 80 962 Signal transduction #> 81 998 Amino acid metabolism #> 82 998 Metabolism of other amino acids #> 83 998 Translation #> 84 998 Membrane transport #> 86 497 Nucleotide metabolism #> 87 495 Nucleotide metabolism #> 103 443 Metabolism of other amino acids #> 116 353 Nucleotide metabolism #> 121 483 Nucleotide metabolism #> 123 304 Membrane transport #> 125 500 Nucleotide metabolism #> 131 483 Metabolism of other amino acids #> 134 405 Metabolism of other amino acids #> 150 500 Metabolism of other amino acids #> 157 487 Membrane transport #> 161 439 Metabolism of terpenoids and polyketides #> 169 500 Nucleotide metabolism #> 172 500 Metabolism of other amino acids #> 178 336 Nucleotide metabolism #> 180 500 Metabolism of other amino acids #> 181 500 Nucleotide metabolism #> 183 478 Nucleotide metabolism #> 184 500 Metabolism of other amino acids #> 186 295 Nucleotide metabolism #> 188 482 Metabolism of terpenoids and polyketides #> 193 483 Membrane transport #> 196 498 Transport and catabolism #> 198 500 Metabolism of terpenoids and polyketides #> 202 476 Metabolism of other amino acids #> 228 261 Nucleotide metabolism #> 233 415 Metabolism of other amino acids #> 235 480 Nucleotide metabolism #> 238 336 Metabolism of other amino acids #> 243 333 Transport and catabolism #> 245 315 Nucleotide metabolism #> 246 315 Metabolism of other amino acids #> 252 500 Metabolism of terpenoids and polyketides #> 253 499 Nucleotide metabolism #> 256 479 Membrane transport #> 266 446 Metabolism of terpenoids and polyketides #> 275 454 Metabolism of terpenoids and polyketides #> 276 491 Metabolism of terpenoids and polyketides #> 291 500 Metabolism of terpenoids and polyketides #> 293 377 Metabolism of terpenoids and polyketides #> 301 500 Metabolism of other amino acids #> 305 275 Metabolism of terpenoids and polyketides #> 306 500 Metabolism of other amino acids #> 312 491 Transport and catabolism #> 327 474 Nucleotide metabolism #> 338 500 Metabolism of terpenoids and polyketides #> 341 403 Transport and catabolism #> 344 469 Nucleotide metabolism #> 346 500 Metabolism of other amino acids #> 354 385 Nucleotide metabolism #> 361 291 Nucleotide metabolism #> 364 500 Nucleotide metabolism #> 370 465 Transport and catabolism #> 372 406 Metabolism of other amino acids #> 375 330 Transport and catabolism #> 389 345 Metabolism of other amino acids #> 399 466 Transport and catabolism #> 405 500 Metabolism of terpenoids and polyketides #> 408 303 Metabolism of other amino acids #> 419 266 Transport and catabolism #> 430 317 Membrane transport #> 445 485 Metabolism of other amino acids #> 458 282 Transport and catabolism #> 460 500 Membrane transport #> 475 366 Transport and catabolism #> 476 473 Metabolism of other amino acids #> 488 484 Metabolism of other amino acids #> 490 490 Membrane transport #> 492 306 Transport and catabolism #> 494 492 Membrane transport #> 499 500 Metabolism of other amino acids #> 501 499 Metabolism of other amino acids #> 513 309 Membrane transport #> 519 360 Membrane transport #> 534 434 Metabolism of other amino acids #> 540 461 Metabolism of other amino acids #> 549 483 Metabolism of terpenoids and polyketides #> 551 464 Membrane transport #> 552 500 Transport and catabolism #> 553 500 Metabolism of terpenoids and polyketides #> 554 266 Nucleotide metabolism #> 567 268 Transport and catabolism #> 571 448 Nucleotide metabolism #> 577 371 Nucleotide metabolism #> 579 500 Membrane transport #> 586 493 Transport and catabolism #> 587 493 Membrane transport #> 593 343 Metabolism of other amino acids #> 597 440 Nucleotide metabolism #> 604 250 Nucleotide metabolism #> 608 301 Nucleotide metabolism #> 610 280 Nucleotide metabolism #> 611 494 Metabolism of terpenoids and polyketides #> 614 283 Metabolism of other amino acids #> 620 312 Nucleotide metabolism #> 630 264 Nucleotide metabolism #> 641 496 Nucleotide metabolism #> 645 497 Nucleotide metabolism #> molecular.level #> 1 metabolites #> 2 metabolites #> 5 metabolites #> 6 metabolites #> 7 metabolites #> 8 metabolites #> 9 metabolites #> 10 metabolites #> 11 metabolites #> 12 metabolites #> 13 metabolites #> 14 metabolites #> 16 metabolites #> 17 metabolites #> 18 metabolites #> 19 metabolites #> 20 metabolites #> 22 metabolites #> 23 metabolites #> 25 metabolites #> 26 metabolites #> 27 metabolites #> 28 metabolites #> 29 metabolites #> 30 metabolites #> 31 metabolites #> 32 metabolites #> 33 metabolites #> 34 metabolites #> 35 metabolites #> 36 metabolites #> 37 metabolites #> 39 metabolites #> 40 metabolites #> 41 metabolites #> 42 metabolites #> 43 metabolites #> 44 metabolites #> 46 metabolites #> 47 metabolites #> 48 metabolites #> 49 metabolites #> 50 metabolites #> 51 metabolites #> 52 metabolites #> 53 metabolites #> 54 metabolites #> 55 metabolites #> 56 metabolites #> 57 metabolites #> 58 metabolites #> 59 metabolites #> 60 metabolites #> 61 metabolites #> 62 metabolites #> 63 metabolites #> 64 metabolites #> 65 metabolites #> 67 metabolites #> 68 metabolites #> 69 metabolites #> 70 metabolites #> 71 metabolites #> 72 metabolites #> 73 metabolites #> 75 metabolites #> 76 metabolites #> 77 metabolites #> 79 metabolites #> 80 metabolites #> 81 metabolites #> 82 metabolites #> 83 metabolites #> 84 metabolites #> 86 contigs #> 87 contigs #> 103 contigs #> 116 contigs #> 121 contigs #> 123 contigs #> 125 contigs #> 131 contigs #> 134 contigs #> 150 contigs #> 157 contigs #> 161 contigs #> 169 contigs #> 172 contigs #> 178 contigs #> 180 contigs #> 181 contigs #> 183 contigs #> 184 contigs #> 186 contigs #> 188 contigs #> 193 contigs #> 196 contigs #> 198 contigs #> 202 contigs #> 228 contigs #> 233 contigs #> 235 contigs #> 238 contigs #> 243 contigs #> 245 contigs #> 246 contigs #> 252 contigs #> 253 contigs #> 256 contigs #> 266 contigs #> 275 contigs #> 276 contigs #> 291 contigs #> 293 contigs #> 301 contigs #> 305 contigs #> 306 contigs #> 312 contigs #> 327 contigs #> 338 contigs #> 341 contigs #> 344 contigs #> 346 contigs #> 354 contigs #> 361 contigs #> 364 contigs #> 370 contigs #> 372 contigs #> 375 contigs #> 389 contigs #> 399 contigs #> 405 contigs #> 408 contigs #> 419 contigs #> 430 contigs #> 445 contigs #> 458 contigs #> 460 contigs #> 475 contigs #> 476 contigs #> 488 contigs #> 490 contigs #> 492 contigs #> 494 contigs #> 499 contigs #> 501 contigs #> 513 contigs #> 519 contigs #> 534 contigs #> 540 contigs #> 549 contigs #> 551 contigs #> 552 contigs #> 553 contigs #> 554 contigs #> 567 contigs #> 571 contigs #> 577 contigs #> 579 contigs #> 586 contigs #> 587 contigs #> 593 contigs #> 597 contigs #> 604 contigs #> 608 contigs #> 610 contigs #> 611 contigs #> 614 contigs #> 620 contigs #> 630 contigs #> 641 contigs #> 645 contigs extendedres.3 #> id irow adjpvalue model nbpar b c #> 1 NAP47_51 46 7.158246e-04 linear 2 -5.600559e-02 NA #> 2 NAP_2 2 6.232579e-05 exponential 3 4.598124e-01 NA #> 5 NAP_30 28 1.028343e-05 linear 2 -4.507832e-02 NA #> 6 NAP_30 28 1.028343e-05 linear 2 -4.507832e-02 NA #> 7 NAP_30 28 1.028343e-05 linear 2 -4.507832e-02 NA #> 8 NAP_38 34 1.885047e-03 exponential 3 6.010677e-01 NA #> 9 NAP_42 38 4.160193e-03 exponential 3 6.721023e-01 NA #> 10 NAP_52 47 3.920169e-02 log-Gauss-probit 5 4.500859e-01 7.2020026 #> 11 NAP_54 49 3.767103e-04 exponential 3 4.520654e-01 NA #> 12 NAP_56 51 1.489919e-03 exponential 3 4.392508e-01 NA #> 13 NAP_58 53 2.834198e-02 linear 2 -1.938127e-02 NA #> 14 NAP_73 67 3.767103e-04 linear 2 -5.787842e-02 NA #> 16 NP_121 197 9.889460e-03 linear 2 6.149475e-02 NA #> 17 NP_121 197 9.889460e-03 linear 2 6.149475e-02 NA #> 18 NP_129 204 7.286216e-03 exponential 3 7.786029e-03 NA #> 19 NP_129 204 7.286216e-03 exponential 3 7.786029e-03 NA #> 20 NP_129 204 7.286216e-03 exponential 3 7.786029e-03 NA #> 22 NP_140 214 8.550044e-03 Gauss-probit 4 6.955281e-01 4.8357744 #> 23 NP_140 214 8.550044e-03 Gauss-probit 4 6.955281e-01 4.8357744 #> 25 NP_147 221 1.061967e-03 exponential 3 -3.195388e-02 NA #> 26 NP_33 113 5.599308e-02 log-Gauss-probit 4 6.150170e-01 4.9173448 #> 27 NP_33 113 5.599308e-02 log-Gauss-probit 4 6.150170e-01 4.9173448 #> 28 NP_33 113 5.599308e-02 log-Gauss-probit 4 6.150170e-01 4.9173448 #> 29 NP_33 113 5.599308e-02 log-Gauss-probit 4 6.150170e-01 4.9173448 #> 30 NP_33 113 5.599308e-02 log-Gauss-probit 4 6.150170e-01 4.9173448 #> 31 NP_35 115 3.238559e-03 Gauss-probit 4 1.507579e+00 5.9751431 #> 32 NP_35 115 3.238559e-03 Gauss-probit 4 1.507579e+00 5.9751431 #> 33 NP_35 115 3.238559e-03 Gauss-probit 4 1.507579e+00 5.9751431 #> 34 NP_35 115 3.238559e-03 Gauss-probit 4 1.507579e+00 5.9751431 #> 35 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 36 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 37 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 39 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 40 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 41 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 42 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 43 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 44 NP_55 135 1.119380e-06 Gauss-probit 4 2.325273e+00 4.4698821 #> 46 NP_56 136 5.289310e-03 exponential 3 -4.360528e-03 NA #> 47 NP_56 136 5.289310e-03 exponential 3 -4.360528e-03 NA #> 48 NP_59 139 1.293440e-02 exponential 3 -3.667815e-01 NA #> 49 NP_59 139 1.293440e-02 exponential 3 -3.667815e-01 NA #> 50 NP_59 139 1.293440e-02 exponential 3 -3.667815e-01 NA #> 51 NP_59 139 1.293440e-02 exponential 3 -3.667815e-01 NA #> 52 NP_60 140 6.939560e-03 exponential 3 -5.150005e-01 NA #> 53 NP_60 140 6.939560e-03 exponential 3 -5.150005e-01 NA #> 54 NP_60 140 6.939560e-03 exponential 3 -5.150005e-01 NA #> 55 NP_60 140 6.939560e-03 exponential 3 -5.150005e-01 NA #> 56 NP_60 140 6.939560e-03 exponential 3 -5.150005e-01 NA #> 57 NP_60 140 6.939560e-03 exponential 3 -5.150005e-01 NA #> 58 NP_68 147 3.449660e-04 Gauss-probit 4 2.444950e+00 5.0555770 #> 59 NP_69 148 1.156302e-03 linear 2 -5.076324e-02 NA #> 60 NP_69 148 1.156302e-03 linear 2 -5.076324e-02 NA #> 61 NP_74 153 7.029527e-02 log-Gauss-probit 5 2.715410e-01 4.8474027 #> 62 NP_74 153 7.029527e-02 log-Gauss-probit 5 2.715410e-01 4.8474027 #> 63 NP_74 153 7.029527e-02 log-Gauss-probit 5 2.715410e-01 4.8474027 #> 64 NP_74 153 7.029527e-02 log-Gauss-probit 5 2.715410e-01 4.8474027 #> 65 NP_74 153 7.029527e-02 log-Gauss-probit 5 2.715410e-01 4.8474027 #> 67 NP_90 168 3.072572e-02 exponential 3 -2.686242e-01 NA #> 68 NP_90 168 3.072572e-02 exponential 3 -2.686242e-01 NA #> 69 NP_90 168 3.072572e-02 exponential 3 -2.686242e-01 NA #> 70 NP_90 168 3.072572e-02 exponential 3 -2.686242e-01 NA #> 71 NP_92 170 2.278810e-03 exponential 3 -4.595689e-03 NA #> 72 NP_92 170 2.278810e-03 exponential 3 -4.595689e-03 NA #> 73 NP_92 170 2.278810e-03 exponential 3 -4.595689e-03 NA #> 75 NP_94 172 1.798752e-05 Gauss-probit 4 3.016576e+00 4.8628416 #> 76 NP_94 172 1.798752e-05 Gauss-probit 4 3.016576e+00 4.8628416 #> 77 NP_94 172 1.798752e-05 Gauss-probit 4 3.016576e+00 4.8628416 #> 79 NP_96 174 5.128859e-04 Gauss-probit 4 2.409906e+00 5.2165398 #> 80 NP_96 174 5.128859e-04 Gauss-probit 4 2.409906e+00 5.2165398 #> 81 NP_98 176 1.544866e-04 Gauss-probit 4 1.880325e+00 5.9083172 #> 82 NP_98 176 1.544866e-04 Gauss-probit 4 1.880325e+00 5.9083172 #> 83 NP_98 176 1.544866e-04 Gauss-probit 4 1.880325e+00 5.9083172 #> 84 NP_98 176 1.544866e-04 Gauss-probit 4 1.880325e+00 5.9083172 #> 85 c00134 2802 2.762369e-04 linear 2 -2.179358e-01 NA #> 86 c00276 39331 9.401685e-07 exponential 3 1.499436e+00 NA #> 87 c00281 41217 2.894669e-06 exponential 3 1.408172e+00 NA #> 88 c00322 52577 1.875371e-03 exponential 3 1.805394e-03 NA #> 91 c00398 54508 1.668205e-05 linear 2 -1.405762e-01 NA #> 95 c00628 61115 5.388575e-07 exponential 3 4.923794e-02 NA #> 99 c00847 5295 9.884514e-03 linear 2 -1.266449e-01 NA #> 102 c00941 7809 4.335219e-03 linear 2 -7.650138e-02 NA #> 103 c00973 8280 6.803372e-03 exponential 3 -7.727847e-01 NA #> 104 c00973 8280 6.803372e-03 exponential 3 -7.727847e-01 NA #> 106 c01041 9614 2.607961e-04 exponential 3 1.528981e+00 NA #> 109 c01117 12251 2.634664e-03 linear 2 1.543520e-01 NA #> 110 c01133 12630 4.922435e-03 Gauss-probit 4 2.075770e+00 9.8438541 #> 111 c01155 13129 6.385399e-03 Gauss-probit 4 1.840019e+00 6.1917399 #> 115 c01250 16689 1.585374e-05 linear 2 4.316514e-01 NA #> 116 c01318 18587 9.761656e-03 exponential 3 -2.541336e-02 NA #> 117 c01370 20546 2.292186e-03 linear 2 9.464515e-02 NA #> 120 c01438 22651 1.939187e-04 Gauss-probit 4 1.764551e+00 10.9277844 #> 121 c01442 22830 8.398538e-07 exponential 3 -4.594974e-01 NA #> 122 c01447 23029 4.281444e-10 exponential 3 -1.254855e+00 NA #> 123 c01449 23118 3.283441e-05 Gauss-probit 4 4.461301e+00 3.6813967 #> 125 c01613 28185 5.354723e-03 linear 2 7.991209e-02 NA #> 127 c01616 28258 1.619046e-06 exponential 3 3.240702e-02 NA #> 128 c01629 28787 2.064119e-03 Gauss-probit 5 4.505403e+00 14.4444824 #> 129 c01643 29361 6.042318e-05 exponential 3 1.083425e-03 NA #> 131 c01645 29447 4.923790e-08 Gauss-probit 4 2.701659e+00 3.1591011 #> 132 c01645 29447 4.923790e-08 Gauss-probit 4 2.701659e+00 3.1591011 #> 133 c01739 32213 2.428740e-04 log-Gauss-probit 4 7.597910e-01 8.6772914 #> 134 c01924 38335 2.843216e-04 Gauss-probit 4 2.446537e+00 11.8943363 #> 137 c01952 39007 6.650930e-03 exponential 3 1.162982e-04 NA #> 140 c02004 40603 1.872409e-03 linear 2 9.583803e-02 NA #> 141 c02010 40836 3.289575e-06 Gauss-probit 4 2.473829e+00 11.5666888 #> 142 c02083 42840 3.959555e-04 linear 2 -1.362122e-01 NA #> 143 c02160 45443 1.197938e-07 exponential 3 4.291368e-01 NA #> 147 c02486 52753 2.171026e-05 exponential 3 -6.980028e-03 NA #> 148 c02572 52958 4.595821e-03 linear 2 1.555437e-01 NA #> 149 c02651 53192 2.080403e-04 exponential 3 4.499442e-02 NA #> 150 c02837 53881 7.971049e-04 linear 2 1.575210e-01 NA #> 152 c02877 53976 2.432349e-03 linear 2 -1.413708e-01 NA #> 156 c02955 54166 8.455883e-03 linear 2 2.166378e-01 NA #> 157 c02964 54187 3.684145e-04 exponential 3 -1.686471e+00 NA #> 158 c03046 54476 2.875687e-05 linear 2 -2.051304e-01 NA #> 161 c03088 54664 3.084821e-03 Gauss-probit 4 2.148866e+00 11.5968176 #> 164 c03134 54867 5.690878e-04 Gauss-probit 4 2.175505e+00 15.6410941 #> 165 c03146 54920 1.245703e-05 linear 2 -4.288489e-01 NA #> 166 c03147 54922 4.186163e-03 log-Gauss-probit 5 7.895259e-01 8.1665963 #> 167 c03150 54936 3.566795e-04 linear 2 1.084285e-01 NA #> 169 c03232 55298 2.548714e-06 linear 2 2.143541e-01 NA #> 170 c03258 55367 4.473786e-04 Gauss-probit 4 1.798037e+00 8.9781782 #> 171 c03284 55434 1.365366e-03 linear 2 -3.672796e-01 NA #> 172 c03284 55434 1.365366e-03 linear 2 -3.672796e-01 NA #> 174 c03319 55519 1.104637e-04 Gauss-probit 4 2.837930e+00 10.6370079 #> 178 c03358 55610 1.361311e-03 Gauss-probit 4 2.732236e+00 10.2055856 #> 179 c03392 55694 7.499082e-05 linear 2 -1.334615e-01 NA #> 180 c03440 55810 2.679169e-03 linear 2 -1.138782e-01 NA #> 181 c03440 55810 2.679169e-03 linear 2 -1.138782e-01 NA #> 183 c03526 56019 2.223268e-04 exponential 3 -1.249273e+00 NA #> 184 c03540 56053 3.430925e-03 linear 2 1.965028e-01 NA #> 185 c03540 56053 3.430925e-03 linear 2 1.965028e-01 NA #> 186 c03544 56063 6.386500e-03 exponential 3 -1.417594e-03 NA #> 188 c03571 56127 9.413418e-03 log-Gauss-probit 4 5.983299e-01 4.1680058 #> 190 c03586 56164 3.854715e-03 Gauss-probit 4 2.552963e+00 11.4923353 #> 193 c03661 56344 7.587641e-03 log-Gauss-probit 4 6.923540e-01 3.0415932 #> 196 c03724 56499 4.297972e-05 log-Gauss-probit 4 5.840913e-01 12.2786104 #> 197 c03745 56548 9.243140e-03 linear 2 -1.172201e-01 NA #> 198 c03760 56585 9.777594e-03 linear 2 -1.351428e-01 NA #> 199 c03761 56586 4.702449e-03 linear 2 1.262182e-01 NA #> 200 c03761 56586 4.702449e-03 linear 2 1.262182e-01 NA #> 201 c03784 56645 5.006652e-03 log-Gauss-probit 4 6.898228e-01 9.6540953 #> 202 c03784 56645 5.006652e-03 log-Gauss-probit 4 6.898228e-01 9.6540953 #> 203 c03784 56645 5.006652e-03 log-Gauss-probit 4 6.898228e-01 9.6540953 #> 205 c03801 56685 2.570986e-03 log-Gauss-probit 4 9.764227e-01 13.7221921 #> 207 c03925 57063 5.381358e-04 Gauss-probit 5 3.210268e+00 2.3821872 #> 208 c03931 57089 2.851070e-03 Gauss-probit 4 6.126334e+00 0.7526332 #> 211 c03948 57165 6.052153e-03 log-Gauss-probit 4 7.690806e-01 11.6144651 #> 214 c03950 57172 5.750220e-03 log-Gauss-probit 4 8.114609e-01 7.7688166 #> 215 c03979 57294 3.113834e-03 exponential 3 -4.177525e-03 NA #> 217 c04048 57585 3.263192e-03 linear 2 -1.211670e-01 NA #> 218 c04049 57589 4.043602e-04 log-Gauss-probit 4 7.902587e-01 13.3308965 #> 220 c04113 57859 1.165972e-04 linear 2 3.130549e-01 NA #> 221 c04117 57876 3.652581e-04 Gauss-probit 5 6.914901e+00 15.3546350 #> 222 c04129 57927 2.456812e-03 linear 2 -1.106194e-01 NA #> 225 c04240 58395 2.561139e-03 exponential 3 -1.404086e-03 NA #> 228 c04342 58826 1.308910e-04 Gauss-probit 4 7.197426e+00 7.7233560 #> 229 c04391 59033 3.624150e-04 linear 2 -1.363145e-01 NA #> 232 c04434 59217 7.874407e-03 Gauss-probit 5 3.908298e+00 2.8969340 #> 233 c04434 59217 7.874407e-03 Gauss-probit 5 3.908298e+00 2.8969340 #> 235 c04513 59551 9.612303e-03 log-Gauss-probit 4 7.506663e-01 11.8496013 #> 236 c04527 59607 1.222252e-03 exponential 3 4.226728e-04 NA #> 237 c04553 59682 2.052935e-04 exponential 3 -6.566892e-03 NA #> 238 c04553 59682 2.052935e-04 exponential 3 -6.566892e-03 NA #> 240 c04553 59682 2.052935e-04 exponential 3 -6.566892e-03 NA #> 242 c04609 59807 6.407706e-03 linear 2 2.392413e-01 NA #> 243 c04613 59817 2.581335e-03 Gauss-probit 4 3.032023e+00 11.9083638 #> 245 c04619 59830 8.483282e-03 exponential 3 2.328075e-02 NA #> 246 c04619 59830 8.483282e-03 exponential 3 2.328075e-02 NA #> 248 c04625 59843 3.759793e-03 linear 2 1.791830e-01 NA #> 251 c04647 59892 1.704160e-03 linear 2 9.344909e-02 NA #> 252 c04647 59892 1.704160e-03 linear 2 9.344909e-02 NA #> 253 c04655 59910 4.758216e-05 log-Gauss-probit 4 7.297684e-01 11.5528527 #> 255 c04667 59936 7.175098e-04 exponential 3 3.727143e-03 NA #> 256 c04683 59973 5.823021e-04 exponential 3 8.026136e-01 NA #> 257 c04702 60014 1.150256e-05 exponential 3 -8.958404e-04 NA #> 259 c04721 60057 1.935071e-03 linear 2 8.621251e-02 NA #> 260 c04785 60199 2.947658e-03 exponential 3 1.554432e+00 NA #> 263 c04803 60240 8.645920e-03 linear 2 1.054475e-01 NA #> 265 c04841 60324 1.209154e-05 linear 2 1.327705e-01 NA #> 266 c04883 60416 7.479393e-03 exponential 3 1.701162e+00 NA #> 270 c04981 60634 1.604178e-03 Gauss-probit 5 6.073747e+00 15.5258805 #> 272 c04990 60654 1.290312e-03 linear 2 2.168492e-01 NA #> 274 c05067 60825 3.429820e-03 exponential 3 4.733132e-01 NA #> 275 c05081 60856 1.998628e-04 exponential 3 4.406436e-02 NA #> 276 c05100 60898 4.685588e-05 exponential 3 1.626252e+00 NA #> 277 c05122 60947 5.831294e-03 linear 2 1.017186e-01 NA #> 279 c05183 61177 7.470683e-05 exponential 3 -6.029337e-03 NA #> 280 c05186 61192 9.977277e-06 exponential 3 -7.710575e-03 NA #> 282 c05207 61277 4.276674e-03 exponential 3 1.045991e+00 NA #> 285 c05269 10 9.270577e-05 Gauss-probit 4 2.656350e+00 9.3830167 #> 286 c05284 73 3.841471e-03 linear 2 2.319088e-01 NA #> 287 c05305 159 1.899100e-03 linear 2 -1.337043e-01 NA #> 288 c05305 159 1.899100e-03 linear 2 -1.337043e-01 NA #> 291 c05326 247 4.812259e-03 linear 2 -1.628214e-01 NA #> 293 c05358 382 7.443184e-03 exponential 3 2.090616e-02 NA #> 295 c05377 463 9.290871e-03 linear 2 -9.062729e-02 NA #> 296 c05385 497 5.701646e-05 exponential 3 -1.697255e-01 NA #> 297 c05401 565 9.242438e-03 linear 2 -8.645475e-02 NA #> 298 c05401 565 9.242438e-03 linear 2 -8.645475e-02 NA #> 299 c05417 633 9.280962e-05 exponential 3 -3.112237e-03 NA #> 301 c05581 1323 4.187799e-03 linear 2 1.169616e-01 NA #> 303 c05589 1360 8.134238e-03 linear 2 -6.289437e-02 NA #> 305 c05641 1567 2.764636e-03 exponential 3 -8.701043e-04 NA #> 306 c05645 1576 7.113588e-04 linear 2 -1.348525e-01 NA #> 307 c05645 1576 7.113588e-04 linear 2 -1.348525e-01 NA #> 308 c05698 1694 3.957047e-04 Gauss-probit 4 1.079324e+00 7.5677415 #> 312 c05903 2148 4.374165e-03 log-Gauss-probit 4 7.532079e-01 12.1685832 #> 314 c05923 2195 1.058132e-05 exponential 3 -2.107493e+00 NA #> 317 c05946 2245 7.079721e-03 Gauss-probit 4 2.368354e+00 11.2206087 #> 320 c05970 2298 2.347871e-03 Gauss-probit 5 3.471728e+00 3.6603885 #> 321 c05970 2298 2.347871e-03 Gauss-probit 5 3.471728e+00 3.6603885 #> 322 c05996 2356 3.511114e-04 exponential 3 7.501887e-01 NA #> 327 c06059 2495 1.623735e-03 exponential 3 9.866188e-01 NA #> 328 c06077 2535 2.710065e-04 exponential 3 3.802400e-02 NA #> 330 c06085 2553 1.680296e-05 exponential 3 -2.430678e-03 NA #> 331 c06133 2659 8.340750e-07 linear 2 1.581037e-01 NA #> 332 c06133 2659 8.340750e-07 linear 2 1.581037e-01 NA #> 333 c06142 2694 3.174858e-04 Gauss-probit 4 1.565882e+00 7.7633888 #> 336 c06164 2784 2.112917e-04 Gauss-probit 5 3.551701e+00 6.6100833 #> 338 c06208 2970 8.742721e-04 linear 2 1.440101e-01 NA #> 339 c06258 3180 3.342031e-03 log-Gauss-probit 4 7.135024e-01 2.8818475 #> 340 c06303 3372 1.986352e-04 Gauss-probit 4 2.664174e+00 5.4338860 #> 341 c06313 3413 6.508671e-04 Gauss-probit 4 2.523979e+00 6.3598648 #> 342 c06429 3686 7.498489e-03 linear 2 2.843827e-01 NA #> 343 c06440 3711 4.047233e-03 Gauss-probit 4 1.930519e+00 11.5544508 #> 344 c06518 3887 4.434225e-04 exponential 3 1.282679e+00 NA #> 346 c06548 3976 1.118557e-04 linear 2 2.267280e-01 NA #> 350 c06637 4352 4.977792e-04 exponential 3 2.940553e-02 NA #> 354 c06762 4630 1.129124e-03 exponential 3 2.579227e-02 NA #> 357 c06876 4888 7.528161e-08 exponential 3 8.665206e-02 NA #> 358 c06880 4897 2.817783e-08 exponential 3 -1.170819e-01 NA #> 360 c06880 4897 2.817783e-08 exponential 3 -1.170819e-01 NA #> 361 c06881 4900 9.898720e-04 exponential 3 3.893310e-04 NA #> 362 c06884 4906 9.612303e-03 log-Gauss-probit 4 6.237164e-01 11.5919778 #> 364 c06943 5037 4.856986e-04 linear 2 1.205147e-01 NA #> 365 c06962 5080 9.268299e-03 linear 2 -7.266508e-02 NA #> 368 c07027 5226 4.204526e-05 exponential 3 -1.818945e-02 NA #> 370 c07072 5328 9.285630e-04 exponential 3 1.750908e+00 NA #> 372 c07118 5428 1.069214e-06 Gauss-probit 4 3.201018e+00 13.8162214 #> 375 c07206 5768 1.331320e-04 exponential 3 1.150705e-02 NA #> 376 c07232 5877 3.268967e-03 linear 2 -1.649789e-01 NA #> 378 c07259 5994 8.634188e-03 exponential 3 -2.238603e-03 NA #> 379 c07261 6000 9.973687e-03 exponential 3 -6.624269e-01 NA #> 380 c07263 6010 6.171276e-03 exponential 3 -3.039276e-04 NA #> 384 c07386 6531 6.587401e-09 exponential 3 1.261410e+00 NA #> 386 c07492 6981 1.040808e-03 linear 2 8.280517e-02 NA #> 387 c07492 6981 1.040808e-03 linear 2 8.280517e-02 NA #> 389 c07529 7138 7.515694e-04 exponential 3 2.811126e-02 NA #> 391 c07550 7226 3.659885e-03 linear 2 -1.065091e-01 NA #> 394 c07703 7740 5.335570e-05 exponential 3 -9.916504e-02 NA #> 395 c07715 7768 1.877882e-09 exponential 3 2.083659e-01 NA #> 397 c07797 7953 9.102023e-03 Gauss-probit 5 5.456328e+00 11.4303586 #> 399 c07859 8092 1.007072e-03 Gauss-probit 4 2.145963e+00 7.6939508 #> 401 c07957 8310 1.752806e-04 Gauss-probit 4 2.462513e+00 8.3205953 #> 404 c08065 8553 3.401488e-04 exponential 3 -1.628308e-02 NA #> 405 c08131 8701 1.673911e-03 linear 2 1.826552e-01 NA #> 408 c08241 8948 1.777483e-04 Gauss-probit 4 4.306086e+00 12.7509474 #> 409 c08251 8970 3.556009e-07 exponential 3 3.107220e-01 NA #> 410 c08284 9045 9.182573e-05 exponential 3 -6.347284e-03 NA #> 411 c08296 9071 6.435369e-04 Gauss-probit 4 1.769636e+00 14.5173033 #> 414 c08408 9323 1.435436e-03 linear 2 -1.328818e-01 NA #> 416 c08437 9388 2.774965e-03 Gauss-probit 4 2.500577e+00 15.2527459 #> 419 c08466 9451 1.593456e-04 exponential 3 8.887870e-04 NA #> 420 c08470 9462 9.083535e-04 exponential 3 2.790157e-03 NA #> 422 c08630 9820 1.427514e-05 linear 2 5.428566e-01 NA #> 427 c08733 10248 3.326616e-05 Gauss-probit 4 3.137538e+00 9.9736456 #> 428 c08733 10248 3.326616e-05 Gauss-probit 4 3.137538e+00 9.9736456 #> 430 c08762 10368 1.641885e-03 exponential 3 3.093377e-03 NA #> 433 c08806 10557 2.319865e-04 exponential 3 -1.396020e-03 NA #> 437 c08946 11149 1.361378e-03 linear 2 -1.268688e-01 NA #> 439 c08979 11300 3.707499e-03 exponential 3 1.744083e-03 NA #> 441 c08979 11300 3.707499e-03 exponential 3 1.744083e-03 NA #> 443 c09104 11625 8.299711e-04 linear 2 2.921985e-01 NA #> 445 c09125 11677 1.569172e-03 exponential 3 8.284746e-01 NA #> 447 c09171 11859 6.956216e-04 exponential 3 4.862071e-03 NA #> 450 c09314 12339 1.109864e-06 linear 2 -1.887393e-01 NA #> 453 c09437 12639 3.571289e-04 Gauss-probit 4 1.928769e+00 11.2188707 #> 454 c09437 12639 3.571289e-04 Gauss-probit 4 1.928769e+00 11.2188707 #> 457 c09562 12942 4.373374e-03 exponential 3 -2.161149e-03 NA #> 458 c09562 12942 4.373374e-03 exponential 3 -2.161149e-03 NA #> 460 c09598 13030 2.362131e-03 linear 2 -2.586441e-01 NA #> 461 c09599 13034 5.092176e-03 linear 2 9.794904e-02 NA #> 464 c09662 13187 7.680486e-03 linear 2 -1.015681e-01 NA #> 465 c09664 13193 4.054480e-04 exponential 3 3.206674e-02 NA #> 466 c09667 13199 4.349398e-03 exponential 3 -3.552147e-02 NA #> 467 c09730 13354 2.336694e-03 exponential 3 -7.259535e-04 NA #> 468 c09850 13648 2.372640e-05 exponential 3 -9.250649e-05 NA #> 469 c09874 13707 9.527637e-03 log-Gauss-probit 4 6.223239e-01 6.1391526 #> 470 c09918 13875 8.329706e-03 linear 2 7.343326e-02 NA #> 471 c09971 14113 5.839468e-03 exponential 3 -3.989293e-02 NA #> 473 c10039 14416 8.764282e-04 linear 2 2.073703e-01 NA #> 475 c10057 14495 3.390469e-03 exponential 3 2.415379e-02 NA #> 476 c10066 14533 1.347692e-03 exponential 3 -9.389332e-01 NA #> 479 c10088 14635 3.780459e-03 linear 2 -8.624646e-02 NA #> 480 c10088 14635 3.780459e-03 linear 2 -8.624646e-02 NA #> 481 c10125 14799 1.608679e-03 Gauss-probit 4 2.867974e+00 11.7480225 #> 482 c10155 14932 5.257232e-06 exponential 3 2.863366e-01 NA #> 483 c10163 14967 4.667371e-03 linear 2 1.195824e-01 NA #> 486 c10229 15259 6.113675e-04 Gauss-probit 4 2.711076e+00 12.5673623 #> 488 c10238 15302 1.228501e-03 exponential 3 -1.793775e+00 NA #> 490 c10269 15440 7.296658e-05 exponential 3 1.592475e+00 NA #> 491 c10269 15440 7.296658e-05 exponential 3 1.592475e+00 NA #> 492 c10302 15585 4.225116e-03 exponential 3 6.617470e-03 NA #> 493 c10304 15596 2.269389e-03 linear 2 -1.056212e-01 NA #> 494 c10311 15626 1.016928e-03 log-Gauss-probit 4 7.845651e-01 3.5216604 #> 495 c10345 15778 3.099644e-03 log-Gauss-probit 4 8.904332e-01 11.1141357 #> 496 c10386 15957 3.625125e-03 linear 2 2.199089e-01 NA #> 498 c10413 16075 3.498590e-04 linear 2 1.135548e-01 NA #> 499 c10413 16075 3.498590e-04 linear 2 1.135548e-01 NA #> 500 c10419 16107 4.121617e-03 Gauss-probit 4 5.137237e+00 8.8683481 #> 501 c10499 16461 1.256216e-07 Gauss-probit 4 2.062548e+00 11.8312797 #> 502 c10511 16512 7.145948e-03 linear 2 1.747655e-01 NA #> 504 c10532 16606 7.887575e-04 exponential 3 8.633562e-02 NA #> 505 c10607 16795 2.129357e-04 Gauss-probit 4 2.821383e+00 14.3133366 #> 509 c10754 17155 5.833637e-04 exponential 3 -2.995883e-04 NA #> 513 c10934 17597 5.753046e-03 exponential 3 7.891420e-03 NA #> 514 c10934 17597 5.753046e-03 exponential 3 7.891420e-03 NA #> 515 c10976 17699 8.637145e-03 linear 2 2.195307e-01 NA #> 517 c11168 18235 1.949306e-03 exponential 3 2.214326e-03 NA #> 518 c11210 18416 7.958846e-04 log-Gauss-probit 4 1.054030e+00 7.7051920 #> 519 c11233 18523 4.606893e-03 exponential 3 1.934382e-02 NA #> 520 c11334 18970 8.791723e-04 exponential 3 1.020052e-02 NA #> 521 c11382 19182 4.750777e-03 Gauss-probit 4 1.103583e+00 3.2368169 #> 522 c11397 19250 3.445348e-03 exponential 3 2.401132e-04 NA #> 523 c11456 19511 3.753562e-09 exponential 3 -1.853928e-01 NA #> 525 c11462 19537 9.770964e-03 log-Gauss-probit 4 7.191494e-01 3.3180250 #> 526 c11480 19620 5.163659e-03 linear 2 1.902326e-01 NA #> 527 c11530 19841 8.558466e-03 log-Gauss-probit 4 1.099263e+00 5.8568962 #> 528 c11558 19962 5.915481e-04 linear 2 1.224014e-01 NA #> 531 c11630 20284 5.955893e-06 exponential 3 -3.666783e-03 NA #> 534 c11906 20969 9.654346e-04 Gauss-probit 4 2.292477e+00 11.7948434 #> 535 c11942 21057 4.470344e-03 exponential 3 1.593425e-03 NA #> 540 c12260 22081 8.245828e-06 exponential 3 1.353701e-01 NA #> 542 c12281 22175 4.161221e-03 log-Gauss-probit 4 6.948815e-01 4.9989594 #> 543 c12403 22716 3.760750e-03 Gauss-probit 4 2.214023e+00 14.5772553 #> 544 c12506 23173 7.671714e-05 exponential 3 -2.509837e-03 NA #> 546 c12544 23346 1.722105e-03 linear 2 1.861575e-01 NA #> 547 c12572 23468 6.773752e-04 exponential 3 1.083771e-02 NA #> 549 c12576 23487 9.267247e-03 log-Gauss-probit 4 1.021352e+00 4.4432065 #> 551 c12705 23819 9.933717e-03 exponential 3 -1.751567e+00 NA #> 552 c12781 24007 1.047802e-03 linear 2 4.825735e-01 NA #> 553 c12927 24362 7.217499e-04 linear 2 3.109088e-01 NA #> 554 c13186 25095 5.598806e-03 exponential 3 -3.355018e-04 NA #> 556 c13243 25350 9.139692e-03 exponential 3 -6.088131e-01 NA #> 558 c13270 25470 5.267827e-03 linear 2 1.432054e-01 NA #> 559 c13277 25500 2.669356e-04 Gauss-probit 4 2.594431e+00 10.6317327 #> 561 c13297 25589 4.131109e-04 exponential 3 -6.832444e-03 NA #> 562 c13297 25589 4.131109e-04 exponential 3 -6.832444e-03 NA #> 565 c13517 26538 8.227406e-04 exponential 3 7.571071e-01 NA #> 566 c13525 26574 3.237246e-03 exponential 3 -1.128907e+00 NA #> 567 c13542 26650 7.852988e-04 exponential 3 2.600645e-03 NA #> 569 c13574 26794 1.921858e-03 linear 2 2.317066e-01 NA #> 571 c13596 26891 2.014638e-03 Gauss-probit 4 2.094345e+00 2.6618273 #> 572 c13598 26896 4.965270e-06 linear 2 2.958897e-01 NA #> 573 c13605 26931 1.210717e-03 linear 2 1.951699e-01 NA #> 574 c13674 27161 2.365368e-03 exponential 3 -6.072924e-01 NA #> 575 c13764 27378 4.475730e-03 exponential 3 5.093825e-03 NA #> 577 c13825 27530 7.036682e-03 Gauss-probit 4 2.375172e+00 6.8208872 #> 579 c14005 27954 6.155794e-03 linear 2 4.216192e-01 NA #> 580 c14005 27954 6.155794e-03 linear 2 4.216192e-01 NA #> 581 c14237 28676 1.538888e-04 log-Gauss-probit 4 7.541007e-01 7.8537519 #> 583 c14363 29213 1.557209e-05 exponential 3 2.253639e-02 NA #> 585 c14423 29467 4.543972e-03 exponential 3 -1.604455e-02 NA #> 586 c14431 29501 2.619248e-03 log-Gauss-probit 4 7.955319e-01 5.9474858 #> 587 c14431 29501 2.619248e-03 log-Gauss-probit 4 7.955319e-01 5.9474858 #> 588 c14618 30291 2.083712e-03 Gauss-probit 5 3.713824e+00 16.5075720 #> 590 c15068 31336 1.093408e-04 linear 2 5.374968e-01 NA #> 591 c15455 32647 2.235788e-06 exponential 3 9.915016e-03 NA #> 593 c15572 33143 7.075275e-03 exponential 3 4.643676e-02 NA #> 595 c15719 33766 3.883724e-04 linear 2 -1.905828e-01 NA #> 596 c15843 34290 1.405103e-05 Gauss-probit 4 3.067016e+00 2.6582772 #> 597 c15942 34598 4.718990e-05 exponential 3 -4.450675e-02 NA #> 598 c15975 34674 3.356863e-03 exponential 3 -8.170672e-01 NA #> 599 c15975 34674 3.356863e-03 exponential 3 -8.170672e-01 NA #> 603 c16742 36804 2.781433e-04 linear 2 3.848784e-01 NA #> 604 c16973 37787 2.825599e-04 Gauss-probit 4 8.319118e+00 -4.6684484 #> 606 c17138 38478 7.921107e-04 linear 2 1.967745e-01 NA #> 608 c17497 39284 3.028115e-04 exponential 3 -2.312831e-03 NA #> 610 c17517 39327 2.461490e-03 exponential 3 5.997141e-04 NA #> 611 c17694 39843 2.019227e-05 exponential 3 -1.826364e+00 NA #> 612 c17823 40393 6.238172e-04 exponential 3 6.779690e-04 NA #> 614 c17823 40393 6.238172e-04 exponential 3 6.779690e-04 NA #> 615 c17823 40393 6.238172e-04 exponential 3 6.779690e-04 NA #> 616 c18178 41738 1.357329e-03 linear 2 1.677462e-01 NA #> 617 c18301 42015 1.848281e-06 exponential 3 1.568251e-01 NA #> 618 c18306 42025 7.861070e-04 Gauss-probit 4 1.686325e+00 6.0977853 #> 619 c18306 42025 7.861070e-04 Gauss-probit 4 1.686325e+00 6.0977853 #> 620 c18315 42046 3.162528e-03 exponential 3 3.707484e-03 NA #> 623 c18540 42550 4.541751e-03 log-Gauss-probit 5 2.314017e-01 3.5945632 #> 624 c18686 42975 4.956775e-03 linear 2 3.126935e-01 NA #> 626 c18794 43434 3.967972e-04 exponential 3 1.595602e-01 NA #> 630 c19738 46332 6.573236e-03 exponential 3 -2.344367e-04 NA #> 631 c20526 48892 1.461304e-06 exponential 3 -3.976049e-02 NA #> 634 c20668 49255 2.423243e-04 linear 2 2.041387e-01 NA #> 641 c21327 51498 7.255831e-06 exponential 3 1.489682e+00 NA #> 642 c21366 51578 5.125046e-03 exponential 3 -6.140642e-01 NA #> 643 c21438 51724 9.104359e-03 linear 2 1.122721e-01 NA #> 644 c21442 51732 7.480590e-05 exponential 3 4.885340e-04 NA #> 645 c21452 51752 7.810285e-07 exponential 3 1.427234e+00 NA #> 646 c21521 51888 5.591900e-05 exponential 3 4.662577e-04 NA #> d e f SDres typology trend y0 #> 1 7.3435706 NA NA 0.12454183 L.dec dec 7.343571 #> 2 5.9418958 -1.6479584 NA 0.12604568 E.dec.convex dec 5.941896 #> 5 7.8591094 NA NA 0.05203245 L.dec dec 7.859109 #> 6 7.8591094 NA NA 0.05203245 L.dec dec 7.859109 #> 7 7.8591094 NA NA 0.05203245 L.dec dec 7.859109 #> 8 6.8579095 -0.3213163 NA 0.23376392 E.dec.convex dec 6.857909 #> 9 6.2092863 -0.3230281 NA 0.28968463 E.dec.convex dec 6.209286 #> 10 7.2888833 1.3087220 -0.1436781 0.07085857 lGP.U U 7.288883 #> 11 6.8685231 -0.6254549 NA 0.15031166 E.dec.convex dec 6.868523 #> 12 7.5584812 -0.2649798 NA 0.15353807 E.dec.convex dec 7.558481 #> 13 6.4674657 NA NA 0.05769085 L.dec dec 6.467466 #> 14 5.7383018 NA NA 0.11726837 L.dec dec 5.738302 #> 16 5.3301711 NA NA 0.18706049 L.inc inc 5.330171 #> 17 5.3301711 NA NA 0.18706049 L.inc inc 5.330171 #> 18 4.9684754 2.0243885 NA 0.14303183 E.inc.convex inc 4.968475 #> 19 4.9684754 2.0243885 NA 0.14303183 E.inc.convex inc 4.968475 #> 20 4.9684754 2.0243885 NA 0.14303183 E.inc.convex inc 4.968475 #> 22 4.8357744 1.4069463 0.2455556 0.10615565 GP.bell bell 4.867514 #> 23 4.8357744 1.4069463 0.2455556 0.10615565 GP.bell bell 4.867514 #> 25 5.4023281 3.3895616 NA 0.06744390 E.dec.concave dec 5.402328 #> 26 4.9173448 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 27 4.9173448 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 28 4.9173448 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 29 4.9173448 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 30 4.9173448 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 31 5.9751431 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 32 5.9751431 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 33 5.9751431 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 34 5.9751431 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 35 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 36 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 37 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 39 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 40 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 41 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 42 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 43 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 44 4.4698821 1.8678530 0.8450245 0.12256991 GP.bell bell 5.081883 #> 46 7.0187638 1.9509846 NA 0.05587260 E.dec.concave dec 7.018764 #> 47 7.0187638 1.9509846 NA 0.05587260 E.dec.concave dec 7.018764 #> 48 4.4916953 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 49 4.4916953 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 50 4.4916953 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 51 4.4916953 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 52 5.2157086 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 53 5.2157086 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 54 5.2157086 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 55 5.2157086 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 56 5.2157086 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 57 5.2157086 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 58 5.0555770 2.6269663 -0.6208164 0.12476292 GP.U U 4.707014 #> 59 5.5138551 NA NA 0.11224716 L.dec dec 5.513855 #> 60 5.5138551 NA NA 0.11224716 L.dec dec 5.513855 #> 61 4.6897871 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 62 4.6897871 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 63 4.6897871 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 64 4.6897871 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 65 4.6897871 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 67 6.0705233 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 68 6.0705233 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 69 6.0705233 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 70 6.0705233 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 71 6.4891514 1.5921459 NA 0.17066885 E.dec.concave dec 6.489151 #> 72 6.4891514 1.5921459 NA 0.17066885 E.dec.concave dec 6.489151 #> 73 6.4891514 1.5921459 NA 0.17066885 E.dec.concave dec 6.489151 #> 75 4.8628416 1.3892912 0.7365937 0.14844069 GP.bell bell 5.525316 #> 76 4.8628416 1.3892912 0.7365937 0.14844069 GP.bell bell 5.525316 #> 77 4.8628416 1.3892912 0.7365937 0.14844069 GP.bell bell 5.525316 #> 79 5.2165398 1.9279453 0.8281597 0.22659347 GP.bell bell 5.817903 #> 80 5.2165398 1.9279453 0.8281597 0.22659347 GP.bell bell 5.817903 #> 81 5.9083172 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 82 5.9083172 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 83 5.9083172 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 84 5.9083172 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 85 10.8510453 NA NA 0.41695413 L.dec dec 10.851045 #> 86 12.4282123 -2.1982296 NA 0.28684892 E.dec.convex dec 12.428212 #> 87 12.4118704 -2.4052289 NA 0.28115971 E.dec.convex dec 12.411870 #> 88 16.4105357 1.1527615 NA 0.14530179 E.inc.convex inc 16.410536 #> 91 12.9488316 NA NA 0.20667778 L.dec dec 12.948832 #> 95 15.7725900 1.8964701 NA 0.31543564 E.inc.convex inc 15.772590 #> 99 14.6496706 NA NA 0.37973174 L.dec dec 14.649671 #> 102 9.3913476 NA NA 0.18766848 L.dec dec 9.391348 #> 103 7.2362696 -2.3752014 NA 0.30192346 E.inc.concave inc 7.236270 #> 104 7.2362696 -2.3752014 NA 0.30192346 E.inc.concave inc 7.236270 #> 106 7.3442837 -1.3800379 NA 0.50872456 E.dec.convex dec 7.344284 #> 109 15.1021881 NA NA 0.37952774 L.inc inc 15.102188 #> 110 9.8438541 3.1688140 1.4586502 0.27599070 GP.bell bell 10.298744 #> 111 6.1917399 2.6438900 -2.1169700 0.72076613 GP.U U 5.437714 #> 115 12.3018744 NA NA 0.78665664 L.inc inc 12.301874 #> 116 9.3005915 1.9823323 NA 0.31949144 E.dec.concave dec 9.300591 #> 117 17.3249844 NA NA 0.20275495 L.inc inc 17.324984 #> 120 10.9277844 1.5194196 -2.4264132 0.80589796 GP.U U 9.253000 #> 121 13.0826064 5.0536325 NA 0.21425367 E.dec.concave dec 13.082606 #> 122 14.4649621 6.7164464 NA 0.21109707 E.dec.concave dec 14.464962 #> 123 3.6813967 2.5679456 7.7390108 0.55445078 GP.bell bell 10.238924 #> 125 10.7640181 NA NA 0.23146795 L.inc inc 10.764018 #> 127 15.1467650 1.8613086 NA 0.20825290 E.inc.convex inc 15.146765 #> 128 11.2906700 4.5827956 -3.3708994 0.30754041 GP.U U 9.768606 #> 129 11.9235370 0.9067940 NA 0.44761261 E.inc.convex inc 11.923537 #> 131 3.1591011 1.7419187 3.3237027 0.41149782 GP.bell bell 5.859022 #> 132 3.1591011 1.7419187 3.3237027 0.41149782 GP.bell bell 5.859022 #> 133 8.6772914 1.7108205 -1.8696263 0.67001839 lGP.U U 8.677291 #> 134 11.8943363 2.4191926 -1.3218368 0.24812481 GP.U U 11.083641 #> 137 12.4524972 0.7590944 NA 0.28188470 E.inc.convex inc 12.452497 #> 140 11.2948498 NA NA 0.20469339 L.inc inc 11.294850 #> 141 11.5666888 1.5731637 -1.1211951 0.19687033 GP.U U 10.650748 #> 142 10.6115156 NA NA 0.27493541 L.dec dec 10.611516 #> 143 7.5281155 3.3691745 NA 0.40223575 E.inc.convex inc 7.528115 #> 147 17.8604532 1.2635774 NA 0.29022358 E.dec.concave dec 17.860453 #> 148 14.7000111 NA NA 0.44007932 L.inc inc 14.700011 #> 149 7.8301186 1.8307946 NA 0.52494332 E.inc.convex inc 7.830119 #> 150 5.6626237 NA NA 0.32569343 L.inc inc 5.662624 #> 152 12.5354341 NA NA 0.38516458 L.dec dec 12.535434 #> 156 4.2547569 NA NA 0.61055166 L.inc inc 4.254757 #> 157 8.0681583 -1.5870424 NA 0.57855035 E.inc.concave inc 8.068158 #> 158 12.3485193 NA NA 0.33573498 L.dec dec 12.348519 #> 161 11.5968176 1.8275480 -0.8682280 0.29736729 GP.U U 10.992074 #> 164 15.6410941 2.4622535 -1.0424723 0.22568934 GP.U U 15.091678 #> 165 10.5084377 NA NA 0.64924704 L.dec dec 10.508438 #> 166 11.7426152 5.4773692 1.4580135 0.29410154 lGP.bell bell 11.742615 #> 167 10.9445611 NA NA 0.20700446 L.inc inc 10.944561 #> 169 8.3293039 NA NA 0.31094519 L.inc inc 8.329304 #> 170 8.9781782 2.1869740 1.3093175 0.39364195 GP.bell bell 9.603054 #> 171 9.1119357 NA NA 0.84138808 L.dec dec 9.111936 #> 172 9.1119357 NA NA 0.84138808 L.dec dec 9.111936 #> 174 10.6370079 2.4022793 -4.0922462 0.73206856 GP.U U 7.777009 #> 178 10.2055856 2.8555099 2.3244195 0.41255116 GP.bell bell 11.551851 #> 179 13.2867883 NA NA 0.23525867 L.dec dec 13.286788 #> 180 13.9590916 NA NA 0.26918435 L.dec dec 13.959092 #> 181 13.9590916 NA NA 0.26918435 L.dec dec 13.959092 #> 183 6.7361771 -0.6498650 NA 0.38185313 E.inc.concave inc 6.736177 #> 184 6.4085538 NA NA 0.52767310 L.inc inc 6.408554 #> 185 6.4085538 NA NA 0.52767310 L.inc inc 6.408554 #> 186 8.9010369 1.0237053 NA 0.37429011 E.dec.concave dec 8.901037 #> 188 4.1680058 2.3167014 1.3407136 0.64489184 lGP.bell bell 4.168006 #> 190 11.4923353 2.5819255 1.4610375 0.34283417 GP.bell bell 12.368447 #> 193 3.0415932 1.8809478 1.7942520 0.87754767 lGP.bell bell 3.041593 #> 196 12.2786104 1.7372724 -2.0216456 0.53171016 lGP.U U 12.278610 #> 197 8.8178134 NA NA 0.36412409 L.dec dec 8.817813 #> 198 10.3504426 NA NA 0.39026969 L.dec dec 10.350443 #> 199 10.2773638 NA NA 0.38834605 L.inc inc 10.277364 #> 200 10.2773638 NA NA 0.38834605 L.inc inc 10.277364 #> 201 9.6540953 1.9560222 -2.1370159 1.14939830 lGP.U U 9.654095 #> 202 9.6540953 1.9560222 -2.1370159 1.14939830 lGP.U U 9.654095 #> 203 9.6540953 1.9560222 -2.1370159 1.14939830 lGP.U U 9.654095 #> 205 13.7221921 2.8275360 -0.5902918 0.20207860 lGP.U U 13.722192 #> 207 -5.1882517 0.0000000 4.6027866 0.51206355 GP.bell bell 3.199754 #> 208 0.7526332 2.5547012 8.4153150 0.55821268 GP.bell bell 8.467177 #> 211 11.6144651 2.6085834 -0.6474606 0.24250502 lGP.U U 11.614465 #> 214 7.7688166 2.5959623 -1.7181058 0.70975203 lGP.U U 7.768817 #> 215 12.6184168 1.0550836 NA 0.85708282 E.dec.concave dec 12.618417 #> 217 10.6857590 NA NA 0.31873117 L.dec dec 10.685759 #> 218 13.3308965 2.0807712 -2.0437685 0.67930675 lGP.U U 13.330896 #> 220 4.3278401 NA NA 0.64244947 L.inc inc 4.327840 #> 221 23.8696589 0.0000000 -8.8575210 0.31200738 GP.U U 10.754626 #> 222 11.3869012 NA NA 0.25848359 L.dec dec 11.386901 #> 225 11.7554052 1.1188963 NA 0.13641264 E.dec.concave dec 11.755405 #> 228 7.7233560 2.5397977 5.2069816 0.18596550 GP.bell bell 12.616033 #> 229 10.8961840 NA NA 0.27093544 L.dec dec 10.896184 #> 232 -2.6304333 0.0000000 3.7693124 0.45467691 GP.bell bell 3.902563 #> 233 -2.6304333 0.0000000 3.7693124 0.45467691 GP.bell bell 3.902563 #> 235 11.8496013 1.9585039 0.8349853 0.42763624 lGP.bell bell 11.849601 #> 236 10.9187587 0.8633870 NA 0.30698381 E.inc.convex inc 10.918759 #> 237 15.7513805 1.2089428 NA 0.43153316 E.dec.concave dec 15.751380 #> 238 15.7513805 1.2089428 NA 0.43153316 E.dec.concave dec 15.751380 #> 240 15.7513805 1.2089428 NA 0.43153316 E.dec.concave dec 15.751380 #> 242 5.6176186 NA NA 0.71745130 L.inc inc 5.617619 #> 243 11.9083638 2.7394740 1.2475547 0.17108221 GP.bell bell 12.737821 #> 245 8.3098306 1.7832021 NA 0.47444618 E.inc.convex inc 8.309831 #> 246 8.3098306 1.7832021 NA 0.47444618 E.inc.convex inc 8.309831 #> 248 6.9999200 NA NA 0.48572253 L.inc inc 6.999920 #> 251 17.9267166 NA NA 0.19130163 L.inc inc 17.926717 #> 252 17.9267166 NA NA 0.19130163 L.inc inc 17.926717 #> 253 11.5528527 1.7586717 0.9857557 0.27811862 lGP.bell bell 11.552853 #> 255 4.1240469 0.9672252 NA 1.22904641 E.inc.convex inc 4.124047 #> 256 11.2738979 -2.4815554 NA 0.24110024 E.dec.convex dec 11.273898 #> 257 11.1766179 0.9162819 NA 0.22747339 E.dec.concave dec 11.176618 #> 259 10.9147353 NA NA 0.16490391 L.inc inc 10.914735 #> 260 6.4933089 -1.5366759 NA 0.65031263 E.dec.convex dec 6.493309 #> 263 10.5032644 NA NA 0.29248928 L.inc inc 10.503264 #> 265 9.5564865 NA NA 0.18002511 L.inc inc 9.556486 #> 266 5.0551521 -0.5889109 NA 0.72440497 E.dec.convex dec 5.055152 #> 270 29.7823352 0.0000000 -13.8372731 0.77132816 GP.U U 8.816835 #> 272 5.1502968 NA NA 0.49677570 L.inc inc 5.150297 #> 274 13.0468298 -2.5200214 NA 0.13144425 E.dec.convex dec 13.046830 #> 275 11.0973433 2.0754574 NA 0.27234802 E.inc.convex inc 11.097343 #> 276 6.9630481 -2.0722462 NA 0.44005180 E.dec.convex dec 6.963048 #> 277 9.0129913 NA NA 0.28336282 L.inc inc 9.012991 #> 279 6.6904034 1.2899867 NA 0.25309746 E.dec.concave dec 6.690403 #> 280 9.2723423 1.2974934 NA 0.27951792 E.dec.concave dec 9.272342 #> 282 13.4155278 -2.2148393 NA 0.40243907 E.dec.convex dec 13.415528 #> 285 9.3830167 1.9563733 -1.1542378 0.23896903 GP.U U 8.502959 #> 286 3.6770965 NA NA 0.64907732 L.inc inc 3.677097 #> 287 8.2045728 NA NA 0.33406887 L.dec dec 8.204573 #> 288 8.2045728 NA NA 0.33406887 L.dec dec 8.204573 #> 291 4.6285396 NA NA 0.44374643 L.dec dec 4.628540 #> 293 9.8243533 1.9682607 NA 0.21543421 E.inc.convex inc 9.824353 #> 295 8.0476185 NA NA 0.25867705 L.dec dec 8.047618 #> 296 12.4697768 3.3599518 NA 0.26868859 E.dec.concave dec 12.469777 #> 297 11.1166216 NA NA 0.24109408 L.dec dec 11.116622 #> 298 11.1166216 NA NA 0.24109408 L.dec dec 11.116622 #> 299 8.8001787 1.0569847 NA 0.44895951 E.dec.concave dec 8.800179 #> 301 12.5829044 NA NA 0.28459726 L.inc inc 12.582904 #> 303 9.8420421 NA NA 0.14724997 L.dec dec 9.842042 #> 305 12.9786729 0.9062525 NA 0.48389149 E.dec.concave dec 12.978673 #> 306 12.9205516 NA NA 0.29030980 L.dec dec 12.920552 #> 307 12.9205516 NA NA 0.29030980 L.dec dec 12.920552 #> 308 7.5677415 0.9950738 -0.7564927 0.23626721 GP.U U 7.073164 #> 312 12.1685832 2.0515078 0.7474307 0.31596860 lGP.bell bell 12.168583 #> 314 8.8258814 -2.6181633 NA 0.49728328 E.inc.concave inc 8.825881 #> 317 11.2206087 2.3761712 -1.7174779 0.56239213 GP.U U 10.182344 #> 320 6.0844002 3.8714820 2.6450452 0.50826185 GP.bell bell 7.183840 #> 321 6.0844002 3.8714820 2.6450452 0.50826185 GP.bell bell 7.183840 #> 322 9.8322505 -1.0676294 NA 0.23732470 E.dec.convex dec 9.832250 #> 327 4.9468313 -0.7537935 NA 0.38646039 E.dec.convex dec 4.946831 #> 328 7.4334490 1.8152385 NA 0.44755458 E.inc.convex inc 7.433449 #> 330 10.2727203 1.0777063 NA 0.24051502 E.dec.concave dec 10.272720 #> 331 9.5975067 NA NA 0.18071219 L.inc inc 9.597507 #> 332 9.5975067 NA NA 0.18071219 L.inc inc 9.597507 #> 333 7.7633888 0.2414632 0.9158559 0.31711928 GP.bell bell 8.668420 #> 336 25.8775428 0.0000000 -11.8133493 1.05863155 GP.U U 4.430464 #> 338 10.3977914 NA NA 0.31334720 L.inc inc 10.397791 #> 339 2.8818475 1.4831086 0.9310508 0.41140105 lGP.bell bell 2.881847 #> 340 5.4338860 2.2028885 1.3974511 0.27541245 GP.bell bell 6.426716 #> 341 6.3598648 2.1273579 -4.1079331 1.11545022 GP.U U 3.480083 #> 342 2.5445734 NA NA 0.88166215 L.inc inc 2.544573 #> 343 11.5544508 2.3238072 -0.7635036 0.23894341 GP.U U 11.184472 #> 344 10.4291831 -2.6796044 NA 0.37877170 E.dec.convex dec 10.429183 #> 346 9.6726997 NA NA 0.45436857 L.inc inc 9.672700 #> 350 5.5944775 1.7426689 NA 0.51295133 E.inc.convex inc 5.594478 #> 354 5.6099340 1.8216559 NA 0.32521096 E.inc.convex inc 5.609934 #> 357 7.4650252 1.7793179 NA 0.55020692 E.inc.convex inc 7.465025 #> 358 7.1131979 2.2776491 NA 0.30554731 E.dec.concave dec 7.113198 #> 360 7.1131979 2.2776491 NA 0.30554731 E.dec.concave dec 7.113198 #> 361 9.3779405 0.8420820 NA 0.38873834 E.inc.convex inc 9.377940 #> 362 11.5919778 2.1014401 -1.3549521 0.53260300 lGP.U U 11.591978 #> 364 10.0143263 NA NA 0.27962037 L.inc inc 10.014326 #> 365 17.9024313 NA NA 0.17878483 L.dec dec 17.902431 #> 368 13.9040590 1.3987102 NA 0.44692567 E.dec.concave dec 13.904059 #> 370 6.1183998 -2.3714972 NA 0.59801495 E.dec.convex dec 6.118400 #> 372 13.8162214 1.9596785 -3.2185534 0.39523662 GP.U U 11.147673 #> 375 11.8055777 1.3537714 NA 0.50658959 E.inc.convex inc 11.805578 #> 376 7.3479036 NA NA 0.43229217 L.dec dec 7.347904 #> 378 13.3150381 1.2295545 NA 0.21279936 E.dec.concave dec 13.315038 #> 379 9.1311118 -2.2535099 NA 0.26268812 E.inc.concave inc 9.131112 #> 380 10.5378650 0.8143290 NA 0.45401830 E.dec.concave dec 10.537865 #> 384 6.3162507 5.0891995 NA 0.46725727 E.inc.convex inc 6.316251 #> 386 10.0567043 NA NA 0.16441109 L.inc inc 10.056704 #> 387 10.0567043 NA NA 0.16441109 L.inc inc 10.056704 #> 389 5.1148212 1.4084038 NA 1.09855421 E.inc.convex inc 5.114821 #> 391 8.3714801 NA NA 0.25820328 L.dec dec 8.371480 #> 394 9.4154096 3.0079390 NA 0.20173024 E.dec.concave dec 9.415410 #> 395 10.3811233 3.1733712 NA 0.13779241 E.inc.convex inc 10.381123 #> 397 23.7778473 0.0000000 -10.9292695 0.88531156 GP.U U 6.674833 #> 399 7.6939508 1.5471002 0.6661478 0.19149575 GP.bell bell 8.207650 #> 401 8.3205953 2.3566612 1.6122935 0.35325412 GP.bell bell 9.340510 #> 404 13.0053551 1.6480345 NA 0.24599239 E.dec.concave dec 13.005355 #> 405 4.8721779 NA NA 0.43574679 L.inc inc 4.872178 #> 408 12.7509474 2.2781947 -2.6699053 0.24834756 GP.U U 10.429737 #> 409 8.9310280 3.3853270 NA 0.34811954 E.inc.convex inc 8.931028 #> 410 10.8334321 1.3088554 NA 0.28990436 E.dec.concave dec 10.833432 #> 411 14.5173033 1.8299680 -2.9355534 1.00890423 GP.U U 12.797480 #> 414 8.2742116 NA NA 0.31619541 L.dec dec 8.274212 #> 416 15.2527459 2.3845901 -1.0454945 0.25908843 GP.U U 14.589230 #> 419 8.3222889 0.8805994 NA 0.48194682 E.inc.convex inc 8.322289 #> 420 8.4387577 1.1156241 NA 0.37889392 E.inc.convex inc 8.438758 #> 422 3.9580970 NA NA 0.87902801 L.inc inc 3.958097 #> 427 9.9736456 2.3940384 -4.7016243 0.59130862 GP.U U 6.459487 #> 428 9.9736456 2.3940384 -4.7016243 0.59130862 GP.U U 6.459487 #> 430 7.5408033 1.2173378 NA 0.27550558 E.inc.convex inc 7.540803 #> 433 11.7688844 1.0415715 NA 0.21113446 E.dec.concave dec 11.768884 #> 437 7.6097461 NA NA 0.30468219 L.dec dec 7.609746 #> 439 3.4938885 0.8961284 NA 0.99563693 E.inc.convex inc 3.493888 #> 441 3.4938885 0.8961284 NA 0.99563693 E.inc.convex inc 3.493888 #> 443 7.8034297 NA NA 0.65170810 L.inc inc 7.803430 #> 445 12.9287491 -1.3776799 NA 0.29718879 E.dec.convex dec 12.928749 #> 447 9.8736895 1.1503631 NA 0.61355815 E.inc.convex inc 9.873690 #> 450 11.4665408 NA NA 0.24072663 L.dec dec 11.466541 #> 453 11.2188707 1.9158332 0.6101410 0.15859610 GP.bell bell 11.591422 #> 454 11.2188707 1.9158332 0.6101410 0.15859610 GP.bell bell 11.591422 #> 457 9.9160221 1.0820381 NA 0.43570777 E.dec.concave dec 9.916022 #> 458 9.9160221 1.0820381 NA 0.43570777 E.dec.concave dec 9.916022 #> 460 6.1955178 NA NA 0.64907510 L.dec dec 6.195518 #> 461 11.5983183 NA NA 0.24519679 L.inc inc 11.598318 #> 464 11.5369318 NA NA 0.28696334 L.dec dec 11.536932 #> 465 7.6420694 1.9968840 NA 0.29198344 E.inc.convex inc 7.642069 #> 466 13.6080352 2.3215767 NA 0.23573761 E.dec.concave dec 13.608035 #> 467 11.5233287 0.8734262 NA 0.54421378 E.dec.concave dec 11.523329 #> 468 8.8190315 0.7225067 NA 0.17256242 E.dec.concave dec 8.819032 #> 469 6.1391526 3.1550982 -1.2353559 0.46567636 lGP.U U 6.139153 #> 470 15.5969330 NA NA 0.18311723 L.inc inc 15.596933 #> 471 7.1664552 1.8676771 NA 0.53449306 E.dec.concave dec 7.166455 #> 473 14.2054782 NA NA 0.46399131 L.inc inc 14.205478 #> 475 12.7384191 1.6671166 NA 0.51466168 E.inc.convex inc 12.738419 #> 476 7.6278041 -2.0128076 NA 0.32054437 E.inc.concave inc 7.627804 #> 479 18.4632294 NA NA 0.20247498 L.dec dec 18.463229 #> 480 18.4632294 NA NA 0.20247498 L.dec dec 18.463229 #> 481 11.7480225 2.3988301 -1.6451186 0.34020978 GP.U U 10.588494 #> 482 5.8879508 3.3358199 NA 0.44022250 E.inc.convex inc 5.887951 #> 483 9.3907657 NA NA 0.35251384 L.inc inc 9.390766 #> 486 12.5673623 2.0444975 -2.6933806 0.70145770 GP.U U 10.540594 #> 488 4.4751185 -1.3700499 NA 0.65855748 E.inc.concave inc 4.475119 #> 490 8.3079954 -2.3133165 NA 0.42499412 E.dec.convex dec 8.307995 #> 491 8.3079954 -2.3133165 NA 0.42499412 E.dec.convex dec 8.307995 #> 492 10.3737271 1.3213539 NA 0.46734668 E.inc.convex inc 10.373727 #> 493 16.8203565 NA NA 0.24465590 L.dec dec 16.820356 #> 494 3.5216604 1.5925756 1.6525193 0.71149572 lGP.bell bell 3.521660 #> 495 11.1141357 3.0740721 -0.5950246 0.20511033 lGP.U U 11.114136 #> 496 6.9911665 NA NA 0.68942828 L.inc inc 6.991166 #> 498 8.6675517 NA NA 0.23785120 L.inc inc 8.667552 #> 499 8.6675517 NA NA 0.23785120 L.inc inc 8.667552 #> 500 8.8683481 2.2554363 -6.3717984 0.76503106 GP.U U 3.081979 #> 501 11.8312797 2.0127634 -2.1716042 0.35429372 GP.U U 10.482349 #> 502 6.9247347 NA NA 0.53935448 L.inc inc 6.924735 #> 504 5.1107369 2.3273524 NA 0.49190939 E.inc.convex inc 5.110737 #> 505 14.3133366 2.0401909 -2.2874180 0.47711867 GP.U U 12.552174 #> 509 11.6564745 0.8059689 NA 0.34163752 E.dec.concave dec 11.656474 #> 513 2.8568072 1.2403251 NA 0.72920067 E.inc.convex inc 2.856807 #> 514 2.8568072 1.2403251 NA 0.72920067 E.inc.convex inc 2.856807 #> 515 3.7375623 NA NA 0.66526211 L.inc inc 3.737562 #> 517 4.4669724 0.9312615 NA 1.17808168 E.inc.convex inc 4.466972 #> 518 7.7051920 2.7907400 -1.3259250 0.46062229 lGP.U U 7.705192 #> 519 11.8729554 1.6928649 NA 0.36955986 E.inc.convex inc 11.872955 #> 520 5.0903345 1.5288013 NA 0.24985468 E.inc.convex inc 5.090334 #> 521 3.2368169 1.7099772 1.2535246 0.47283855 GP.bell bell 3.614205 #> 522 11.2851985 0.7767753 NA 0.58488759 E.inc.convex inc 11.285199 #> 523 11.1769600 2.5412298 NA 0.24873284 E.dec.concave dec 11.176960 #> 525 3.3180250 1.2611850 0.8999648 0.48348498 lGP.bell bell 3.318025 #> 526 2.9577864 NA NA 0.54268515 L.inc inc 2.957786 #> 527 5.8568962 1.4500622 -1.9460898 0.98274605 lGP.U U 5.856896 #> 528 10.1316456 NA NA 0.28742947 L.inc inc 10.131646 #> 531 6.8771861 0.9719432 NA 0.65133666 E.dec.concave dec 6.877186 #> 534 11.7948434 1.8803822 -1.7229567 0.53837776 GP.U U 10.564068 #> 535 6.6915012 0.9153870 NA 0.99255522 E.inc.convex inc 6.691501 #> 540 4.8284296 2.0830008 NA 0.73289707 E.inc.convex inc 4.828430 #> 542 4.9989594 1.5821319 -1.2090070 0.51207828 lGP.U U 4.998959 #> 543 14.5772553 2.2981412 -1.5915164 0.53119435 GP.U U 13.648609 #> 544 12.2812084 1.0480454 NA 0.31467644 E.dec.concave dec 12.281208 #> 546 2.4794339 NA NA 0.52220615 L.inc inc 2.479434 #> 547 2.6824742 1.2062259 NA 0.82653462 E.inc.convex inc 2.682474 #> 549 4.4432065 1.5956017 -2.0801672 1.01087060 lGP.U U 4.443206 #> 551 3.9219997 -1.4167347 NA 0.80658748 E.inc.concave inc 3.922000 #> 552 2.9837278 NA NA 1.23243335 L.inc inc 2.983728 #> 553 3.2938960 NA NA 0.78506590 L.inc inc 3.293896 #> 554 4.7820339 0.7862608 NA 0.62138156 E.dec.concave dec 4.782034 #> 556 8.9741606 -0.8263232 NA 0.28582133 E.inc.concave inc 8.974161 #> 558 6.9317875 NA NA 0.37680979 L.inc inc 6.931787 #> 559 10.6317327 1.8611164 -1.6585672 0.40393199 GP.U U 9.349429 #> 561 6.2030766 1.3022662 NA 0.31243642 E.dec.concave dec 6.203077 #> 562 6.2030766 1.3022662 NA 0.31243642 E.dec.concave dec 6.203077 #> 565 8.8032367 -2.4187724 NA 0.22248510 E.dec.convex dec 8.803237 #> 566 6.2678143 -1.4799430 NA 0.46551188 E.inc.concave inc 6.267814 #> 567 4.1003451 0.9665863 NA 0.83033880 E.inc.convex inc 4.100345 #> 569 5.2877014 NA NA 0.65093913 L.inc inc 5.287701 #> 571 2.6618273 1.8081921 1.5558322 0.59126123 GP.bell bell 3.733593 #> 572 4.9407625 NA NA 0.47588238 L.inc inc 4.940763 #> 573 11.7181620 NA NA 0.49107177 L.inc inc 11.718162 #> 574 6.8551980 -0.3848874 NA 0.24733881 E.inc.concave inc 6.855198 #> 575 6.0290060 1.1910100 NA 0.60886000 E.inc.convex inc 6.029006 #> 577 6.8208872 2.2045847 -1.9822962 0.65642859 GP.U U 5.532363 #> 579 2.3530681 NA NA 1.29727813 L.inc inc 2.353068 #> 580 2.3530681 NA NA 1.29727813 L.inc inc 2.353068 #> 581 7.8537519 3.5301455 -1.0908882 0.28620983 lGP.U U 7.853752 #> 583 16.1210861 1.6875592 NA 0.25581662 E.inc.convex inc 16.121086 #> 585 6.9329387 1.5675702 NA 0.50617193 E.dec.concave dec 6.932939 #> 586 5.9474858 2.1464196 -1.2818324 0.48811443 lGP.U U 5.947486 #> 587 5.9474858 2.1464196 -1.2818324 0.48811443 lGP.U U 5.947486 #> 588 12.9839302 5.0595658 -2.4186880 0.32894425 GP.U U 12.332673 #> 590 11.8293086 NA NA 1.20927918 L.inc inc 11.829309 #> 591 8.0048304 1.2968103 NA 0.33827142 E.inc.convex inc 8.004830 #> 593 2.7094606 1.8595387 NA 0.73835019 E.inc.convex inc 2.709461 #> 595 7.5037249 NA NA 0.40049288 L.dec dec 7.503725 #> 596 2.6582772 2.6722696 4.3931116 0.45650946 GP.bell bell 5.663831 #> 597 5.2667162 1.7742492 NA 0.45397783 E.dec.concave dec 5.266716 #> 598 8.2084258 -2.0754915 NA 0.30806244 E.inc.concave inc 8.208426 #> 599 8.2084258 -2.0754915 NA 0.30806244 E.inc.concave inc 8.208426 #> 603 3.3517045 NA NA 0.78185445 L.inc inc 3.351704 #> 604 -4.6684484 2.7306565 14.1086676 0.29870979 GP.bell bell 8.700290 #> 606 15.0002172 NA NA 0.53837112 L.inc inc 15.000217 #> 608 7.1764685 1.0238480 NA 0.42350852 E.dec.concave dec 7.176469 #> 610 2.7836841 0.8429907 NA 0.60237813 E.inc.convex inc 2.783684 #> 611 8.7976822 -1.8935670 NA 0.47334309 E.inc.concave inc 8.797682 #> 612 9.5804433 0.9456406 NA 0.19945603 E.inc.convex inc 9.580443 #> 614 9.5804433 0.9456406 NA 0.19945603 E.inc.convex inc 9.580443 #> 615 9.5804433 0.9456406 NA 0.19945603 E.inc.convex inc 9.580443 #> 616 5.1115295 NA NA 0.40444003 L.inc inc 5.111529 #> 617 8.3996060 3.2698204 NA 0.21379500 E.inc.convex inc 8.399606 #> 618 6.0977853 2.1816572 -3.1142421 1.03760938 GP.U U 4.749125 #> 619 6.0977853 2.1816572 -3.1142421 1.03760938 GP.U U 4.749125 #> 620 2.6041848 1.0531930 NA 0.75747748 E.inc.convex inc 2.604185 #> 623 2.7776193 1.5555118 1.7762050 0.50391975 lGP.bell bell 2.777619 #> 624 5.3838933 NA NA 0.89586768 L.inc inc 5.383893 #> 626 5.0070495 2.6958540 NA 0.52415863 E.inc.convex inc 5.007050 #> 630 8.2625336 0.7222172 NA 0.96123411 E.dec.concave dec 8.262534 #> 631 11.8824074 1.7807252 NA 0.31764055 E.dec.concave dec 11.882407 #> 634 6.5549589 NA NA 0.44384894 L.inc inc 6.554959 #> 641 13.3495350 -1.8926198 NA 0.34219661 E.dec.convex dec 13.349535 #> 642 15.6634034 -2.0276704 NA 0.22497205 E.inc.concave inc 15.663403 #> 643 17.0456861 NA NA 0.30817653 L.inc inc 17.045686 #> 644 15.5325419 0.9010627 NA 0.14782250 E.inc.convex inc 15.532542 #> 645 12.3446582 -2.4650317 NA 0.25729560 E.dec.convex dec 12.344658 #> 646 15.7385354 0.8902771 NA 0.15980885 E.inc.convex inc 15.738535 #> yrange maxychange xextrem yextrem BMD.zSD BMR.zSD BMD.xfold #> 1 0.4346034 0.4346034 NA NA 2.2237393 7.219029 NA #> 2 0.4556672 0.4556672 NA NA 0.5279668 5.815850 NA #> 5 0.3498078 0.3498078 NA NA 1.1542677 7.807077 NA #> 6 0.3498078 0.3498078 NA NA 1.1542677 7.807077 NA #> 7 0.3498078 0.3498078 NA NA 1.1542677 7.807077 NA #> 8 0.6010677 0.6010677 NA NA 0.1582542 6.624146 NA #> 9 0.6721023 0.6721023 NA NA 0.1821546 5.919602 0.8318574 #> 10 0.1912790 0.1912790 1.4588204 7.097604 0.7315304 7.218025 NA #> 11 0.4520636 0.4520636 NA NA 0.2528186 6.718211 NA #> 12 0.4392508 0.4392508 NA NA 0.1139635 7.404943 NA #> 13 0.1503987 0.1503987 NA NA 2.9766289 6.409775 NA #> 14 0.4491366 0.4491366 NA NA 2.0261156 5.621033 NA #> 16 0.4771993 0.4771993 NA NA 3.0418937 5.517232 NA #> 17 0.4771993 0.4771993 NA NA 3.0418937 5.517232 NA #> 18 0.3520280 0.3520280 NA NA 5.9997652 5.111507 NA #> 19 0.3520280 0.3520280 NA NA 5.9997652 5.111507 NA #> 20 0.3520280 0.3520280 NA NA 5.9997652 5.111507 NA #> 22 0.2455556 0.2138158 1.4069463 5.081330 0.6597819 4.973670 NA #> 23 0.2455556 0.2138158 1.4069463 5.081330 0.6597819 4.973670 NA #> 25 0.2833937 0.2833937 NA NA 3.8465968 5.334884 NA #> 26 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 27 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 28 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 29 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 30 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 31 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 32 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 33 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 34 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 35 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 36 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 37 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 39 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 40 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 41 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 42 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 43 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 44 0.8109383 0.5779147 1.8678530 5.314907 0.6370853 5.204453 6.6295891 #> 46 0.2284144 0.2284144 NA NA 5.1225625 6.962891 NA #> 47 0.2284144 0.2284144 NA NA 5.1225625 6.962891 NA #> 48 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 49 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 50 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 51 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 52 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 53 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 54 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 55 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 56 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 57 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 58 0.5522909 0.2800374 2.6269663 4.434761 0.8261462 4.582251 NA #> 59 0.3939227 0.3939227 NA NA 2.2111898 5.401608 NA #> 60 0.3939227 0.3939227 NA NA 2.2111898 5.401608 NA #> 61 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 62 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 63 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 64 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 65 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 67 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 68 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 69 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 70 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 71 0.5966741 0.5966741 NA NA 5.7972864 6.318483 NA #> 72 0.5966741 0.5966741 NA NA 5.7972864 6.318483 NA #> 73 0.5966741 0.5966741 NA NA 5.7972864 6.318483 NA #> 75 0.6573939 0.5832748 1.3892912 5.599435 3.9480418 5.376875 7.2728900 #> 76 0.6573939 0.5832748 1.3892912 5.599435 3.9480418 5.376875 7.2728900 #> 77 0.6573939 0.5832748 1.3892912 5.599435 3.9480418 5.376875 7.2728900 #> 79 0.7838623 0.5570660 1.9279453 6.044699 1.8746151 6.044497 NA #> 80 0.7838623 0.5570660 1.9279453 6.044699 1.8746151 6.044497 NA #> 81 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 82 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 83 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 84 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 85 1.4451322 1.4451322 NA NA 1.9131972 10.434091 4.9790105 #> 86 1.4260064 1.4260064 NA NA 0.4667565 12.141363 3.8804658 #> 87 1.3187707 1.3187707 NA NA 0.5356979 12.130711 5.1282913 #> 88 0.5667240 0.5667240 NA NA 5.0725915 16.555837 NA #> 91 0.9321607 0.9321607 NA NA 1.4702189 12.742154 NA #> 95 1.5755952 1.5755952 NA NA 3.7973745 16.088026 NA #> 99 0.8397821 0.8397821 NA NA 2.9983980 14.269939 NA #> 102 0.5072807 0.5072807 NA NA 2.4531384 9.203679 NA #> 103 0.7254029 0.7254029 NA NA 1.1767628 7.538193 6.5436013 #> 104 0.7254029 0.7254029 NA NA 1.1767628 7.538193 6.5436013 #> 106 1.5164596 1.5164596 NA NA 0.5582910 6.835559 0.9033421 #> 109 1.0235083 1.0235083 NA NA 2.4588452 15.481716 NA #> 110 1.0956824 1.0037607 3.1688140 11.302504 0.7285396 10.574734 NA #> 111 1.9146107 1.3629446 2.6438900 4.074770 1.0793925 4.716948 0.8236293 #> 115 2.8622806 2.8622806 NA NA 1.8224349 13.088531 2.8499557 #> 116 0.6953599 0.6953599 NA NA 5.1699097 8.981100 NA #> 117 0.6275920 0.6275920 NA NA 2.1422646 17.527739 NA #> 120 2.3898736 1.6382451 1.5194196 8.501371 4.0482830 10.058898 4.2241830 #> 121 1.2471032 1.2471032 NA NA 1.9341638 12.868353 NA #> 122 2.1130749 2.1130749 NA NA 1.0443056 14.253865 5.1497136 #> 123 2.6271853 1.4457016 2.5679456 11.420407 0.7339797 10.793374 1.6629870 #> 125 0.5298971 0.5298971 NA NA 2.8965322 10.995486 NA #> 127 1.1100339 1.1100339 NA NA 3.7319420 15.355018 NA #> 128 1.1159414 0.6118908 2.9011529 9.264556 1.2030629 9.461066 NA #> 129 1.6230068 1.6230068 NA NA 5.4645388 12.371150 6.3516074 #> 131 2.6773137 2.0535315 1.7419187 6.482804 0.7603447 6.270519 1.3328628 #> 132 2.6773137 2.0535315 1.7419187 6.482804 0.7603447 6.270519 1.3328628 #> 133 1.8696263 1.8696263 1.7108205 6.807665 0.5760685 8.007273 0.6673274 #> 134 1.0214920 0.5111417 2.4191926 10.572500 0.7894809 10.835516 NA #> 137 0.7231739 0.7231739 NA NA 5.9160090 12.734382 NA #> 140 0.6355020 0.6355020 NA NA 2.1358264 11.499543 NA #> 141 0.9825269 0.7772726 1.5731637 10.445494 1.2700689 10.453878 NA #> 142 0.9032233 0.9032233 NA NA 2.0184340 10.336580 NA #> 143 2.6423399 2.6423399 NA NA 2.2280425 7.930351 3.4134579 #> 147 1.3202534 1.3202534 NA NA 4.7401393 17.570230 NA #> 148 1.0314101 1.0314101 NA NA 2.8292974 15.140090 NA #> 149 1.6382243 1.6382243 NA NA 4.6483666 8.355062 5.3321567 #> 150 1.0445220 1.0445220 NA NA 2.0676187 5.988317 3.5948366 #> 152 0.9374300 0.9374300 NA NA 2.7244982 12.150270 NA #> 156 1.4365255 1.4365255 NA NA 2.8183056 4.865309 1.9639952 #> 157 1.6606248 1.6606248 NA NA 0.6668007 8.646709 1.0329475 #> 158 1.3602197 1.3602197 NA NA 1.6366905 12.012784 6.0198387 #> 161 0.7968439 0.5333591 1.8275480 10.728590 4.9242758 11.289442 NA #> 164 0.8762376 0.4930563 2.4622535 14.598622 0.7873949 14.865989 NA #> 165 2.8436971 2.8436971 NA NA 1.5139296 9.859191 2.4503823 #> 166 1.0312987 0.7141166 2.5296882 12.059797 2.0821730 12.036717 NA #> 167 0.7189896 0.7189896 NA NA 1.9091328 11.151566 NA #> 169 1.4213819 1.4213819 NA NA 1.4506148 8.640249 3.8857687 #> 170 1.2475819 0.6844412 2.1869740 10.287496 0.9126300 9.996696 NA #> 171 2.4354311 2.4354311 NA NA 2.2908652 8.270548 2.4809261 #> 172 2.4354311 2.4354311 NA NA 2.2908652 8.270548 2.4809261 #> 174 2.7438223 1.5115754 2.4022793 6.544762 0.9531756 7.044940 1.0251183 #> 178 1.4297124 0.9781543 2.8555099 12.530005 0.8151722 11.964402 NA #> 179 0.8849830 0.8849830 NA NA 1.7627459 13.051530 NA #> 180 0.7551267 0.7551267 NA NA 2.3637907 13.689907 NA #> 181 0.7551267 0.7551267 NA NA 2.3637907 13.689907 NA #> 183 1.2492267 1.2492267 NA NA 0.2370668 7.118030 0.5035207 #> 184 1.3030102 1.3030102 NA NA 2.6853208 6.936227 3.2613039 #> 185 1.3030102 1.3030102 NA NA 2.6853208 6.936227 3.2613039 #> 186 0.9204590 0.9204590 NA NA 5.7121222 8.526747 6.5967242 #> 188 1.3407136 1.3407136 2.3167014 5.508719 1.1232833 4.812898 0.9282272 #> 190 1.0456724 0.5849261 2.5819255 12.953373 1.0452152 12.711281 NA #> 193 1.7942520 1.7942520 1.8809478 4.835845 0.8218053 3.919141 0.5103535 #> 196 2.0216456 2.0216456 1.7372724 10.256965 0.6687799 11.746900 0.9695193 #> 197 0.7772864 0.7772864 NA NA 3.1063283 8.453689 NA #> 198 0.8961317 0.8961317 NA NA 2.8878327 9.960173 NA #> 199 0.8369527 0.8369527 NA NA 3.0767840 10.665710 NA #> 200 0.8369527 0.8369527 NA NA 3.0767840 10.665710 NA #> 201 2.1370159 2.1370159 1.9560222 7.517079 0.9072409 8.504697 0.8197933 #> 202 2.1370159 2.1370159 1.9560222 7.517079 0.9072409 8.504697 0.8197933 #> 203 2.1370159 2.1370159 1.9560222 7.517079 0.9072409 8.504697 0.8197933 #> 205 0.5902918 0.5902918 2.8275360 13.131900 0.6768404 13.520114 NA #> 207 1.3762816 0.9567808 2.1064523 4.156535 0.6470138 3.711818 0.3740702 #> 208 1.6710504 0.9702795 2.5547012 9.167948 1.4222300 9.025390 6.4602636 #> 211 0.6474606 0.6474606 2.6085834 10.967005 0.8877879 11.371960 NA #> 214 1.7181058 1.7181058 2.5959623 6.050711 0.8824251 7.059065 0.9338753 #> 215 2.2364894 2.2364894 NA NA 5.6222008 11.761334 6.0286569 #> 217 0.8034587 0.8034587 NA NA 2.6305103 10.367028 NA #> 218 2.0437685 2.0437685 2.0807712 11.287128 0.6438946 12.651590 1.0021942 #> 220 2.0758668 2.0758668 NA NA 2.0521944 4.970290 1.3824542 #> 221 1.0880222 0.6435345 2.6519769 10.111091 0.7392714 10.442619 NA #> 222 0.7335175 0.7335175 NA NA 2.3366923 11.128418 NA #> 225 0.5248384 0.5248384 NA NA 5.1318609 11.618993 NA #> 228 0.7767741 0.4624698 2.5397977 12.930338 0.9318105 12.801999 NA #> 229 0.9039016 0.9039016 NA NA 1.9875757 10.625249 NA #> 232 0.9872534 0.6272225 2.2864089 4.529785 1.0599593 4.357240 0.8573274 #> 233 0.9872534 0.6272225 2.2864089 4.529785 1.0599593 4.357240 0.8573274 #> 235 0.8349853 0.8349853 1.9585039 12.684587 0.8218319 12.277238 NA #> 236 0.9147026 0.9147026 NA NA 5.6891398 11.225742 NA #> 237 1.5763081 1.5763081 NA NA 5.0780515 15.319847 6.6301060 #> 238 1.5763081 1.5763081 NA NA 5.0780515 15.319847 6.6301060 #> 240 1.5763081 1.5763081 NA NA 5.0780515 15.319847 6.6301060 #> 242 1.5864088 1.5864088 NA NA 2.9988610 6.335070 2.3480977 #> 243 0.7000964 0.4180970 2.7394740 13.155918 0.7253125 12.908904 NA #> 245 0.9360331 0.9360331 NA NA 5.4609223 8.784277 6.4241875 #> 246 0.9360331 0.9360331 NA NA 5.4609223 8.784277 6.4241875 #> 248 1.1881624 1.1881624 NA NA 2.7107625 7.485642 3.9065761 #> 251 0.6196609 0.6196609 NA NA 2.0471215 18.118018 NA #> 252 0.6196609 0.6196609 NA NA 2.0471215 18.118018 NA #> 253 0.9857557 0.9857557 1.7586717 12.538608 0.5508093 11.830971 NA #> 255 3.5343401 3.5343401 NA NA 5.6112409 5.353093 4.5608152 #> 256 0.7471482 0.7471482 NA NA 0.8865055 11.032798 NA #> 257 1.2440698 1.2440698 NA NA 5.0770781 10.949145 6.5328956 #> 259 0.5716752 0.5716752 NA NA 1.9127607 11.079639 NA #> 260 1.5336580 1.5336580 NA NA 0.8327309 5.842996 0.8310632 #> 263 0.6992226 0.6992226 NA NA 2.7737899 10.795754 NA #> 265 0.8804012 0.8804012 NA NA 1.3559120 9.736512 NA #> 266 1.7011398 1.7011398 NA NA 0.3267449 4.330747 0.2076643 #> 270 2.1967634 1.1526784 2.4964781 7.664156 1.0466404 8.045507 1.2705901 #> 272 1.4379270 1.4379270 NA NA 2.2908810 5.647073 2.3750592 #> 274 0.4392427 0.4392427 NA NA 0.8198382 12.915386 NA #> 275 1.0315095 1.0315095 NA NA 4.0915465 11.369691 NA #> 276 1.5599561 1.5599561 NA NA 0.6538412 6.522996 1.1581900 #> 277 0.6744958 0.6744958 NA NA 2.7857533 9.296354 NA #> 279 1.0236475 1.0236475 NA NA 4.8512276 6.437306 6.0863878 #> 280 1.2704995 1.2704995 NA NA 4.6939228 8.992824 6.2252369 #> 282 0.9935949 0.9935949 NA NA 1.0757865 13.013089 NA #> 285 0.9088697 0.6346895 1.9563733 8.228779 1.2951527 8.263990 NA #> 286 1.5377875 1.5377875 NA NA 2.7988468 4.326174 1.5855784 #> 287 0.8865933 0.8865933 NA NA 2.4985646 7.870504 6.1363560 #> 288 0.8865933 0.8865933 NA NA 2.4985646 7.870504 6.1363560 #> 291 1.0796687 1.0796687 NA NA 2.7253570 4.184793 2.8427097 #> 293 0.5863849 0.5863849 NA NA 4.7734831 10.039788 NA #> 295 0.6009495 0.6009495 NA NA 2.8542954 7.788941 NA #> 296 1.0516373 1.0516373 NA NA 3.1885322 12.201088 NA #> 297 0.5732814 0.5732814 NA NA 2.7886737 10.875528 NA #> 298 0.5732814 0.5732814 NA NA 2.7886737 10.875528 NA #> 299 1.6474111 1.6474111 NA NA 5.2621976 8.351219 5.9699880 #> 301 0.7755726 0.7755726 NA NA 2.4332531 12.867502 NA #> 303 0.4170526 0.4170526 NA NA 2.3412265 9.694792 NA #> 305 1.3091568 1.3091568 NA NA 5.7300529 12.494781 6.6231563 #> 306 0.8942070 0.8942070 NA NA 2.1527950 12.630242 NA #> 307 0.8942070 0.8942070 NA NA 2.1527950 12.630242 NA #> 308 0.7564918 0.4945771 0.9950738 6.811249 0.7116013 6.836896 NA #> 312 0.7474307 0.7474307 2.0515078 12.916014 0.7635263 12.484552 NA #> 314 1.9400638 1.9400638 NA NA 0.7046383 9.323165 1.4207090 #> 317 1.3754663 0.6962531 2.3761712 9.503131 1.4872255 9.619952 NA #> 320 1.5861301 1.0776961 2.6022023 7.692274 2.5611897 7.692102 6.0661427 #> 321 1.5861301 1.0776961 2.6022023 7.692274 2.5611897 7.692102 6.0661427 #> 322 0.7486829 0.7486829 NA NA 0.4060345 9.594926 NA #> 327 0.9864697 0.9864697 NA NA 0.3747033 4.560371 0.5245918 #> 328 1.4292787 1.4292787 NA NA 4.6236368 7.881004 5.4871571 #> 330 1.1401586 1.1401586 NA NA 4.9624796 10.032205 6.5188896 #> 331 1.0483855 1.0483855 NA NA 1.1429980 9.778219 6.0703879 #> 332 1.0483855 1.0483855 NA NA 1.1429980 9.778219 6.0703879 #> 333 0.9156340 0.9048096 0.2414632 8.679245 1.7158581 8.351301 4.1888113 #> 336 3.1244464 2.4160958 2.3109888 2.014368 0.5585263 3.371832 0.2148246 #> 338 0.9549307 0.9549307 NA NA 2.1758702 10.711139 NA #> 339 0.9310508 0.9310508 1.4831086 3.812898 0.5958514 3.293249 0.4972954 #> 340 1.0463324 0.6417115 2.2028885 6.831337 1.0293193 6.702129 NA #> 341 3.2718424 2.0436913 2.1273579 2.251932 1.5320368 2.364633 0.3746104 #> 342 1.8857419 1.8857419 NA NA 3.1002661 3.426236 0.8947707 #> 343 0.7001329 0.3935249 2.3238072 10.790947 1.0252456 10.945529 NA #> 344 1.1746841 1.1746841 NA NA 0.9378058 10.050411 4.4938667 #> 346 1.5034330 1.5034330 NA NA 2.0040254 10.127068 4.2662140 #> 350 1.2917549 1.2917549 NA NA 5.0794294 6.107429 5.2227689 #> 354 0.9567759 0.9567759 NA NA 4.7558343 5.935145 5.6919180 #> 357 3.5130625 3.5130625 NA NA 3.5491119 8.015232 4.0271629 #> 358 2.0350385 2.0350385 NA NA 2.9236395 6.807651 4.4565040 #> 360 2.0350385 2.0350385 NA NA 2.9236395 6.807651 4.4565040 #> 361 1.0233370 1.0233370 NA NA 5.8164566 9.766679 6.5575207 #> 362 1.3549521 1.3549521 2.1014401 10.237026 0.8960631 11.059375 1.4831675 #> 364 0.7991327 0.7991327 NA NA 2.3202186 10.293947 NA #> 365 0.4818421 0.4818421 NA NA 2.4603955 17.723646 NA #> 368 2.0649632 2.0649632 NA NA 4.5338399 13.457133 6.0836994 #> 370 1.6440213 1.6440213 NA NA 0.9909542 5.520385 1.0195641 #> 372 2.1088211 1.5588160 1.9596785 10.597668 0.9547622 10.752436 5.8228192 #> 375 1.5309438 1.5309438 NA NA 5.1540813 12.312167 6.2821526 #> 376 1.0939749 1.0939749 NA NA 2.6202881 6.915611 4.4538453 #> 378 0.4899501 0.4899501 NA NA 5.6128703 13.102239 NA #> 379 0.6274954 0.6274954 NA NA 1.1382454 9.393800 NA #> 380 1.0448656 1.0448656 NA NA 5.9525599 10.083847 NA #> 384 3.3807780 3.3807780 NA NA 1.6037111 6.783508 2.0659663 #> 386 0.5490811 0.5490811 NA NA 1.9855173 10.221115 NA #> 387 0.5490811 0.5490811 NA NA 1.9855173 10.221115 NA #> 389 3.0879871 3.0879871 NA NA 5.1982036 6.213375 4.1613342 #> 391 0.7062619 0.7062619 NA NA 2.4242366 8.113277 NA #> 394 0.7998367 0.7998367 NA NA 3.3387424 9.213679 NA #> 395 1.4755404 1.4755404 NA NA 1.6108049 10.518916 5.6764686 #> 397 2.0091568 1.0916682 2.4592236 5.583165 1.3712891 5.789522 0.9112142 #> 399 0.6258898 0.4734413 1.5471002 8.360099 4.1335653 8.016154 NA #> 401 1.2548501 0.6624716 2.3566612 9.932889 0.9613178 9.693764 NA #> 404 0.8939560 0.8939560 NA NA 4.5803310 12.759363 NA #> 405 1.2111865 1.2111865 NA NA 2.3856251 5.307925 2.6674183 #> 408 1.0680948 0.7193997 2.2781947 10.081042 1.0862987 10.181390 NA #> 409 1.8924337 1.8924337 NA NA 2.5443588 9.279148 4.5849528 #> 410 1.0002044 1.0002044 NA NA 5.0301698 10.543528 NA #> 411 2.8615248 1.6457951 1.8299680 11.581750 1.1535189 11.788575 5.2775387 #> 414 0.8811392 0.8811392 NA NA 2.3795238 7.958016 6.2267455 #> 416 0.7982567 0.4162785 2.3845901 14.207251 1.1340646 14.330141 NA #> 419 1.6551785 1.6551785 NA NA 5.5456395 8.804236 6.0260053 #> 420 1.0612794 1.0612794 NA NA 5.4871921 8.817652 6.3760243 #> 422 3.5996821 3.5996821 NA NA 1.6192638 4.837125 0.7291239 #> 427 2.8125159 1.6250497 2.3940384 5.272021 0.7601546 5.868179 0.8418771 #> 428 2.8125159 1.6250497 2.3940384 5.272021 0.7601546 5.868179 0.8418771 #> 430 0.7148534 0.7148534 NA NA 5.4786411 7.816309 NA #> 433 0.8109830 0.8109830 NA NA 5.2343760 11.557750 NA #> 437 0.8412667 0.8412667 NA NA 2.4015542 7.305064 5.9981247 #> 439 2.8504330 2.8504330 NA NA 5.6894334 4.489525 4.7539040 #> 441 2.8504330 2.8504330 NA NA 5.6894334 4.489525 4.7539040 #> 443 1.9375683 1.9375683 NA NA 2.2303608 8.455138 2.6705919 #> 445 0.8217456 0.8217456 NA NA 0.6120840 12.631560 NA #> 447 1.5447062 1.5447062 NA NA 5.5743185 10.487248 6.1181961 #> 450 1.2515303 1.2515303 NA NA 1.2754451 11.225814 6.0753329 #> 453 0.5794020 0.3418123 1.9158332 11.829012 0.9001452 11.750018 NA #> 454 0.5794020 0.3418123 1.9158332 11.829012 0.9001452 11.750018 NA #> 457 0.9890126 0.9890126 NA NA 5.7470070 9.480314 NA #> 458 0.9890126 0.9890126 NA NA 5.7470070 9.480314 NA #> 460 1.7150692 1.7150692 NA NA 2.5095297 5.546443 2.3953832 #> 461 0.6495001 0.6495001 NA NA 2.5033097 11.843515 NA #> 464 0.6734981 0.6734981 NA NA 2.8253294 11.249968 NA #> 465 0.8555082 0.8555082 NA NA 4.6189505 7.934053 6.4142382 #> 466 0.5824151 0.5824151 NA NA 4.7196194 13.372298 NA #> 467 1.4382279 1.4382279 NA NA 5.7829063 10.979115 6.4375378 #> 468 0.8953279 0.8953279 NA NA 5.4417552 8.646469 6.6200856 #> 469 1.2353559 1.2353559 3.1550982 4.903797 1.3227556 5.673476 1.5114489 #> 470 0.4869360 0.4869360 NA NA 2.4936551 15.780050 NA #> 471 1.3494676 1.3494676 NA NA 4.9812861 6.631962 5.4957409 #> 473 1.3750723 1.3750723 NA NA 2.2375015 14.669470 NA #> 475 1.2652916 1.2652916 NA NA 5.1762835 13.253081 NA #> 476 0.9041080 0.9041080 NA NA 0.8406026 7.948348 3.3682176 #> 479 0.5719003 0.5719003 NA NA 2.3476324 18.260754 NA #> 480 0.5719003 0.5719003 NA NA 2.3476324 18.260754 NA #> 481 1.0913351 0.6057451 2.3988301 10.102904 1.1650959 10.248284 NA #> 482 1.8038011 1.8038011 NA NA 3.1061545 6.328173 3.7267989 #> 483 0.7929511 0.7929511 NA NA 2.9478731 9.743279 NA #> 486 2.0494969 1.3828849 2.0444975 9.873982 5.2731825 11.242051 5.9137826 #> 488 1.7795910 1.7795910 NA NA 0.6267952 5.133676 0.3931901 #> 490 1.5018619 1.5018619 NA NA 0.7181485 7.883001 1.7061269 #> 491 1.5018619 1.5018619 NA NA 0.7181485 7.883001 1.7061269 #> 492 0.9936782 0.9936782 NA NA 5.6440554 10.841074 NA #> 493 0.7003740 0.7003740 NA NA 2.3163527 16.575701 NA #> 494 1.6525193 1.6525193 1.5925756 5.174180 0.5751369 4.233156 0.4008074 #> 495 0.5950246 0.5950246 3.0740721 10.519111 0.8381449 10.909025 NA #> 496 1.4582159 1.4582159 NA NA 3.1350631 7.680595 3.1791194 #> 498 0.7529821 0.7529821 NA NA 2.0945933 8.905403 NA #> 499 0.7529821 0.7529821 NA NA 2.0945933 8.905403 NA #> 500 1.9384481 1.3530192 2.2554363 2.496550 5.8011356 3.847010 0.7231269 #> 501 1.9945506 1.1718772 2.0127634 9.659675 0.5750563 10.128055 6.1142017 #> 502 1.1588701 1.1588701 NA NA 3.0861609 7.464089 3.9623005 #> 504 1.4049630 1.4049630 NA NA 4.4260549 5.602646 4.5019378 #> 505 1.6786893 1.1524342 2.0401909 12.025919 1.4522317 12.075055 NA #> 509 1.1207480 1.1207480 NA NA 5.6739990 11.314837 NA #> 513 1.6477686 1.6477686 NA NA 5.6272766 3.586008 4.4854464 #> 514 1.6477686 1.6477686 NA NA 5.6272766 3.586008 4.4854464 #> 515 1.4557079 1.4557079 NA NA 3.0303834 4.402824 1.7025240 #> 517 2.7369191 2.7369191 NA NA 5.8469929 5.645054 4.9467474 #> 518 1.3259250 1.3259250 2.7907400 6.379267 0.6026376 7.244570 0.9306075 #> 519 0.9527003 0.9527003 NA NA 5.0802181 12.242515 NA #> 520 0.7702132 0.7702132 NA NA 4.9509541 5.340189 6.0080600 #> 521 1.2534643 0.8761367 1.7099772 4.490342 0.7375741 4.087043 0.5751990 #> 522 1.2238207 1.2238207 NA NA 6.0576628 11.870086 6.5680391 #> 523 2.3341764 2.3341764 NA NA 2.1622238 10.928227 4.9554389 #> 525 0.8999648 0.8999648 1.2611850 4.217990 0.5657408 3.801510 0.4566400 #> 526 1.2614322 1.2614322 NA NA 2.8527457 3.500472 1.5548265 #> 527 1.9460898 1.9460898 1.4500622 3.910806 0.4011651 4.874150 0.2639840 #> 528 0.8116440 0.8116440 NA NA 2.3482523 10.419075 NA #> 531 3.3631722 3.3631722 NA NA 5.0398424 6.225849 5.0923826 #> 534 1.5216846 1.0295037 1.8803822 10.071887 4.9758852 11.102445 NA #> 535 2.2285391 2.2285391 NA NA 5.8914318 7.684056 5.5312271 #> 540 3.1308885 3.1308885 NA NA 3.8712302 5.561327 3.1637050 #> 542 1.2090070 1.2090070 1.5821319 3.789952 0.6363127 4.486881 0.6282973 #> 543 1.3570049 0.6941344 2.2981412 12.985739 1.3780037 13.117415 NA #> 544 1.4017054 1.4017054 NA NA 5.0717760 11.966532 6.4927085 #> 546 1.2344106 1.2344106 NA NA 2.8051842 3.001640 1.3319009 #> 547 2.6339438 2.6339438 NA NA 5.2437498 3.509009 3.9184068 #> 549 2.0801672 2.0801672 1.5956017 2.363039 0.4677758 3.432336 0.2651867 #> 551 1.7353224 1.7353224 NA NA 0.8742707 4.728587 0.3591303 #> 552 3.1999446 3.1999446 NA NA 2.5538772 4.216161 0.6182950 #> 553 2.0616360 2.0616360 NA NA 2.5250685 4.078962 1.0594414 #> 554 1.5426292 1.5426292 NA NA 5.9163079 4.160652 5.7105061 #> 556 0.6086139 0.6086139 NA NA 0.5237934 9.259982 NA #> 558 0.9495953 0.9495953 NA NA 2.6312532 7.308597 4.8404495 #> 559 1.3525467 0.9762831 1.8611164 8.973165 4.7863633 9.753361 6.4487178 #> 561 1.1047895 1.1047895 NA NA 5.0063684 5.890640 5.8855757 #> 562 1.1047895 1.1047895 NA NA 5.0063684 5.890640 5.8855757 #> 565 0.7082924 0.7082924 NA NA 0.8415993 8.580752 NA #> 566 1.1161200 1.1161200 NA NA 0.7867886 6.733326 1.1989832 #> 567 2.4773277 2.4773277 NA NA 5.5764311 4.930684 4.8975036 #> 569 1.5364463 1.5364463 NA NA 2.8093253 5.938641 2.2820679 #> 571 1.4460633 0.9619972 1.8081921 4.217660 5.0186649 3.142332 1.0034930 #> 572 1.9620447 1.9620447 NA NA 1.6083100 5.416645 1.6697987 #> 573 1.2941718 1.2941718 NA NA 2.5161241 12.209234 6.0040817 #> 574 0.6072923 0.6072923 NA NA 0.2013097 7.102537 NA #> 575 1.3284196 1.3284196 NA NA 5.7071899 6.637866 5.6955727 #> 577 1.6331559 0.9393843 2.2045847 4.838591 1.7413609 4.875934 1.2937236 #> 579 2.7957572 2.7957572 NA NA 3.0768950 3.650346 0.5581026 #> 580 2.7957572 2.7957572 NA NA 3.0768950 3.650346 0.5581026 #> 581 1.0908882 1.0908882 3.5301455 6.762864 1.0281330 7.567542 1.9155915 #> 583 1.1239704 1.1239704 NA NA 4.2421167 16.376903 NA #> 585 1.0866179 1.0866179 NA NA 5.4593968 6.426767 5.9394660 #> 586 1.2818324 1.2818324 2.1464196 4.665653 0.7105856 5.459371 0.8008469 #> 587 1.2818324 1.2818324 2.1464196 4.665653 0.7105856 5.459371 0.8008469 #> 588 1.1820174 0.7790280 2.9011092 11.929683 1.8336510 12.003729 NA #> 590 3.5641413 3.5641413 NA NA 2.2498351 13.038588 2.2008147 #> 591 1.6381642 1.6381642 NA NA 4.6149428 8.343102 5.7104719 #> 593 1.5961529 1.5961529 NA NA 5.2575129 3.447811 3.5740658 #> 595 1.2637546 1.2637546 NA NA 2.1014113 7.103232 3.9372516 #> 596 2.4832543 1.3875575 2.6722696 7.051389 0.5554829 6.120341 0.6992043 #> 597 1.8241896 1.8241896 NA NA 4.2864651 4.812738 4.5279917 #> 598 0.7835917 0.7835917 NA NA 0.9822553 8.516488 NA #> 599 0.7835917 0.7835917 NA NA 0.9822553 8.516488 NA #> 603 2.5521288 2.5521288 NA NA 2.0314323 4.133559 0.8708476 #> 604 1.4684527 0.7399295 2.7306565 9.440219 0.6335929 8.998999 NA #> 606 1.3048116 1.3048116 NA NA 2.7359803 15.538588 NA #> 608 1.5003885 1.5003885 NA NA 5.3399279 6.752960 5.8776276 #> 610 1.5629901 1.5629901 NA NA 5.8277486 3.386062 5.1779858 #> 611 1.7713152 1.7713152 NA NA 0.5680459 9.271025 1.2444712 #> 612 0.7519149 0.7519149 NA NA 5.3784638 9.779899 NA #> 614 0.7519149 0.7519149 NA NA 5.3784638 9.779899 NA #> 615 0.7519149 0.7519149 NA NA 5.3784638 9.779899 NA #> 616 1.1123253 1.1123253 NA NA 2.4110229 5.515970 3.0471797 #> 617 1.0347975 1.0347975 NA NA 2.8121975 8.613401 6.0472213 #> 618 3.0183770 1.7655821 2.1816572 2.983543 0.9510929 3.711516 0.4370000 #> 619 3.0183770 1.7655821 2.1816572 2.983543 0.9510929 3.711516 0.4370000 #> 620 2.0074095 2.0074095 NA NA 5.6077502 3.361662 4.4929978 #> 623 2.2144941 2.2144941 1.6229804 4.992113 1.0642503 3.281539 0.9871425 #> 624 2.0734706 2.0734706 NA NA 2.8650025 6.279761 1.7217797 #> 626 1.7075076 1.7075076 NA NA 3.9228053 5.531208 3.8287058 #> 630 2.2773688 2.2773688 NA NA 6.0081475 7.301299 5.8988929 #> 631 1.6071190 1.6071190 NA NA 3.9104454 11.564767 6.1083670 #> 634 1.3536434 1.3536434 NA NA 2.1742522 6.998808 3.2110326 #> 641 1.4448595 1.4448595 NA NA 0.4939544 13.007338 4.2861144 #> 642 0.5907318 0.5907318 NA NA 0.9251921 15.888375 NA #> 643 0.7444761 0.7444761 NA NA 2.7449083 17.353863 NA #> 644 0.7667073 0.7667073 NA NA 5.1501661 15.680364 NA #> 645 1.3303544 1.3303544 NA NA 0.4900168 12.087363 4.9350083 #> 646 0.8000254 0.8000254 NA NA 5.1991367 15.898344 NA #> BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper #> 1 6.609214 0.97850954 4.0686985 Inf Inf #> 2 5.347706 0.20008806 1.1095586 Inf Inf #> 5 7.073198 0.75185882 1.4649978 Inf Inf #> 6 7.073198 0.75185882 1.4649978 Inf Inf #> 7 7.073198 0.75185882 1.4649978 Inf Inf #> 8 6.172119 0.05543773 0.6804425 0.561154372 Inf #> 9 5.588358 0.08095270 0.7936032 0.329293169 Inf #> 10 8.017772 0.42468408 1.0520363 Inf Inf #> 11 6.181671 0.07579775 0.7005182 Inf Inf #> 12 6.802633 0.03694799 0.4209217 Inf Inf #> 13 5.820719 1.67433198 5.3037292 Inf Inf #> 14 5.164472 1.25236329 2.8522870 7.563758934 Inf #> 16 5.863188 1.32631865 6.0595553 5.925234838 Inf #> 17 5.863188 1.32631865 6.0595553 5.925234838 Inf #> 18 5.465323 2.76071129 7.1933590 7.679066254 Inf #> 19 5.465323 2.76071129 7.1933590 7.679066254 Inf #> 20 5.465323 2.76071129 7.1933590 7.679066254 Inf #> 22 4.380763 0.36442365 2.2863213 Inf Inf #> 23 4.380763 0.36442365 2.2863213 Inf Inf #> 25 4.862095 1.87728956 5.7928776 Inf Inf #> 26 4.425610 0.63687870 2.6421628 1.607320792 Inf #> 27 4.425610 0.63687870 2.6421628 1.607320792 Inf #> 28 4.425610 0.63687870 2.6421628 1.607320792 Inf #> 29 4.425610 0.63687870 2.6421628 1.607320792 Inf #> 30 4.425610 0.63687870 2.6421628 1.607320792 Inf #> 31 5.860923 0.37773418 3.4329037 2.120911243 Inf #> 32 5.860923 0.37773418 3.4329037 2.120911243 Inf #> 33 5.860923 0.37773418 3.4329037 2.120911243 Inf #> 34 5.860923 0.37773418 3.4329037 2.120911243 Inf #> 35 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 36 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 37 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 39 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 40 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 41 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 42 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 43 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 44 4.573695 0.32686514 3.1332891 5.738912687 Inf #> 46 6.316887 2.43380228 6.3223929 Inf Inf #> 47 6.316887 2.43380228 6.3223929 Inf Inf #> 48 4.940865 0.35799216 4.2882933 2.320483615 Inf #> 49 4.940865 0.35799216 4.2882933 2.320483615 Inf #> 50 4.940865 0.35799216 4.2882933 2.320483615 Inf #> 51 4.940865 0.35799216 4.2882933 2.320483615 Inf #> 52 5.737279 0.30086446 3.1833272 1.307410467 Inf #> 53 5.737279 0.30086446 3.1833272 1.307410467 Inf #> 54 5.737279 0.30086446 3.1833272 1.307410467 Inf #> 55 5.737279 0.30086446 3.1833272 1.307410467 Inf #> 56 5.737279 0.30086446 3.1833272 1.307410467 Inf #> 57 5.737279 0.30086446 3.1833272 1.307410467 Inf #> 58 5.177716 0.46124416 1.4873687 Inf Inf #> 59 4.962470 1.03576992 3.6179409 Inf Inf #> 60 4.962470 1.03576992 3.6179409 Inf Inf #> 61 5.158766 0.48406738 1.2370211 0.808097974 Inf #> 62 5.158766 0.48406738 1.2370211 0.808097974 Inf #> 63 5.158766 0.48406738 1.2370211 0.808097974 Inf #> 64 5.158766 0.48406738 1.2370211 0.808097974 Inf #> 65 5.158766 0.48406738 1.2370211 0.808097974 Inf #> 67 6.677576 0.26644454 4.2871164 Inf Inf #> 68 6.677576 0.26644454 4.2871164 Inf Inf #> 69 6.677576 0.26644454 4.2871164 Inf Inf #> 70 6.677576 0.26644454 4.2871164 Inf Inf #> 71 5.840236 3.09334793 6.6162300 7.323353364 Inf #> 72 5.840236 3.09334793 6.6162300 7.323353364 Inf #> 73 5.840236 3.09334793 6.6162300 7.323353364 Inf #> 75 4.972785 0.63064808 4.7996149 5.945710618 Inf #> 76 4.972785 0.63064808 4.7996149 5.945710618 Inf #> 77 4.972785 0.63064808 4.7996149 5.945710618 Inf #> 79 5.236113 0.52106128 5.9739107 5.919751612 Inf #> 80 5.236113 0.52106128 5.9739107 5.919751612 Inf #> 81 5.980553 0.42901273 4.3080978 5.248292886 Inf #> 82 5.980553 0.42901273 4.3080978 5.248292886 Inf #> 83 5.980553 0.42901273 4.3080978 5.248292886 Inf #> 84 5.980553 0.42901273 4.3080978 5.248292886 Inf #> 85 9.765941 1.25476148 2.7589141 3.944767854 Inf #> 86 11.185391 0.24296501 0.8254193 2.323655028 Inf #> 87 11.170683 0.28244583 0.9252443 2.789277177 Inf #> 88 18.051589 2.64963205 5.5727686 Inf Inf #> 91 11.653948 1.04939875 1.9503267 Inf Inf #> 95 17.349849 1.99350628 5.0456881 6.137589248 Inf #> 99 13.184704 1.71972684 5.2546852 Inf Inf #> 102 8.452213 1.52079518 3.8197300 Inf Inf #> 103 7.959897 0.46760570 2.8942164 1.944016318 Inf #> 104 7.959897 0.46760570 2.8942164 1.944016318 Inf #> 106 6.609855 0.21744945 1.5471627 0.428071765 2.5957963 #> 109 16.612407 1.54015368 3.7311047 Inf Inf #> 110 9.268869 0.37327629 1.2105842 1.951273142 Inf #> 111 4.893943 0.66647676 1.8535634 0.596011289 1.4002372 #> 115 13.532062 1.25523264 2.4785285 2.184471756 4.1050629 #> 116 8.370532 2.62074447 6.1892385 6.399079578 Inf #> 117 19.057483 1.33538267 3.2723752 Inf Inf #> 120 10.178300 0.49685136 4.8689356 0.720186659 5.3855908 #> 121 11.774346 1.07799896 3.2324908 6.134109074 Inf #> 122 13.018466 0.65089363 1.4477020 4.613686534 5.6633468 #> 123 11.262816 0.39650460 1.0210325 0.935884812 6.2063487 #> 125 11.840420 1.64980574 4.7578654 Inf Inf #> 127 16.661441 2.10623596 4.9747503 Inf Inf #> 128 10.745467 0.45072040 4.9073419 5.535135720 Inf #> 129 13.115891 2.95611239 5.6794781 5.141147897 6.5072131 #> 131 6.444924 0.37828212 3.9147635 0.641720795 4.6921648 #> 132 6.444924 0.37828212 3.9147635 0.641720795 4.6921648 #> 133 7.809562 0.28329273 0.9217664 0.371304357 1.0567280 #> 134 12.192005 0.45119793 1.3369379 Inf Inf #> 137 13.697747 2.88540036 6.0230168 Inf Inf #> 140 12.424335 1.39985045 3.1165736 Inf Inf #> 141 11.715823 0.46705147 4.3039298 Inf Inf #> 142 9.550364 1.05386556 3.3648968 5.999140808 Inf #> 143 8.280927 1.21655973 3.4294082 2.333290039 4.5329127 #> 147 16.074408 2.74155251 5.3503657 Inf Inf #> 148 16.170012 1.72942685 4.9473537 6.574719043 Inf #> 149 8.613130 2.33640966 5.5962017 3.329265202 6.0213409 #> 150 6.228886 1.30775683 3.0619063 2.633213483 5.1889529 #> 152 11.281891 1.68457955 4.6751632 6.392129679 Inf #> 156 4.680233 1.62293691 5.9759268 1.316830682 3.9749306 #> 157 8.874974 0.27525816 1.6705374 0.448004236 2.7783783 #> 158 11.113667 1.07953096 2.3571861 4.876575034 Inf #> 161 12.091282 0.60384236 5.6978525 Inf Inf #> 164 16.600846 0.48326750 1.0560307 Inf Inf #> 165 9.457594 1.02570209 1.9393894 2.006597935 3.1213521 #> 166 10.568354 0.76647179 5.2718059 Inf Inf #> 167 12.039017 1.17918063 2.7286462 Inf Inf #> 169 9.162234 0.96799087 1.9783744 3.147459144 5.1690481 #> 170 8.642749 0.50907354 5.1415268 1.604331864 Inf #> 171 8.200742 1.50525257 3.4011695 1.955152886 3.7737373 #> 172 8.200742 1.50525257 3.4011695 1.955152886 3.7737373 #> 174 6.999308 0.53075189 4.6854038 0.666553386 5.0930986 #> 178 10.396666 0.53046359 1.1464625 1.683797922 Inf #> 179 11.958109 1.14853940 2.4913773 Inf Inf #> 180 12.563182 1.54875481 3.5028687 Inf Inf #> 181 12.563182 1.54875481 3.5028687 Inf Inf #> 183 7.409795 0.08183997 0.5980861 0.191250605 1.6257035 #> 184 7.049409 1.75885065 4.3783442 2.228962042 5.6185328 #> 185 7.049409 1.75885065 4.3783442 2.228962042 5.6185328 #> 186 8.010933 2.93461899 6.0719877 5.699704509 Inf #> 188 4.584806 0.50278548 1.8172104 0.398838259 1.5901895 #> 190 11.131602 0.58190671 1.6479060 Inf Inf #> 193 3.345752 0.35654678 1.4592305 0.166561570 1.1565046 #> 196 11.050749 0.43301683 0.8971572 0.740395116 1.2380754 #> 197 7.936032 1.74049030 5.8493362 5.058661772 Inf #> 198 9.315398 1.73688931 5.1197523 5.388840084 Inf #> 199 11.305100 1.60498652 5.3246257 5.203053760 Inf #> 200 11.305100 1.60498652 5.3246257 5.203053760 Inf #> 201 8.688686 0.35183367 1.7316828 0.362628186 1.4953577 #> 202 8.688686 0.35183367 1.7316828 0.362628186 1.4953577 #> 203 8.688686 0.35183367 1.7316828 0.362628186 1.4953577 #> 205 15.094411 0.22352954 1.1815352 Inf Inf #> 207 3.519730 0.29910845 1.8306631 0.199774284 1.4325785 #> 208 7.620460 0.57763293 5.4940389 1.085545152 Inf #> 211 12.775912 0.41079211 1.5942930 Inf Inf #> 214 6.991935 0.36258894 1.5883451 0.452166289 1.6600473 #> 215 11.356575 2.93270043 5.9633443 4.127378018 6.3349736 #> 217 9.617183 1.66564175 4.0856814 6.348263101 Inf #> 218 11.997807 0.32329297 1.0469995 0.647250519 1.6091792 #> 220 4.760624 1.37853596 3.1602333 1.011869465 2.2765836 #> 221 11.830089 0.31337952 1.3891586 Inf Inf #> 222 10.248211 1.43903732 3.7180841 Inf Inf #> 225 10.579865 2.77040545 5.6067950 Inf Inf #> 228 11.354430 0.48380870 1.5400128 Inf Inf #> 229 9.806566 1.26495795 3.0819744 6.038177033 Inf #> 232 4.292819 0.40044672 6.5720202 0.372516223 5.9756156 #> 233 4.292819 0.40044672 6.5720202 0.372516223 5.9756156 #> 235 10.664641 0.31820753 1.5828158 1.301585711 Inf #> 236 12.010635 2.82937876 5.8296064 6.441719601 Inf #> 237 14.176242 2.74132244 5.6217506 6.059639702 Inf #> 238 14.176242 2.74132244 5.6217506 6.059639702 Inf #> 240 14.176242 2.74132244 5.6217506 6.059639702 Inf #> 242 6.179380 1.79369036 5.2099565 1.536556109 4.4694075 #> 243 11.464039 0.45259236 0.9852054 Inf Inf #> 245 9.140814 2.60802191 6.3443547 4.773931693 Inf #> 246 9.140814 2.60802191 6.3443547 4.773931693 Inf #> 248 7.699912 1.67808514 5.1396151 2.700837923 Inf #> 251 19.719388 1.32526215 3.0171899 Inf Inf #> 252 19.719388 1.32526215 3.0171899 Inf Inf #> 253 10.397567 0.27487668 0.8684603 1.202020409 Inf #> 255 4.536452 3.02720467 5.8125246 1.185965587 4.9408312 #> 256 10.146508 0.34554032 1.8031346 Inf Inf #> 257 10.058956 3.07025121 5.3715482 6.218567131 Inf #> 259 12.006209 1.26617938 2.6694875 Inf Inf #> 260 5.843978 0.30500837 2.5122396 0.378730892 2.5190324 #> 263 11.553591 1.79186086 4.7606571 Inf Inf #> 265 10.512135 1.00581659 1.7217125 5.981752578 Inf #> 266 4.549637 0.11229115 1.1562790 0.089201977 0.6082146 #> 270 7.935151 0.36344217 4.8271636 0.571262850 6.0547691 #> 272 5.665327 1.54004500 3.3695645 1.714682866 3.6378978 #> 274 11.742147 0.33666389 1.6530558 Inf Inf #> 275 12.207078 2.02820057 5.2593819 6.170577485 Inf #> 276 6.266743 0.27957255 1.4053942 0.623857241 2.4264491 #> 277 9.914290 1.67314034 4.8813673 6.260515221 Inf #> 279 6.021363 2.57101943 5.4991451 5.108155638 6.3704832 #> 280 8.345108 2.69574649 5.3067808 5.531666056 6.4733040 #> 282 12.073975 0.40904089 2.6783910 5.386193008 Inf #> 285 9.353255 0.52323314 5.1004381 6.448319399 Inf #> 286 4.044806 1.57343097 4.8340198 1.004095616 2.8864003 #> 287 7.384116 1.60569926 3.5771123 4.539553747 Inf #> 288 7.384116 1.60569926 3.5771123 4.539553747 Inf #> 291 4.165686 1.61304816 4.7440724 2.054933805 4.8155223 #> 293 10.806789 2.28426943 5.7598313 Inf Inf #> 295 7.242857 1.83136398 4.8498220 6.283356411 Inf #> 296 11.222799 1.61923981 4.8209580 6.446861410 Inf #> 297 10.004959 1.75515628 4.9422872 Inf Inf #> 298 10.004959 1.75515628 4.9422872 Inf Inf #> 299 7.920161 2.73919695 5.6174409 4.537291440 6.2201378 #> 301 13.841195 1.57349911 3.9836203 Inf Inf #> 303 8.857838 1.45426811 3.6196180 Inf Inf #> 305 11.680806 2.74748436 5.9480926 5.959090533 Inf #> 306 11.628496 1.31584676 3.3142276 Inf Inf #> 307 11.628496 1.31584676 3.3142276 Inf Inf #> 308 7.780480 0.29506317 3.0199956 4.254602701 Inf #> 312 10.951725 0.32210163 1.4464539 Inf Inf #> 314 9.708470 0.31620212 1.3244315 0.720852189 2.8039243 #> 317 11.200578 0.68145620 6.3611319 1.486068665 Inf #> 320 6.465456 0.51980328 5.7343561 0.917012370 6.5546099 #> 321 6.465456 0.51980328 5.7343561 0.917012370 6.5546099 #> 322 8.849025 0.14938717 1.0207350 Inf Inf #> 327 4.452148 0.11967646 1.0401929 0.195348516 1.6577848 #> 328 8.176794 2.15278536 5.5732310 3.537939516 6.1536307 #> 330 9.245448 2.89498554 5.3843413 6.133839112 Inf #> 331 10.557257 0.77715707 1.4762641 5.166992021 Inf #> 332 10.557257 0.77715707 1.4762641 5.166992021 Inf #> 333 7.801578 0.93447304 2.7014552 2.315744699 Inf #> 336 3.987417 0.29731794 1.3828385 0.151027996 0.7370402 #> 338 11.437571 1.50483906 3.0487558 5.435990512 Inf #> 339 3.170032 0.18131969 1.0088612 0.144550692 0.9361412 #> 340 5.784045 0.52312717 5.1958248 1.581893139 Inf #> 341 3.132075 0.55429742 5.7718558 0.247759130 0.9522847 #> 342 2.799031 1.79473176 5.9520232 0.518304035 1.9018699 #> 343 12.302919 0.55519755 5.6084166 Inf Inf #> 344 9.386265 0.41050756 2.0533236 2.060511247 Inf #> 346 10.639970 1.12797148 3.0911075 3.100615311 6.0301429 #> 350 6.153925 2.53621646 5.9795879 2.919533786 6.0579747 #> 354 6.170927 2.27138538 5.7358531 3.729207860 6.3344473 #> 357 8.211528 2.17027304 4.7299005 2.748717002 5.1089096 #> 358 6.401878 1.70642374 4.3812001 3.592866018 5.4785532 #> 360 6.401878 1.70642374 4.3812001 3.592866018 5.4785532 #> 361 10.315734 2.77164549 6.0800988 5.523600805 Inf #> 362 10.432780 0.42488885 1.3612369 0.950713483 Inf #> 364 11.015759 1.35631407 3.9482498 6.116495321 Inf #> 365 16.112188 1.51507954 3.7087226 Inf Inf #> 368 12.513653 2.68685892 5.2631287 5.372229614 6.3760247 #> 370 5.506560 0.38959091 2.2272543 0.503732566 2.2560210 #> 372 12.262440 0.43168938 4.3245839 5.064185119 Inf #> 375 12.986136 2.72596759 5.7854763 5.147440713 Inf #> 376 6.613113 1.67497503 4.2608419 3.230241717 Inf #> 378 11.983534 2.80885642 6.0493796 Inf Inf #> 379 10.044223 0.39958196 2.7913521 Inf Inf #> 380 9.484078 3.19121913 6.0330978 5.757855434 Inf #> 384 6.947876 0.83900191 2.4495022 1.324503954 3.0764073 #> 386 11.062375 1.39139459 2.7623036 Inf Inf #> 387 11.062375 1.39139459 2.7623036 Inf Inf #> 389 5.626303 2.38471036 5.6950050 1.252120890 5.0100759 #> 391 7.534332 1.43162683 3.9766076 5.881437620 Inf #> 394 8.473869 1.59053072 4.8455224 6.493876912 Inf #> 395 11.419236 0.91299791 2.4019524 5.236758636 6.0300079 #> 397 6.007350 0.47269962 6.1399990 0.441645392 1.9386531 #> 399 7.386885 0.58678810 4.9024458 Inf Inf #> 401 8.406459 0.41247948 1.9597416 1.808817338 Inf #> 404 11.704820 2.30323194 5.5256078 Inf Inf #> 405 5.359396 1.48802589 3.7982782 1.854106107 4.4410965 #> 408 11.472711 0.49209232 4.7797975 Inf Inf #> 409 9.824131 1.37232930 4.1176062 3.397031389 5.6597211 #> 410 9.750089 2.67782583 5.6571666 6.277773208 Inf #> 411 14.077228 0.55214614 5.2737085 0.835070682 Inf #> 414 7.446790 1.54644313 3.7563391 4.675890098 Inf #> 416 16.048153 0.57103992 5.7120187 Inf Inf #> 419 9.154518 3.01643160 5.7582422 4.454667912 6.2076045 #> 420 9.282633 2.59941851 5.8239482 4.994810496 Inf #> 422 4.353907 1.08136166 2.2450742 0.537628909 1.0784105 #> 427 5.813539 0.44049003 1.2312424 0.600531494 1.3655704 #> 428 5.813539 0.44049003 1.2312424 0.600531494 1.3655704 #> 430 8.294884 3.11003882 5.9115909 5.959748471 Inf #> 433 10.591996 2.95429136 5.5813685 Inf Inf #> 437 6.848772 1.53954471 3.7611556 4.431727423 Inf #> 439 3.843277 2.73897612 5.8700482 1.077465424 5.0228002 #> 441 3.843277 2.73897612 5.8700482 1.077465424 5.0228002 #> 443 8.583773 1.55012771 3.4384422 2.006763926 4.3319657 #> 445 11.635874 0.24885181 1.7730190 Inf Inf #> 447 10.861058 2.67167471 6.0475253 4.118430525 6.5466679 #> 450 10.319887 0.89713656 1.6364676 5.143636334 Inf #> 453 10.432280 0.49291097 4.8114751 Inf Inf #> 454 10.432280 0.49291097 4.8114751 Inf Inf #> 457 8.924420 2.95397705 6.1224920 5.862093858 Inf #> 458 8.924420 2.95397705 6.1224920 5.862093858 Inf #> 460 5.575966 1.51555559 4.1284671 1.763094570 3.8398589 #> 461 12.758150 1.49781631 4.2006366 Inf Inf #> 464 10.383239 1.67750256 5.1249891 Inf Inf #> 465 8.406276 2.26344376 5.5834726 5.190863194 Inf #> 466 12.247232 2.47216394 5.8384901 Inf Inf #> 467 10.370996 2.98412616 5.8970326 5.436215750 Inf #> 468 7.937128 3.14646412 5.5666368 6.350040973 Inf #> 469 5.525237 0.67558337 2.1715428 0.883026937 2.2991852 #> 470 17.156626 1.53615953 4.1273934 Inf Inf #> 471 6.449810 2.44381942 5.9263373 3.362335705 6.2025526 #> 473 15.626026 1.37851705 3.3887887 4.957730063 Inf #> 475 14.012261 2.57284026 6.0635216 5.596013765 Inf #> 476 8.390585 0.35181993 1.9120592 1.372831217 Inf #> 479 16.616906 1.55051252 3.5295052 Inf Inf #> 480 16.616906 1.55051252 3.5295052 Inf Inf #> 481 11.647343 0.59290513 5.3000812 Inf Inf #> 482 6.476746 1.49174316 4.6941363 2.091069038 5.2154405 #> 483 10.329842 1.75332519 5.0613449 5.435643045 Inf #> 486 11.594653 0.62508123 5.7068993 1.327086711 Inf #> 488 4.922630 0.20461711 1.6845815 0.139945658 1.2568975 #> 490 7.477196 0.32356830 1.4338426 0.971756751 3.0973018 #> 491 7.477196 0.32356830 1.4338426 0.971756751 3.0973018 #> 492 11.411100 2.93033518 6.1810802 5.558239912 Inf #> 493 15.138321 1.51510806 3.7392715 Inf Inf #> 494 3.873826 0.18220941 1.0969609 0.091699534 0.8486895 #> 495 12.225549 0.37518670 1.5715812 Inf Inf #> 496 7.690283 1.92555525 5.6517887 2.146849617 6.0728723 #> 498 9.534307 1.36558810 3.0277098 5.762503096 Inf #> 499 9.534307 1.36558810 3.0277098 5.762503096 Inf #> 500 2.773781 0.65667378 6.2902176 0.373623234 3.3879709 #> 501 11.530584 0.33731937 0.9197975 1.508058972 Inf #> 502 7.617208 1.89109990 5.3403262 2.591205923 Inf #> 504 5.621811 2.21981342 5.6568219 2.434121632 5.6965397 #> 505 13.807391 0.56359317 5.2624212 5.795155090 Inf #> 509 10.490827 3.02457964 5.8110892 6.145402448 Inf #> 513 3.142488 2.82669704 6.1106136 1.280102495 5.2509376 #> 514 3.142488 2.82669704 6.1106136 1.280102495 5.2509376 #> 515 4.111318 1.69251190 5.7986233 1.068040105 3.3844872 #> 517 4.913670 2.50987504 6.1097889 1.020668794 5.2178549 #> 518 6.934673 0.18681863 1.2570076 0.472633948 1.7840523 #> 519 13.060251 2.75999567 5.7929246 6.338097788 Inf #> 520 5.599368 2.23211655 5.7085379 4.411957201 6.4752810 #> 521 3.975625 0.46888113 3.5368956 0.412540372 2.2564088 #> 522 12.413718 3.13307067 6.1573656 5.357729276 Inf #> 523 10.059264 1.30750373 3.0842068 4.408059421 5.4651710 #> 525 3.649828 0.07147828 1.0011930 0.048498052 0.8778772 #> 526 3.253565 1.66602930 4.7801024 1.004363597 2.7209529 #> 527 5.271207 0.01267481 1.1158721 0.007461455 0.7837629 #> 528 11.144810 1.49647165 3.5311407 6.064214526 Inf #> 531 6.189467 3.29076592 5.3622969 3.470973856 5.4352797 #> 534 11.620474 0.57587075 5.6502320 1.732666151 Inf #> 535 7.360651 3.25243304 6.1717262 2.147243007 5.7371913 #> 540 5.311273 2.09802632 5.1109137 1.458393435 4.7290176 #> 542 4.499063 0.23982321 0.9989047 0.284675115 1.0044222 #> 543 15.013470 0.65235594 6.4260092 6.453090944 Inf #> 544 11.053088 2.83893468 5.4797507 6.000722553 Inf #> 546 2.727377 1.63856489 5.0203541 0.849091855 2.7034316 #> 547 2.950722 2.72792323 5.7943095 0.996722114 4.8068402 #> 549 3.998886 0.05399315 1.2442471 0.014320654 0.6768693 #> 551 4.314200 0.27996824 2.9680621 0.123733758 1.4896918 #> 552 3.282101 1.41586955 4.2784519 0.347847946 1.0906435 #> 553 3.623286 1.47301680 4.2390445 0.687419859 1.8230891 #> 554 4.303831 3.07905725 6.0763029 2.724727869 5.8528389 #> 556 9.871577 0.16711640 2.2687004 3.193289198 Inf #> 558 7.624966 1.63151673 4.5223468 3.363446161 Inf #> 559 10.284372 0.59329962 5.2313257 5.176477559 Inf #> 561 5.582769 2.66774103 5.5482430 4.568013933 6.2629489 #> 562 5.582769 2.66774103 5.5482430 4.568013933 6.2629489 #> 565 7.922913 0.35369329 1.9728850 5.976877847 Inf #> 566 6.894596 0.27440127 2.3879979 0.444798465 4.1009270 #> 567 4.510380 3.04662568 5.8599969 1.798504986 5.3875293 #> 569 5.816472 1.52421746 4.8285511 1.473564631 3.9997281 #> 571 4.106953 0.64467381 5.9696696 0.448604431 4.9243516 #> 572 5.434839 1.14264286 2.0822884 1.309733690 2.2514858 #> 573 12.889978 1.48412235 4.3165892 4.269357714 Inf #> 574 7.540718 0.07897595 0.5722466 0.563141265 Inf #> 575 6.631907 2.89367417 6.1680412 3.315818503 6.2557758 #> 577 4.979126 0.65520412 6.2161646 0.688726137 5.5777863 #> 579 2.588375 1.71979781 5.8334366 0.292697242 1.3384842 #> 580 2.588375 1.71979781 5.8334366 0.292697242 1.3384842 #> 581 7.068377 0.58618031 1.5088144 1.409797613 2.6648659 #> 583 17.733195 2.39865845 5.2992829 Inf Inf #> 585 6.239645 2.80289650 6.1722055 3.950276091 Inf #> 586 5.352737 0.31331655 1.2981207 0.454482941 1.4204213 #> 587 5.352737 0.31331655 1.2981207 0.454482941 1.4204213 #> 588 13.565940 0.45906313 4.8255941 Inf Inf #> 590 13.012239 1.42463855 3.5561543 1.534388758 3.5978054 #> 591 8.805313 2.83479706 5.2682879 4.675475928 6.0753703 #> 593 2.980407 2.37205969 6.2112758 1.061635135 5.0651393 #> 595 6.753352 1.23459866 3.1105371 3.053248092 5.6494469 #> 596 6.230214 0.34643538 0.7061183 0.537200768 1.0041140 #> 597 4.740045 2.16859102 5.3652514 2.729597152 5.5368126 #> 598 9.029268 0.38133494 2.3607272 1.910081924 Inf #> 599 9.029268 0.38133494 2.3607272 1.910081924 Inf #> 603 3.686875 1.29618555 3.0446474 0.632688925 1.4249475 #> 604 7.830261 0.38533255 0.8633687 1.665082423 Inf #> 606 16.500239 1.43708766 4.9137897 5.328617842 Inf #> 608 6.458822 2.75629900 5.7006541 4.245740698 6.1784483 #> 610 3.062052 3.04378438 6.0023852 1.537420079 5.4386620 #> 611 9.677450 0.25304437 1.2327338 0.630969777 2.7434432 #> 612 10.538488 3.21741886 5.6953260 6.601291694 Inf #> 614 10.538488 3.21741886 5.6953260 6.601291694 Inf #> 615 10.538488 3.21741886 5.6953260 6.601291694 Inf #> 616 5.622682 1.54405416 3.9836789 2.208028998 5.2513390 #> 617 9.239567 1.42731619 4.4521191 5.208723101 Inf #> 618 4.274213 0.56990952 5.7413732 0.323578135 0.7493740 #> 619 4.274213 0.56990952 5.7413732 0.323578135 0.7493740 #> 620 2.864603 2.80741209 6.0552893 1.044011927 5.0611838 #> 623 3.055381 0.60165852 1.1093746 0.524417704 1.0267987 #> 624 5.922283 1.71616519 5.0135594 1.113832033 3.2169318 #> 626 5.507754 1.98395938 5.4333538 1.954210759 5.5281589 #> 630 7.436280 2.99251277 6.0940883 2.959495418 5.9721761 #> 631 10.694167 2.10569740 5.1906991 5.449552006 6.5326155 #> 634 7.210455 1.25345804 3.2662254 2.404386300 4.8405299 #> 641 12.014582 0.23273791 0.8898776 2.227135223 Inf #> 642 17.229744 0.36546453 2.4410258 Inf Inf #> 643 18.750255 1.63965480 4.8149657 Inf Inf #> 644 17.085796 3.03104811 5.4289920 Inf Inf #> 645 11.110192 0.27124408 0.8292255 2.915067056 Inf #> 646 17.312389 2.97458678 5.4604316 Inf Inf #> nboot.successful path_class #> 1 1000 Lipid metabolism #> 2 957 Lipid metabolism #> 5 1000 Biosynthesis of other secondary metabolites #> 6 1000 Membrane transport #> 7 1000 Signal transduction #> 8 648 Lipid metabolism #> 9 620 Lipid metabolism #> 10 872 Lipid metabolism #> 11 909 Lipid metabolism #> 12 565 Lipid metabolism #> 13 1000 Lipid metabolism #> 14 1000 Lipid metabolism #> 16 1000 Membrane transport #> 17 1000 Signal transduction #> 18 718 Amino acid metabolism #> 19 718 Biosynthesis of other secondary metabolites #> 20 718 Translation #> 22 975 Membrane transport #> 23 975 Signal transduction #> 25 938 Membrane transport #> 26 962 Amino acid metabolism #> 27 962 Metabolism of other amino acids #> 28 962 Biosynthesis of other secondary metabolites #> 29 962 Translation #> 30 962 Membrane transport #> 31 979 Amino acid metabolism #> 32 979 Biosynthesis of other secondary metabolites #> 33 979 Translation #> 34 979 Membrane transport #> 35 851 Amino acid metabolism #> 36 851 Metabolism of other amino acids #> 37 851 Lipid metabolism #> 39 851 Energy metabolism #> 40 851 Translation #> 41 851 Biosynthesis of other secondary metabolites #> 42 851 Membrane transport #> 43 851 Signal transduction #> 44 1000 Amino acid metabolism #> 46 859 Energy metabolism #> 47 859 Signal transduction #> 48 833 Amino acid metabolism #> 49 833 Metabolism of other amino acids #> 50 833 Biosynthesis of other secondary metabolites #> 51 833 Membrane transport #> 52 890 Amino acid metabolism #> 53 890 Metabolism of other amino acids #> 54 890 Energy metabolism #> 55 890 Translation #> 56 890 Membrane transport #> 57 890 Signal transduction #> 58 940 Amino acid metabolism #> 59 1000 Lipid metabolism #> 60 1000 Amino acid metabolism #> 61 635 Amino acid metabolism #> 62 635 Metabolism of other amino acids #> 63 635 Biosynthesis of other secondary metabolites #> 64 635 Translation #> 65 635 Membrane transport #> 67 820 Amino acid metabolism #> 68 820 Metabolism of other amino acids #> 69 820 Biosynthesis of other secondary metabolites #> 70 820 Membrane transport #> 71 722 Energy metabolism #> 72 722 Membrane transport #> 73 722 Signal transduction #> 75 953 Amino acid metabolism #> 76 953 Lipid metabolism #> 77 953 Energy metabolism #> 79 962 Amino acid metabolism #> 80 962 Signal transduction #> 81 998 Amino acid metabolism #> 82 998 Metabolism of other amino acids #> 83 998 Translation #> 84 998 Membrane transport #> 85 500 Energy metabolism #> 86 497 Nucleotide metabolism #> 87 495 Nucleotide metabolism #> 88 332 Translation #> 91 500 Energy metabolism #> 95 469 Translation #> 99 500 Amino acid metabolism #> 102 500 Lipid metabolism #> 103 443 Metabolism of other amino acids #> 104 443 Amino acid metabolism #> 106 491 Translation #> 109 500 Energy metabolism #> 110 417 Lipid metabolism #> 111 395 Lipid metabolism #> 115 500 Energy metabolism #> 116 353 Nucleotide metabolism #> 117 500 Translation #> 120 492 Translation #> 121 483 Nucleotide metabolism #> 122 498 Energy metabolism #> 123 304 Membrane transport #> 125 500 Nucleotide metabolism #> 127 481 Translation #> 128 291 Amino acid metabolism #> 129 279 Signal transduction #> 131 483 Metabolism of other amino acids #> 132 483 Amino acid metabolism #> 133 497 Translation #> 134 405 Metabolism of other amino acids #> 137 253 Amino acid metabolism #> 140 500 Translation #> 141 481 Lipid metabolism #> 142 500 Amino acid metabolism #> 143 499 Translation #> 147 363 Amino acid metabolism #> 148 500 Translation #> 149 393 Amino acid metabolism #> 150 500 Metabolism of other amino acids #> 152 500 Translation #> 156 500 Lipid metabolism #> 157 487 Membrane transport #> 158 500 Amino acid metabolism #> 161 439 Metabolism of terpenoids and polyketides #> 164 423 Energy metabolism #> 165 500 Energy metabolism #> 166 261 Signal transduction #> 167 500 Translation #> 169 500 Nucleotide metabolism #> 170 466 Signal transduction #> 171 500 Amino acid metabolism #> 172 500 Metabolism of other amino acids #> 174 359 Translation #> 178 336 Nucleotide metabolism #> 179 500 Translation #> 180 500 Metabolism of other amino acids #> 181 500 Nucleotide metabolism #> 183 478 Nucleotide metabolism #> 184 500 Metabolism of other amino acids #> 185 500 Amino acid metabolism #> 186 295 Nucleotide metabolism #> 188 482 Metabolism of terpenoids and polyketides #> 190 344 Translation #> 193 483 Membrane transport #> 196 498 Transport and catabolism #> 197 500 Energy metabolism #> 198 500 Metabolism of terpenoids and polyketides #> 199 500 Amino acid metabolism #> 200 500 Energy metabolism #> 201 476 Energy metabolism #> 202 476 Metabolism of other amino acids #> 203 476 Amino acid metabolism #> 205 496 Lipid metabolism #> 207 479 Energy metabolism #> 208 260 Amino acid metabolism #> 211 493 Translation #> 214 494 Translation #> 215 305 Energy metabolism #> 217 500 Energy metabolism #> 218 497 Lipid metabolism #> 220 500 Energy metabolism #> 221 279 Translation #> 222 500 Lipid metabolism #> 225 317 Translation #> 228 261 Nucleotide metabolism #> 229 500 Amino acid metabolism #> 232 415 Amino acid metabolism #> 233 415 Metabolism of other amino acids #> 235 480 Nucleotide metabolism #> 236 251 Lipid metabolism #> 237 336 Amino acid metabolism #> 238 336 Metabolism of other amino acids #> 240 336 Energy metabolism #> 242 500 Signal transduction #> 243 333 Transport and catabolism #> 245 315 Nucleotide metabolism #> 246 315 Metabolism of other amino acids #> 248 500 Translation #> 251 500 Energy metabolism #> 252 500 Metabolism of terpenoids and polyketides #> 253 499 Nucleotide metabolism #> 255 275 Lipid metabolism #> 256 479 Membrane transport #> 257 293 Lipid metabolism #> 259 500 Lipid metabolism #> 260 473 Translation #> 263 500 Amino acid metabolism #> 265 500 Lipid metabolism #> 266 446 Metabolism of terpenoids and polyketides #> 270 303 Translation #> 272 500 Energy metabolism #> 274 468 Energy metabolism #> 275 454 Metabolism of terpenoids and polyketides #> 276 491 Metabolism of terpenoids and polyketides #> 277 500 Energy metabolism #> 279 370 Amino acid metabolism #> 280 376 Amino acid metabolism #> 282 441 Lipid metabolism #> 285 415 Translation #> 286 500 Translation #> 287 500 Energy metabolism #> 288 500 Amino acid metabolism #> 291 500 Metabolism of terpenoids and polyketides #> 293 377 Metabolism of terpenoids and polyketides #> 295 500 Translation #> 296 483 Lipid metabolism #> 297 500 Signal transduction #> 298 500 Lipid metabolism #> 299 326 Amino acid metabolism #> 301 500 Metabolism of other amino acids #> 303 500 Translation #> 305 275 Metabolism of terpenoids and polyketides #> 306 500 Metabolism of other amino acids #> 307 500 Amino acid metabolism #> 308 498 Amino acid metabolism #> 312 491 Transport and catabolism #> 314 497 Amino acid metabolism #> 317 360 Lipid metabolism #> 320 297 Energy metabolism #> 321 297 Amino acid metabolism #> 322 483 Energy metabolism #> 327 474 Nucleotide metabolism #> 328 414 Amino acid metabolism #> 330 335 Amino acid metabolism #> 331 500 Lipid metabolism #> 332 500 Signal transduction #> 333 494 Lipid metabolism #> 336 464 Translation #> 338 500 Metabolism of terpenoids and polyketides #> 339 488 Translation #> 340 388 Translation #> 341 403 Transport and catabolism #> 342 500 Signal transduction #> 343 439 Amino acid metabolism #> 344 469 Nucleotide metabolism #> 346 500 Metabolism of other amino acids #> 350 371 Amino acid metabolism #> 354 385 Nucleotide metabolism #> 357 476 Lipid metabolism #> 358 499 Lipid metabolism #> 360 499 Amino acid metabolism #> 361 291 Nucleotide metabolism #> 362 487 Lipid metabolism #> 364 500 Nucleotide metabolism #> 365 500 Energy metabolism #> 368 399 Energy metabolism #> 370 465 Transport and catabolism #> 372 406 Metabolism of other amino acids #> 375 330 Transport and catabolism #> 376 500 Translation #> 378 318 Lipid metabolism #> 379 436 Amino acid metabolism #> 380 252 Energy metabolism #> 384 490 Translation #> 386 500 Amino acid metabolism #> 387 500 Energy metabolism #> 389 345 Metabolism of other amino acids #> 391 500 Lipid metabolism #> 394 475 Translation #> 395 500 Amino acid metabolism #> 397 300 Translation #> 399 466 Transport and catabolism #> 401 413 Energy metabolism #> 404 396 Lipid metabolism #> 405 500 Metabolism of terpenoids and polyketides #> 408 303 Metabolism of other amino acids #> 409 497 Amino acid metabolism #> 410 352 Translation #> 411 480 Energy metabolism #> 414 500 Energy metabolism #> 416 385 Lipid metabolism #> 419 266 Transport and catabolism #> 420 303 Translation #> 422 500 Energy metabolism #> 427 336 Lipid metabolism #> 428 336 Amino acid metabolism #> 430 317 Membrane transport #> 433 311 Energy metabolism #> 437 500 Translation #> 439 261 Amino acid metabolism #> 441 261 Lipid metabolism #> 443 500 Lipid metabolism #> 445 485 Metabolism of other amino acids #> 447 291 Translation #> 450 500 Energy metabolism #> 453 486 Translation #> 454 486 Signal transduction #> 457 282 Signal transduction #> 458 282 Transport and catabolism #> 460 500 Membrane transport #> 461 500 Translation #> 464 500 Lipid metabolism #> 465 420 Amino acid metabolism #> 466 375 Energy metabolism #> 467 298 Energy metabolism #> 468 265 Translation #> 469 489 Translation #> 470 500 Translation #> 471 377 Energy metabolism #> 473 500 Translation #> 475 366 Transport and catabolism #> 476 473 Metabolism of other amino acids #> 479 500 Energy metabolism #> 480 500 Signal transduction #> 481 336 Lipid metabolism #> 482 488 Translation #> 483 500 Translation #> 486 384 Translation #> 488 484 Metabolism of other amino acids #> 490 490 Membrane transport #> 491 490 Energy metabolism #> 492 306 Transport and catabolism #> 493 500 Energy metabolism #> 494 492 Membrane transport #> 495 493 Lipid metabolism #> 496 500 Translation #> 498 500 Amino acid metabolism #> 499 500 Metabolism of other amino acids #> 500 271 Translation #> 501 499 Metabolism of other amino acids #> 502 500 Translation #> 504 431 Energy metabolism #> 505 385 Translation #> 509 276 Amino acid metabolism #> 513 309 Membrane transport #> 514 309 Signal transduction #> 515 500 Translation #> 517 289 Amino acid metabolism #> 518 498 Translation #> 519 360 Membrane transport #> 520 366 Energy metabolism #> 521 497 Translation #> 522 256 Energy metabolism #> 523 500 Energy metabolism #> 525 472 Translation #> 526 500 Translation #> 527 461 Translation #> 528 500 Translation #> 531 334 Lipid metabolism #> 534 434 Metabolism of other amino acids #> 535 293 Amino acid metabolism #> 540 461 Metabolism of other amino acids #> 542 490 Translation #> 543 405 Energy metabolism #> 544 332 Energy metabolism #> 546 500 Lipid metabolism #> 547 351 Lipid metabolism #> 549 483 Metabolism of terpenoids and polyketides #> 551 464 Membrane transport #> 552 500 Transport and catabolism #> 553 500 Metabolism of terpenoids and polyketides #> 554 266 Nucleotide metabolism #> 556 456 Energy metabolism #> 558 500 Amino acid metabolism #> 559 423 Translation #> 561 357 Lipid metabolism #> 562 357 Amino acid metabolism #> 565 481 Translation #> 566 470 Amino acid metabolism #> 567 268 Transport and catabolism #> 569 500 Lipid metabolism #> 571 448 Nucleotide metabolism #> 572 500 Translation #> 573 500 Lipid metabolism #> 574 391 Lipid metabolism #> 575 301 Amino acid metabolism #> 577 371 Nucleotide metabolism #> 579 500 Membrane transport #> 580 500 Signal transduction #> 581 500 Translation #> 583 433 Translation #> 585 324 Amino acid metabolism #> 586 493 Transport and catabolism #> 587 493 Membrane transport #> 588 275 Translation #> 590 500 Energy metabolism #> 591 368 Amino acid metabolism #> 593 343 Metabolism of other amino acids #> 595 500 Amino acid metabolism #> 596 332 Energy metabolism #> 597 440 Nucleotide metabolism #> 598 452 Signal transduction #> 599 452 Lipid metabolism #> 603 500 Lipid metabolism #> 604 250 Nucleotide metabolism #> 606 500 Energy metabolism #> 608 301 Nucleotide metabolism #> 610 280 Nucleotide metabolism #> 611 494 Metabolism of terpenoids and polyketides #> 612 283 Lipid metabolism #> 614 283 Metabolism of other amino acids #> 615 283 Amino acid metabolism #> 616 500 Amino acid metabolism #> 617 491 Lipid metabolism #> 618 461 Lipid metabolism #> 619 461 Amino acid metabolism #> 620 312 Nucleotide metabolism #> 623 251 Lipid metabolism #> 624 500 Energy metabolism #> 626 462 Lipid metabolism #> 630 264 Nucleotide metabolism #> 631 460 Amino acid metabolism #> 634 500 Amino acid metabolism #> 641 496 Nucleotide metabolism #> 642 450 Translation #> 643 500 Translation #> 644 296 Translation #> 645 497 Nucleotide metabolism #> 646 305 Translation #> molecular.level #> 1 metabolites #> 2 metabolites #> 5 metabolites #> 6 metabolites #> 7 metabolites #> 8 metabolites #> 9 metabolites #> 10 metabolites #> 11 metabolites #> 12 metabolites #> 13 metabolites #> 14 metabolites #> 16 metabolites #> 17 metabolites #> 18 metabolites #> 19 metabolites #> 20 metabolites #> 22 metabolites #> 23 metabolites #> 25 metabolites #> 26 metabolites #> 27 metabolites #> 28 metabolites #> 29 metabolites #> 30 metabolites #> 31 metabolites #> 32 metabolites #> 33 metabolites #> 34 metabolites #> 35 metabolites #> 36 metabolites #> 37 metabolites #> 39 metabolites #> 40 metabolites #> 41 metabolites #> 42 metabolites #> 43 metabolites #> 44 metabolites #> 46 metabolites #> 47 metabolites #> 48 metabolites #> 49 metabolites #> 50 metabolites #> 51 metabolites #> 52 metabolites #> 53 metabolites #> 54 metabolites #> 55 metabolites #> 56 metabolites #> 57 metabolites #> 58 metabolites #> 59 metabolites #> 60 metabolites #> 61 metabolites #> 62 metabolites #> 63 metabolites #> 64 metabolites #> 65 metabolites #> 67 metabolites #> 68 metabolites #> 69 metabolites #> 70 metabolites #> 71 metabolites #> 72 metabolites #> 73 metabolites #> 75 metabolites #> 76 metabolites #> 77 metabolites #> 79 metabolites #> 80 metabolites #> 81 metabolites #> 82 metabolites #> 83 metabolites #> 84 metabolites #> 85 contigs #> 86 contigs #> 87 contigs #> 88 contigs #> 91 contigs #> 95 contigs #> 99 contigs #> 102 contigs #> 103 contigs #> 104 contigs #> 106 contigs #> 109 contigs #> 110 contigs #> 111 contigs #> 115 contigs #> 116 contigs #> 117 contigs #> 120 contigs #> 121 contigs #> 122 contigs #> 123 contigs #> 125 contigs #> 127 contigs #> 128 contigs #> 129 contigs #> 131 contigs #> 132 contigs #> 133 contigs #> 134 contigs #> 137 contigs #> 140 contigs #> 141 contigs #> 142 contigs #> 143 contigs #> 147 contigs #> 148 contigs #> 149 contigs #> 150 contigs #> 152 contigs #> 156 contigs #> 157 contigs #> 158 contigs #> 161 contigs #> 164 contigs #> 165 contigs #> 166 contigs #> 167 contigs #> 169 contigs #> 170 contigs #> 171 contigs #> 172 contigs #> 174 contigs #> 178 contigs #> 179 contigs #> 180 contigs #> 181 contigs #> 183 contigs #> 184 contigs #> 185 contigs #> 186 contigs #> 188 contigs #> 190 contigs #> 193 contigs #> 196 contigs #> 197 contigs #> 198 contigs #> 199 contigs #> 200 contigs #> 201 contigs #> 202 contigs #> 203 contigs #> 205 contigs #> 207 contigs #> 208 contigs #> 211 contigs #> 214 contigs #> 215 contigs #> 217 contigs #> 218 contigs #> 220 contigs #> 221 contigs #> 222 contigs #> 225 contigs #> 228 contigs #> 229 contigs #> 232 contigs #> 233 contigs #> 235 contigs #> 236 contigs #> 237 contigs #> 238 contigs #> 240 contigs #> 242 contigs #> 243 contigs #> 245 contigs #> 246 contigs #> 248 contigs #> 251 contigs #> 252 contigs #> 253 contigs #> 255 contigs #> 256 contigs #> 257 contigs #> 259 contigs #> 260 contigs #> 263 contigs #> 265 contigs #> 266 contigs #> 270 contigs #> 272 contigs #> 274 contigs #> 275 contigs #> 276 contigs #> 277 contigs #> 279 contigs #> 280 contigs #> 282 contigs #> 285 contigs #> 286 contigs #> 287 contigs #> 288 contigs #> 291 contigs #> 293 contigs #> 295 contigs #> 296 contigs #> 297 contigs #> 298 contigs #> 299 contigs #> 301 contigs #> 303 contigs #> 305 contigs #> 306 contigs #> 307 contigs #> 308 contigs #> 312 contigs #> 314 contigs #> 317 contigs #> 320 contigs #> 321 contigs #> 322 contigs #> 327 contigs #> 328 contigs #> 330 contigs #> 331 contigs #> 332 contigs #> 333 contigs #> 336 contigs #> 338 contigs #> 339 contigs #> 340 contigs #> 341 contigs #> 342 contigs #> 343 contigs #> 344 contigs #> 346 contigs #> 350 contigs #> 354 contigs #> 357 contigs #> 358 contigs #> 360 contigs #> 361 contigs #> 362 contigs #> 364 contigs #> 365 contigs #> 368 contigs #> 370 contigs #> 372 contigs #> 375 contigs #> 376 contigs #> 378 contigs #> 379 contigs #> 380 contigs #> 384 contigs #> 386 contigs #> 387 contigs #> 389 contigs #> 391 contigs #> 394 contigs #> 395 contigs #> 397 contigs #> 399 contigs #> 401 contigs #> 404 contigs #> 405 contigs #> 408 contigs #> 409 contigs #> 410 contigs #> 411 contigs #> 414 contigs #> 416 contigs #> 419 contigs #> 420 contigs #> 422 contigs #> 427 contigs #> 428 contigs #> 430 contigs #> 433 contigs #> 437 contigs #> 439 contigs #> 441 contigs #> 443 contigs #> 445 contigs #> 447 contigs #> 450 contigs #> 453 contigs #> 454 contigs #> 457 contigs #> 458 contigs #> 460 contigs #> 461 contigs #> 464 contigs #> 465 contigs #> 466 contigs #> 467 contigs #> 468 contigs #> 469 contigs #> 470 contigs #> 471 contigs #> 473 contigs #> 475 contigs #> 476 contigs #> 479 contigs #> 480 contigs #> 481 contigs #> 482 contigs #> 483 contigs #> 486 contigs #> 488 contigs #> 490 contigs #> 491 contigs #> 492 contigs #> 493 contigs #> 494 contigs #> 495 contigs #> 496 contigs #> 498 contigs #> 499 contigs #> 500 contigs #> 501 contigs #> 502 contigs #> 504 contigs #> 505 contigs #> 509 contigs #> 513 contigs #> 514 contigs #> 515 contigs #> 517 contigs #> 518 contigs #> 519 contigs #> 520 contigs #> 521 contigs #> 522 contigs #> 523 contigs #> 525 contigs #> 526 contigs #> 527 contigs #> 528 contigs #> 531 contigs #> 534 contigs #> 535 contigs #> 540 contigs #> 542 contigs #> 543 contigs #> 544 contigs #> 546 contigs #> 547 contigs #> 549 contigs #> 551 contigs #> 552 contigs #> 553 contigs #> 554 contigs #> 556 contigs #> 558 contigs #> 559 contigs #> 561 contigs #> 562 contigs #> 565 contigs #> 566 contigs #> 567 contigs #> 569 contigs #> 571 contigs #> 572 contigs #> 573 contigs #> 574 contigs #> 575 contigs #> 577 contigs #> 579 contigs #> 580 contigs #> 581 contigs #> 583 contigs #> 585 contigs #> 586 contigs #> 587 contigs #> 588 contigs #> 590 contigs #> 591 contigs #> 593 contigs #> 595 contigs #> 596 contigs #> 597 contigs #> 598 contigs #> 599 contigs #> 603 contigs #> 604 contigs #> 606 contigs #> 608 contigs #> 610 contigs #> 611 contigs #> 612 contigs #> 614 contigs #> 615 contigs #> 616 contigs #> 617 contigs #> 618 contigs #> 619 contigs #> 620 contigs #> 623 contigs #> 624 contigs #> 626 contigs #> 630 contigs #> 631 contigs #> 634 contigs #> 641 contigs #> 642 contigs #> 643 contigs #> 644 contigs #> 645 contigs #> 646 contigs sensitivityplot(extendedres.3, BMDtype = \"zSD\", group = \"path_class\", colorby = \"molecular.level\", BMDsummary = \"first.quartile\") # }"},{"path":"/reference/sensitivityplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of a summary of BMD values per group of items — sensitivityplot","title":"Plot of a summary of BMD values per group of items — sensitivityplot","text":"Plot summary BMD values per group items (groups defined example biological annotation), groups ordered values chosen summary (ECDF plot) ordered definition factor coding , points sized numbers items per group.","code":""},{"path":"/reference/sensitivityplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of a summary of BMD values per group of items — sensitivityplot","text":"","code":"sensitivityplot(extendedres, BMDtype = c(\"zSD\", \"xfold\"), group, ECDF_plot = TRUE, colorby, BMDsummary = c(\"first.quartile\", \"median\" , \"median.and.IQR\"), BMD_log_transfo = TRUE, line.size = 0.5, line.alpha = 0.5, point.alpha = 0.5)"},{"path":"/reference/sensitivityplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of a summary of BMD values per group of items — sensitivityplot","text":"extendedres dataframe results provided bmdcalc (res) subset data frame (selected lines). dataframe can extended additional columns coming example annotation items, lines can replicated corresponding item one annotation. extended dataframe must least contain column giving chosen BMD values compute sensitivity (column BMD.zSD BMD.xfold). BMDtype type BMD used, \"zSD\" (default choice) \"xfold\". group name column extendedres coding groups want estimate global sensitivity. ECDF_plot FALSE, column factor ordered want groups appear plot bottom . ECDF_plot TRUE (default choice) groups appear ordered values BMD summary value bottom , else ordered corresponding levels factor given group. colorby given, ECDF_plot fixed FALSE. colorby optional argument naming column extendedres coding additional level grouping materialized color. missing, ECDF_plot fixed FALSE. BMDsummary type summary used sensitivity plot, \"first.quartile\" (default choice) plot first quartiles BMD values per group, \"median\" plot medians BMD values per group \"median..IQR\" plot medians interval corresponding inter-quartile range (IQR). BMD_log_transfo TRUE, default choice, log transformation BMD used plot. line.size Width lines. line.alpha Transparency lines. point.alpha Transparency points.","code":""},{"path":"/reference/sensitivityplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot of a summary of BMD values per group of items — sensitivityplot","text":"chosen summary calculated BMD values group (groups can example defined pathways biological annotation items) plotted ECDF plot (ordered BMD summary) order levels factor defining groups bottom . plot point sized according number items corresponding group. Optionally different levels (e.g. different molecular levels multi-omics approach) can coded different colors.","code":""},{"path":"/reference/sensitivityplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot of a summary of BMD values per group of items — sensitivityplot","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/sensitivityplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot of a summary of BMD values per group of items — sensitivityplot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/sensitivityplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot of a summary of BMD values per group of items — sensitivityplot","text":"","code":"# (1) An example from data published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # a dataframe with metabolomic results (output $res of bmdcalc() or bmdboot() functions) resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # a dataframe with annotation of each item identified in the previous file # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe # bootstrap results and annotation annotres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(annotres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport ### an ECDFplot of 25th quantiles of BMD-zSD calculated by pathway sensitivityplot(annotres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\") # \\donttest{ # same plot in raw BMD scale (so not in log scale) sensitivityplot(annotres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\", BMD_log_transfo = FALSE) ### Plot of 25th quantiles of BMD-zSD calculated by pathway ### in the order of the levels as defined in the group input levels(annotres$path_class) #> [1] \"Amino acid metabolism\" #> [2] \"Biosynthesis of other secondary metabolites\" #> [3] \"Carbohydrate metabolism\" #> [4] \"Energy metabolism\" #> [5] \"Lipid metabolism\" #> [6] \"Membrane transport\" #> [7] \"Metabolism of other amino acids\" #> [8] \"Signal transduction\" #> [9] \"Translation\" sensitivityplot(annotres, BMDtype = \"zSD\", group = \"path_class\", ECDF_plot = FALSE, BMDsummary = \"first.quartile\") ### an ECDFplot of medians of BMD-zSD calculated by pathway sensitivityplot(annotres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"median\") ### an ECDFplot of medians of BMD-zSD calculated by pathway ### with addition of interquartile ranges (IQRs) sensitivityplot(annotres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"median.and.IQR\") ### The same plot playing with graphical parameters sensitivityplot(annotres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"median.and.IQR\", line.size = 1.5, line.alpha = 0.4, point.alpha = 1) # (2) # An example with two molecular levels # ### Rename metabolomic results metabextendedres <- annotres # Import the dataframe with transcriptomic results contigresfilename <- system.file(\"extdata\", \"triclosanSVcontigres.txt\", package = \"DRomics\") contigres <- read.table(contigresfilename, header = TRUE, stringsAsFactors = TRUE) str(contigres) #> 'data.frame':\t447 obs. of 27 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... # Import the dataframe with functional annotation (or any other descriptor/category # you want to use, here KEGG pathway classes) contigannotfilename <- system.file(\"extdata\", \"triclosanSVcontigannot.txt\", package = \"DRomics\") contigannot <- read.table(contigannotfilename, header = TRUE, stringsAsFactors = TRUE) str(contigannot) #> 'data.frame':\t562 obs. of 2 variables: #> $ contig : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ path_class: Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... # Merging of both previous dataframes contigextendedres <- merge(x = contigres, y = contigannot, by.x = \"id\", by.y = \"contig\") # to see the structure of this dataframe str(contigextendedres) #> 'data.frame':\t562 obs. of 28 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... #> $ path_class : Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... ### Merge metabolomic and transcriptomic results extendedres <- rbind(metabextendedres, contigextendedres) extendedres$molecular.level <- factor(c(rep(\"metabolites\", nrow(metabextendedres)), rep(\"contigs\", nrow(contigextendedres)))) str(extendedres) #> 'data.frame':\t646 obs. of 29 variables: #> $ id : Factor w/ 478 levels \"NAP47_51\",\"NAP_2\",..: 1 2 3 4 4 4 4 5 6 7 ... #> $ irow : int 46 2 21 28 28 28 28 34 38 47 ... #> $ adjpvalue : num 7.16e-04 6.23e-05 1.11e-05 1.03e-05 1.03e-05 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 3 3 3 3 3 2 2 4 ... #> $ nbpar : int 2 3 2 2 2 2 2 3 3 5 ... #> $ b : num -0.056 0.4598 -0.0595 -0.0451 -0.0451 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 7.34 5.94 5.39 7.86 7.86 ... #> $ e : num NA -1.65 NA NA NA ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.1245 0.126 0.0793 0.052 0.052 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 7 7 7 7 7 2 2 9 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 3 3 3 3 1 ... #> $ y0 : num 7.34 5.94 5.39 7.86 7.86 ... #> $ yrange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ maxychange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 2.224 0.528 1.333 1.154 1.154 ... #> $ BMR.zSD : num 7.22 5.82 5.31 7.81 7.81 ... #> $ BMD.xfold : num NA NA NA NA NA ... #> $ BMR.xfold : num 6.61 5.35 4.85 7.07 7.07 ... #> $ BMD.zSD.lower : num 0.979 0.2 0.853 0.752 0.752 ... #> $ BMD.zSD.upper : num 4.07 1.11 1.75 1.46 1.46 ... #> $ BMD.xfold.lower : num Inf Inf 7.61 Inf Inf ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 1000 957 1000 1000 1000 1000 1000 648 620 872 ... #> $ path_class : Factor w/ 18 levels \"Amino acid metabolism\",..: 5 5 3 3 2 6 8 5 5 5 ... #> $ molecular.level : Factor w/ 2 levels \"contigs\",\"metabolites\": 2 2 2 2 2 2 2 2 2 2 ... ### Plot of 25th quantiles of BMD-zSD calculated by pathway ### and colored by molecular level # optional inverse alphabetic ordering of groups for the plot extendedres$path_class <- factor(extendedres$path_class, levels = sort(levels(extendedres$path_class), decreasing = TRUE)) sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", colorby = \"molecular.level\", BMDsummary = \"first.quartile\") ### Plot of medians and IQRs of BMD-zSD calculated by pathway ### and colored by molecular level sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", colorby = \"molecular.level\", BMDsummary = \"median.and.IQR\", line.size = 1.2, line.alpha = 0.4, point.alpha = 0.8) # }"},{"path":"/reference/targetplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Dose-reponse plot for target items — targetplot","title":"Dose-reponse plot for target items — targetplot","text":"Plots dose-response raw data target items (whether response considered significant) fitted curves available.","code":""},{"path":"/reference/targetplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dose-reponse plot for target items — targetplot","text":"","code":"targetplot(items, f, add.fit = TRUE, dose_log_transfo = TRUE)"},{"path":"/reference/targetplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dose-reponse plot for target items — targetplot","text":"items character vector specifying identifiers items plot. f object class \"drcfit\". add.fit TRUE fitted curve added items selected responsive items best fit model obtained. dose_log_transfo TRUE, default choice, log transformation used dose axis.","code":""},{"path":"/reference/targetplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dose-reponse plot for target items — targetplot","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/targetplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dose-reponse plot for target items — targetplot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/targetplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dose-reponse plot for target items — targetplot","text":"","code":"# A toy example on a very small subsample of a microarray data set) # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package=\"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.01) #> Removing intercept from test coefficients f <- drcfit(s_quad, progressbar = TRUE) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |==== | 6% | |======== | 12% | |============ | 18% | |================ | 24% | |===================== | 29% | |========================= | 35% | |============================= | 41% | |================================= | 47% | |===================================== | 53% | |========================================= | 59% | |============================================= | 65% | |================================================= | 71% | |====================================================== | 76% | |========================================================== | 82% | |============================================================== | 88% | |================================================================== | 94% | |======================================================================| 100% # Plot of chosen items with fitted curves when available # targetitems <- c(\"88.1\", \"1\", \"3\", \"15\") targetplot(targetitems, f = f) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # \\donttest{ # The same plot in raw scale instead of default log scale # targetplot(targetitems, f = f, dose_log_transfo = FALSE) # The same plot in x log scale choosing x limits for plot # to enlarge the space between the control and the non null doses # if (require(ggplot2)) targetplot(targetitems, f = f, dose_log_transfo = TRUE) + scale_x_log10(limits = c(0.1, 10)) #> Scale for x is already present. #> Adding another scale for x, which will replace the existing scale. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # The same plot without fitted curves # targetplot(targetitems, f = f, add.fit = FALSE) # }"},{"path":"/reference/trendplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of the repartition of trends per group — trendplot","title":"Plot of the repartition of trends per group — trendplot","text":"Provides plot repartition dose-response trends per group items.","code":""},{"path":"/reference/trendplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of the repartition of trends per group — trendplot","text":"","code":"trendplot(extendedres, group, facetby, ncol4faceting, add.color = TRUE)"},{"path":"/reference/trendplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of the repartition of trends per group — trendplot","text":"extendedres dataframe results provided drcfit (fitres) bmdcalc (res) subset data frame (selected lines). dataframe extended additional columns coming group (example functional annotation items) /another level (example molecular level), lines can replicated corresponding item one annotation. extended dataframe must least contain results dose-response modelling column giving trend (trend). group name column extendedres coding groups want see repartition dose-response trends. column factor ordered want groups appear plot bottom . facetby optional argument naming column extendedres chosen split plot facets using ggplot2::facet_wrap (split omitted). ncol4faceting number columns faceting. add.color TRUE color added coding trend.","code":""},{"path":"/reference/trendplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot of the repartition of trends per group — trendplot","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/trendplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot of the repartition of trends per group — trendplot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/trendplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot of the repartition of trends per group — trendplot","text":"","code":"# (1) # An example from the paper published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # the dataframe with metabolomic results resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # the dataframe with annotation of each item identified in the previous file # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe extendedres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(extendedres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport # (1.a) Trendplot by pathway trendplot(extendedres, group = \"path_class\") # \\donttest{ # (1.b) Trendplot by pathway without color trendplot(extendedres, group = \"path_class\", add.color = FALSE) # (1.c) Reordering of the groups before plotting extendedres$path_class <- factor(extendedres$path_class, levels = sort(levels(extendedres$path_class), decreasing = TRUE)) trendplot(extendedres, group = \"path_class\", add.color = FALSE) # (2) # An example with two molecular levels # ### Rename metabolomic results metabextendedres <- extendedres # Import the dataframe with transcriptomic results contigresfilename <- system.file(\"extdata\", \"triclosanSVcontigres.txt\", package = \"DRomics\") contigres <- read.table(contigresfilename, header = TRUE, stringsAsFactors = TRUE) str(contigres) #> 'data.frame':\t447 obs. of 27 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... # Import the dataframe with functional annotation (or any other descriptor/category # you want to use, here KEGG pathway classes) contigannotfilename <- system.file(\"extdata\", \"triclosanSVcontigannot.txt\", package = \"DRomics\") contigannot <- read.table(contigannotfilename, header = TRUE, stringsAsFactors = TRUE) str(contigannot) #> 'data.frame':\t562 obs. of 2 variables: #> $ contig : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ path_class: Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... # Merging of both previous dataframes contigextendedres <- merge(x = contigres, y = contigannot, by.x = \"id\", by.y = \"contig\") # to see the structure of this dataframe str(contigextendedres) #> 'data.frame':\t562 obs. of 28 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... #> $ path_class : Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... ### Merge metabolomic and transcriptomic results extendedres <- rbind(metabextendedres, contigextendedres) extendedres$molecular.level <- factor(c(rep(\"metabolites\", nrow(metabextendedres)), rep(\"contigs\", nrow(contigextendedres)))) str(extendedres) #> 'data.frame':\t646 obs. of 29 variables: #> $ id : Factor w/ 478 levels \"NAP47_51\",\"NAP_2\",..: 1 2 3 4 4 4 4 5 6 7 ... #> $ irow : int 46 2 21 28 28 28 28 34 38 47 ... #> $ adjpvalue : num 7.16e-04 6.23e-05 1.11e-05 1.03e-05 1.03e-05 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 3 3 3 3 3 2 2 4 ... #> $ nbpar : int 2 3 2 2 2 2 2 3 3 5 ... #> $ b : num -0.056 0.4598 -0.0595 -0.0451 -0.0451 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 7.34 5.94 5.39 7.86 7.86 ... #> $ e : num NA -1.65 NA NA NA ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.1245 0.126 0.0793 0.052 0.052 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 7 7 7 7 7 2 2 9 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 3 3 3 3 1 ... #> $ y0 : num 7.34 5.94 5.39 7.86 7.86 ... #> $ yrange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ maxychange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 2.224 0.528 1.333 1.154 1.154 ... #> $ BMR.zSD : num 7.22 5.82 5.31 7.81 7.81 ... #> $ BMD.xfold : num NA NA NA NA NA ... #> $ BMR.xfold : num 6.61 5.35 4.85 7.07 7.07 ... #> $ BMD.zSD.lower : num 0.979 0.2 0.853 0.752 0.752 ... #> $ BMD.zSD.upper : num 4.07 1.11 1.75 1.46 1.46 ... #> $ BMD.xfold.lower : num Inf Inf 7.61 Inf Inf ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 1000 957 1000 1000 1000 1000 1000 648 620 872 ... #> $ path_class : Factor w/ 18 levels \"Translation\",..: 5 5 7 7 8 4 2 5 5 5 ... #> $ molecular.level : Factor w/ 2 levels \"contigs\",\"metabolites\": 2 2 2 2 2 2 2 2 2 2 ... ### trend plot of both molecular levels # optional inverse alphabetic ordering of groups for the plot extendedres$path_class <- factor(extendedres$path_class, levels = sort(levels(extendedres$path_class), decreasing = TRUE)) trendplot(extendedres, group = \"path_class\", facetby = \"molecular.level\") # }"},{"path":"/reference/zebraf.html","id":null,"dir":"Reference","previous_headings":"","what":"Transcriptomic dose-response to ionizing radiation in zebrafish with batch effect — zebraf","title":"Transcriptomic dose-response to ionizing radiation in zebrafish with batch effect — zebraf","text":"sample RNAseq data set dose-response chronic exposure ionizing radiation zebrafish embryo fertilization 48 hours post-fertilization corresponding batch effect experiment.","code":""},{"path":"/reference/zebraf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transcriptomic dose-response to ionizing radiation in zebrafish with batch effect — zebraf","text":"","code":"data(zebraf)"},{"path":"/reference/zebraf.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Transcriptomic dose-response to ionizing radiation in zebrafish with batch effect — zebraf","text":"zebraf contains list three objects, zebraf$counts integer matrix counts reads (1000 rows sample pf 1000 transcripts 16 columns 16 sampels), zebraf$dose, numeric vector coding dose sample zebraf$batch factor coding batch sample.","code":""},{"path":[]},{"path":"/reference/zebraf.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transcriptomic dose-response to ionizing radiation in zebrafish with batch effect — zebraf","text":"Murat El Houdigui, S., Adam-Guillermin, C., Loro, G., Arcanjo, C., Frelon, S., Floriani, M., ... & Armant, O. 2019. systems biology approach reveals neuronal muscle developmental defects chronic exposure ionising radiation zebrafish. Scientific reports, 9(1), 1-15.","code":""},{"path":"/reference/zebraf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Transcriptomic dose-response to ionizing radiation in zebrafish with batch effect — zebraf","text":"Zhang, Y., Parmigiani, G., & Johnson, W. E. (2020). ComBat-seq: batch effect adjustment RNA-seq count data. NAR genomics bioinformatics, 2(3), lqaa078.","code":""},{"path":"/reference/zebraf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transcriptomic dose-response to ionizing radiation in zebrafish with batch effect — zebraf","text":"","code":"# (1) load of data # data(zebraf) str(zebraf) #> List of 3 #> $ counts: int [1:1000, 1:16] 453 331 897 12 326 533 1948 904 583 154 ... #> ..- attr(*, \"dimnames\")=List of 2 #> .. ..$ : chr [1:1000] \"ENSDARG00000102141\" \"ENSDARG00000102123\" \"ENSDARG00000114503\" \"ENSDARG00000115971\" ... #> .. ..$ : chr [1:16] \"I10_05mG_E5\" \"I10_05mG_E6\" \"I10_05mG_E7\" \"I10_C5\" ... #> $ dose : num [1:16] 500 500 500 0 0 0 0 50000 50000 50000 ... #> $ batch : Factor w/ 2 levels \"I10\",\"I17\": 1 1 1 1 1 1 1 2 2 2 ... # (2) formating of data for use in DRomics # data4DRomics <- formatdata4DRomics(signalmatrix = zebraf$counts, dose = zebraf$dose) # (3) Normalization and transformation of data followed # by PCA plot with vizualisation of the batch effect # o <- RNAseqdata(data4DRomics, transfo.method = \"vst\") #> converting counts to integer mode #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. PCAdataplot(o, batch = zebraf$batch) # \\donttest{ PCAdataplot(o, label = TRUE) # (4) Batch effect correction using ComBat_seq{sva} # if(!requireNamespace(\"sva\", quietly = TRUE)) { BECcounts <- ComBat_seq(as.matrix(o$raw.counts), batch = as.factor(zebraf$batch), group = as.factor(o$dose)) BECdata4DRomics <- formatdata4DRomics(signalmatrix = BECcounts, dose = o$dose) (o.BEC <- RNAseqdata(BECdata4DRomics, transfo.method = \"vst\")) plot(o.BEC) PCAdataplot(o.BEC, batch = zebraf$batch) PCAdataplot(o.BEC, label = TRUE) } # }"},{"path":"/news/index.html","id":"dromics-development-version","dir":"Changelog","previous_headings":"","what":"DRomics (development version)","title":"DRomics (development version)","text":"NEW FEATURES Add component output RNAseqdata, continuousomicdata(), continuousanchoringdata(), microarraydata() (data.sd, gives, item, sd response per condition - NA replicate condition). Add new argument drcfit(), named deltaAICminfromnullmodel, order relax requirements information criterion keep best fitted model (see ? drcfit()). Modification curvesplot() able put argument dose_log_transfo default TRUE functions. Curvesplot now use minimum maximum values chosen BMD fix rage theoretical curve calculated (plotted) ad chosen BMD required input function. Add argument dose_log_transfo plot.continuousanchoringdata(), default TRUE. plot x log scale, add label x axis. Add output drcfit named information.criterion.val information criterion value null model change names components (replacement AIC InfoCrit names). BUG FIXES Add sample names column names output formatdata4DRomics. Change default value range4boxplot (plot.RNAseqdata(), plot.continuousomicdata(), plot.microarraydata()) 0 instead 1e6 whiskers always go extrems.","code":""},{"path":"/news/index.html","id":"dromics-25-2","dir":"Changelog","previous_headings":"","what":"DRomics 2.5-2","title":"DRomics 2.5-2","text":"CRAN release: 2024-01-31 NEW FEATURES Put argument dose_log_transfo default TRUE functions plot.drcfit(), plotfit2pdf(), targetplot() BMD_log_transfo TRUE functions bmdplot(), bmdplotwithgradient() sensitivityplot(). Add argument BMD_log_transfo default TRUE functions plot.bmdcalc() plot.bmdboot(). Put argument scaling default TRUE curvesplot() bmdplotwithgradient(). Add xlab ylab plots curvesplot() (signal scaled signal y-axis) change color lab “scaled signal” plots bmdplotwithgradient() signal scaled. Add possibility (new argument addBMD curvesplot()) add points BMD-BMR values curvesplots put default TRUE. Add Peer Community Journal citation. Add function bmdfilter() proposing filters retain items associated best estimated BMD values DRomics workflow output. Add arguments line.size, line.alpha point.alpha sensitivityplot() bmdplot() Add free y scale plots residuals, make readable even anchoring data endpoints different orders magnitude. BUG FIXES Fix bug appeared occasionally bootstrap procedure (error bmdboot() due fail call uniroot()). Define scale nb items sensitivityplot() trendplot() get 4 integer values min max rounded 0.5 0.75 quartiles. Fix bug plotfit2pdf : now items appear order (p-value selection) even BMD values added plot fitted curves. Fix bug drcfit occur anchoring data sets many NA values.","code":""},{"path":"/news/index.html","id":"dromics-25-0","dir":"Changelog","previous_headings":"","what":"DRomics 2.5-0","title":"DRomics 2.5-0","text":"CRAN release: 2023-01-24 NEW FEATURES Add function selectgroups() select represented /sensitive groups focus biological interpretation. Add RNAseq data batch effect (zebraf) example use ComBat_seq{sva} correct batch effect. Add PCAdataplot() function visualize omic data. Add column named maxychange (maximal absolute y change () control) output drcfit() (bmdcalc() bmdboot()) Add argument named scaling curvesplot() bmdplotwithgradient() enables scaling shifted signal (y - y0) dividing maxychange (new output drcfit). Add function formatdata4DRomics() format data DRomics matrix signal measurements vector observed/tested doses. Add range4boxplot default fixed 1e6 arguments plot functions RNAseqdata(), microarraydata() continuousomicdata() objects, prevent automatic plot many outliers individual points produce lighter plot files. Change default value transfo.method RNAseqdata() (put “vst” number samples larger 30) BUG FIXES Make sensitivityplot works even BMDsummary given input (“first.quartile” defined default)","code":""},{"path":"/news/index.html","id":"dromics-24-0","dir":"Changelog","previous_headings":"","what":"DRomics 2.4-0","title":"DRomics 2.4-0","text":"CRAN release: 2022-01-06 NEW FEATURES Forbid use “ANOVA” method select items half doses without replicates (e.g. situ data) Add example data set named insitu_RNAseq_sample.txt tests examples Add arguments BMDoutput BMDtype plot.drcfit() plotfit2pdf make possible add BMD values confidence intervals plot fits. Add argument (enablesfequal0inGP) default TRUE drcfit(), enable simplification Gauss-probit model 5 parameters version f = 0 (corresponds probit model) prevent overfitting parameter f close 0 (evaluated using information criterion). Add argument (enablesfequal0inLGP) default TRUE drcfit(), enable simplification log-Gauss-probit model 5 parameters version f = 0 (corresponds log-probit model) prevent overfitting parameter f close 0 (evaluated using information criterion). Add argument (preventsfitsoutofrange) default TRUE drcfit() prevent fits biphasic models giving extreme value range observed signal, happen rare cases. Add defensive code microarraydata(), continuousomicdata(), continuousanchoringdata(), RNAseqdata(), argument backgrounddose added prevent use DRomics design dose zero. case observationnal data, prevent calculation BMDs extrapolation, doses considered corresponding background exposition must fixed 0, example using new argument.","code":""},{"path":"/news/index.html","id":"dromics-23-0","dir":"Changelog","previous_headings":"","what":"DRomics 2.3-0","title":"DRomics 2.3-0","text":"CRAN release: 2021-10-04 NEW FEATURES argument facetby2 added bmdplotwithgradient() curvesplot() able split plots rows columns using facet_grid(). Add function trendplot() plot repartition dose-response trend per group items. Add function sensitivityplot() plot various summaries BMD values per group items. Add function bmdplot() takes extendedres first argument trendplot(), bmdplotwithgradient(), sensitivityplot() curvesplot(). Removing drcfit() argument sigmoid.model confusing users useful common use package.","code":""},{"path":"/news/index.html","id":"dromics-22-0","dir":"Changelog","previous_headings":"","what":"DRomics 2.2-0","title":"DRomics 2.2-0","text":"CRAN release: 2021-02-09 NEW FEATURES second-order Akaike criterion (AICc) recommended prevent overfitting small number data poinst dose-response crives implemented defined default option argument information.criterion drcfit(). example file package (?DRomics) replaced vignette help use package Shiny application. Improvement computation low BMD values designs high ratio maximal minimal (non null) tested doses, add two arguments bmdcalc, minBMD ratio2switchinlog. Add two columns output bmdcalc (BMR.zSD BMR.xfold) Add function plotfit2pdf() plot fits (residual plots) pdf file, using raw scale log scale dose removing option saveplot2pdf drcfit(). Replacement class ‘metabolomicdata’ class ‘continuousomicdata’ add function continuousomicdata() called metabolomicdata() used types continuous omics data proteomics data. default color changed bmdplotwithgradient() (green replaced blue color blind people) Removing three four datasets Zou et al. 2017 make package lighter Add test residuals heteroscedasticity output drcfit: residualtests BUG FIXES handling RNAseqdata() cases vst() may give stop message.","code":""},{"path":"/news/index.html","id":"dromics-21-3","dir":"Changelog","previous_headings":"","what":"DRomics 2.1-3","title":"DRomics 2.1-3","text":"CRAN release: 2020-09-23 NEW FEATURES Add function bmdplotwithgradient() Add function ecdfquantileplot() Add argument named information.criterion drcfit() choose use AIC BIC best fit model selection process. Add possibility enter data R object class data.frame Add published datasets (Zhou et al. 2017, Larras et al. 2020) corresponding help pages Add function continuousanchoringdata modification itemselect() enable selection significant responses continuous anchoring data. Add argument dose_log_transfo plot.drcfit enable use log tranformation x-axis. Add element list drcfit() output : unfitres giving information selected items modelling step successful Add function plot raw data target items optionally fitted curves items selected step 2 step 3 successful (new function targetplot()). Add argument transfo.blind RNAseqdata() Add argument free.y.scales curvesplot() enable free y scales facets dose_log_transfo use x log scale plot calculation signal. Add examples DRomics.Rd help multi-omics approach Add argument round.counts enable rounding read counts come Kallisto Salmon. BUG FIXES make direct use varianceStabilizingTransformation() automatic small RNAseq data sets (low number items: < 1000) fix bug RNAseqdata() occured using vst() small datasets.","code":""},{"path":"/news/index.html","id":"dromics-20-1","dir":"Changelog","previous_headings":"","what":"DRomics 2.0-1","title":"DRomics 2.0-1","text":"CRAN release: 2019-09-16 NEW FEATURES Add filter itemselect(), exclude selection items high proportion non detected values (assuming imputed common minimum value). Add argument point.type enables change point type , ecdfplotwithCI coding given factor. Add argument plot.type function plot.drcfit() enable residual plots.","code":""},{"path":"/news/index.html","id":"dromics-20-0","dir":"Changelog","previous_headings":"","what":"DRomics 2.0-0","title":"DRomics 2.0-0","text":"NEW FEATURES Replacement function omicdata() function microarraydata() add two new data importation functions, RNAseqdata() metabolomicdata().","code":""},{"path":"/news/index.html","id":"dromics-11-3","dir":"Changelog","previous_headings":"","what":"DRomics 1.1-3","title":"DRomics 1.1-3","text":"NEW FEATURES Replacement argument named bytypology plot.bmdcalc argument named can taka three values, “none”, “trend”, “model” “typology”. Add function plot fitted curves (new function curvesplot()).","code":""},{"path":"/news/index.html","id":"dromics-11-2","dir":"Changelog","previous_headings":"","what":"DRomics 1.1-2","title":"DRomics 1.1-2","text":"NEW FEATURES Add function plot distribution variable ecdf plot, confidence intervals variable (new function ecdfplotwithCI)","code":""},{"path":"/news/index.html","id":"dromics-11-1","dir":"Changelog","previous_headings":"","what":"DRomics 1.1-1","title":"DRomics 1.1-1","text":"NEW FEATURES Add bootstrap computation confidence intervals benchmark doses (new function bmdboot) Add function plot distribution variable ecdf plot, confidence intervals variable (new function ecdfplotwithCI)","code":""},{"path":"/news/index.html","id":"dromics-10-1","dir":"Changelog","previous_headings":"","what":"DRomics 1.0-1","title":"DRomics 1.0-1","text":"NEW FEATURES Add column yextrem results drcfit (y value extremum biphasic curves)","code":""},{"path":"/news/index.html","id":"dromics-10-0","dir":"Changelog","previous_headings":"","what":"DRomics 1.0-0","title":"DRomics 1.0-0","text":"Initial release.","code":""}] +[{"path":"/articles/DRomics_vignette.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Overview of the DRomics package","text":"DRomics freely available tool dose-response (concentration-response) characterization omics data. especially dedicated omics data obtained using typical dose-response design, favoring great number tested doses (concentrations) rather great number replicates (need replicates use DRomics). first step consists importing, checking needed normalizing/transforming data (step 1), aim proposed workflow select monotonic /biphasic significantly responsive items (e.g. probes, contigs, metabolites) (step 2), choose best-fit model among predefined family monotonic biphasic models describe response selected item (step 3), derive benchmark dose concentration fitted curve (step 4). steps can performed R using DRomics functions, using shiny application named DRomics-shiny. available version, DRomics supports single-channel microarray data (log2 scale), RNAseq data (raw counts) continuous omics data (log scale), metabolomics data calculated AUC values (area curve), proteomics data expressed protein abundance peak intensity values. Proteomics data expressed spectral counts analyzed RNAseq data using raw counts carefully checking validity assumptions made processing RNAseq data. order link responses across biological levels based common method, DRomics also handles continuous apical data long meet use conditions least squares regression (homoscedastic Gaussian regression, see section least squares reminder needed). built environmental risk assessment context omics data often collected non-sequenced species species communities, DRomics provide annotation pipeline. annotation items selected DRomics may complex context, must done outside DRomics using databases KEGG Gene Ontology. DRomics functions can used help interpretation workflow results view biological annotation. enables multi-omics approach, comparison responses different levels organization (view common biological annotation). can also used compare responses one organization level, measured different experimental conditions (e.g. different time points). interpretation can performed R using DRomics functions, using second shiny application DRomicsInterpreter-shiny. vignette intended help users start using DRomics package. complementary reference manual can find details function package. first part vignette (Main workflow, steps 1 4) also help users first shiny application DRomics-shiny. second part (Help biological interpretation DRomics outputs) also help users second shiny application DRomicsInterpreter-shiny. shiny applications can used locally R session installation package required shiny tools (see DRomics web page need help install package: https://lbbe.univ-lyon1.fr/fr/dromics) want install package computer, can also launch two shiny applications shiny server lab, respectively https://lbbe-shiny.univ-lyon1.fr/DRomics/inst/DRomics-shiny/ https://lbbe-shiny.univ-lyon1.fr/DRomics/inst/DRomicsInterpreter-shiny/. want use R functions prefer use shiny applications, locally computer shiny server, can skip pieces code focus explanations outputs also given shiny applications. one day want go using R functions, recommend start whole R code corresponding analysis provided last page two shiny applications.","code":"# Installation of required shiny packages install.packages(c(\"shiny\", \"shinyBS\", \"shinycssloaders\", \"shinyjs\", \"shinyWidgets\", \"sortable\")) # Launch of the first shiny application DRomics-shiny shiny::runApp(system.file(\"DRomics-shiny\", package = \"DRomics\")) # Launch of the second shiny application DRomicsInterpreter-shiny shiny::runApp(system.file(\"DRomicsInterpreter-shiny\", package = \"DRomics\"))"},{"path":[]},{"path":[]},{"path":[]},{"path":"/articles/DRomics_vignette.html","id":"textfile","dir":"Articles","previous_headings":"Main workflow > Step 1: importation, check and normalization / transformation of data if needed > General format of imported data","what":"Importation of data from a unique text file","title":"Overview of the DRomics package","text":"Whatever type data imported DRomics (e.g. RNAseq, microarray, metabolomic data), data can imported .txt file (e.g. “mydata.txt”) organized one row per item (e.g. transcript, probe, metabolite) one column per sample. additional first row, name item identifier (e.g. “id”), must tested doses concentrations numeric format corresponding sample (example, triplicates treatment, first line “item”, 0, 0, 0, 0.1, 0.1, 0.1, etc.). additional first column must give identifier item (identifier probe, transcript, metabolite, …, name endpoint anchoring data), columns give responses item sample. file imported within DRomics using internal call function read.table() default field separator (sep argument) default decimal separator (dec argument “.”). remember, necessary, transform another decimal separator (e.g. “,”) “.” importing data. Different examples .txt files formatted DRomics workflow available package, one named “RNAseq_sample.txt”. can look data coded file using following code. use local dataset formatted way, use datafilename type \"yourchosenname.txt\".","code":"# Import the text file just to see what will be automatically imported datafilename <- system.file(\"extdata\", \"RNAseq_sample.txt\", package = \"DRomics\") # datafilename <- \"yourchosenname.txt\" # for a local file # Have a look of what information is coded in this file d <- read.table(file = datafilename, header = FALSE) nrow(d) ## [1] 1000 head(d) ## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 ## 1 RefSeq 0 0 0.22 0.22 0.22 0.67 0.67 0.67 2 ## 2 NM_144958 2072 2506 2519.00 2116.00 1999.00 2113.00 2219.00 2322.00 2359 ## 3 NR_102758 0 0 0.00 0.00 0.00 0.00 0.00 0.00 0 ## 4 NM_172405 198 265 250.00 245.00 212.00 206.00 227.00 246.00 265 ## 5 NM_029777 18 29 25.00 19.00 19.00 13.00 22.00 19.00 19 ## 6 NM_001130188 0 0 0.00 0.00 0.00 0.00 0.00 1.00 0 ## V11 V12 V13 V14 V15 ## 1 2 2 6 6 6 ## 2 1932 1705 2110 2311 2140 ## 3 0 0 0 0 0 ## 4 205 175 288 315 242 ## 5 26 16 26 32 33 ## 6 0 0 1 0 1"},{"path":"/articles/DRomics_vignette.html","id":"Robject","dir":"Articles","previous_headings":"Main workflow > Step 1: importation, check and normalization / transformation of data if needed > General format of imported data","what":"Importation of data as an R object","title":"Overview of the DRomics package","text":"Alternatively R object class data.frame can directly given input, corresponds output read.table(file, header = FALSE) file described previous section. can see example RNAseq data set available DRomics R object (named Zhou_kidney_pce) extended version (rows) previous dataset coded “RNAseq_sample.txt”. data already imported R different format one described , can use formatdata4DRomics() function build R object directly useable DRomics workflow. formatdata4DRomics() needs two arguments input: matrix data one row item one column sample, numeric vector coding dose sample. names samples can added third optional argument (see ?formatdata4DRomics details). example using RNAseq dataset package coded R object named zebraf. Whatever way format data, strongly recommend carefully look following sections check use good scale data, depends type measured signal (counts reads, fluorescence signal, …).","code":"# Load and look at the dataset directly coded as an R object data(Zhou_kidney_pce) nrow(Zhou_kidney_pce) ## [1] 33395 head(Zhou_kidney_pce) ## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 ## 1 RefSeq 0 0 0.22 0.22 0.22 0.67 0.67 0.67 2 ## 2 NM_144958 2072 2506 2519.00 2116.00 1999.00 2113.00 2219.00 2322.00 2359 ## 3 NR_102758 0 0 0.00 0.00 0.00 0.00 0.00 0.00 0 ## 4 NM_172405 198 265 250.00 245.00 212.00 206.00 227.00 246.00 265 ## 5 NM_029777 18 29 25.00 19.00 19.00 13.00 22.00 19.00 19 ## 6 NM_001130188 0 0 0.00 0.00 0.00 0.00 0.00 1.00 0 ## V11 V12 V13 V14 V15 ## 1 2 2 6 6 6 ## 2 1932 1705 2110 2311 2140 ## 3 0 0 0 0 0 ## 4 205 175 288 315 242 ## 5 26 16 26 32 33 ## 6 0 0 1 0 1 # Load and look at the data as initially coded data(zebraf) str(zebraf) ## List of 3 ## $ counts: int [1:1000, 1:16] 453 331 897 12 326 533 1948 904 583 154 ... ## ..- attr(*, \"dimnames\")=List of 2 ## .. ..$ : chr [1:1000] \"ENSDARG00000102141\" \"ENSDARG00000102123\" \"ENSDARG00000114503\" \"ENSDARG00000115971\" ... ## .. ..$ : chr [1:16] \"I10_05mG_E5\" \"I10_05mG_E6\" \"I10_05mG_E7\" \"I10_C5\" ... ## $ dose : num [1:16] 500 500 500 0 0 0 0 50000 50000 50000 ... ## $ batch : Factor w/ 2 levels \"I10\",\"I17\": 1 1 1 1 1 1 1 2 2 2 ... (samples <- colnames(zebraf$counts)) ## [1] \"I10_05mG_E5\" \"I10_05mG_E6\" \"I10_05mG_E7\" \"I10_C5\" \"I10_C6\" ## [6] \"I10_C7\" \"I10_C8\" \"I17_50mG_E5\" \"I17_50mG_E6\" \"I17_50mG_E8\" ## [11] \"I17_5mG_E5\" \"I17_5mG_E7\" \"I17_5mG_E8\" \"I17_C5\" \"I17_C7\" ## [16] \"I17_C8\" # Formatting of data for use in DRomics # data4DRomics <- formatdata4DRomics(signalmatrix = zebraf$counts, dose = zebraf$dose, samplenames = samples) # Look at the dataset coded as an R object nrow(data4DRomics) ## [1] 1001 head(data4DRomics) ## I10_05mG_E5 I10_05mG_E6 I10_05mG_E7 I10_C5 I10_C6 I10_C7 ## 1 item 500 500 500 0 0 0 ## 2 ENSDARG00000102141 453 656 590 636 529 453 ## 3 ENSDARG00000102123 331 505 401 465 488 430 ## 4 ENSDARG00000114503 897 1055 1073 1017 1022 992 ## 5 ENSDARG00000115971 12 23 24 21 40 32 ## 6 ENSDARG00000098311 326 544 416 484 330 297 ## I10_C8 I17_50mG_E5 I17_50mG_E6 I17_50mG_E8 I17_5mG_E5 I17_5mG_E7 I17_5mG_E8 ## 1 0 50000 50000 50000 5000 5000 5000 ## 2 566 790 557 1005 688 775 425 ## 3 582 498 427 815 450 675 307 ## 4 1124 1153 900 2115 1064 1555 824 ## 5 30 22 14 34 19 36 21 ## 6 396 502 380 1065 522 688 332 ## I17_C5 I17_C7 I17_C8 ## 1 0 0 0 ## 2 719 516 667 ## 3 522 475 541 ## 4 1449 1152 1341 ## 5 44 27 22 ## 6 666 471 506"},{"path":[]},{"path":"/articles/DRomics_vignette.html","id":"description-of-the-classical-types-of-data-handled-by-dromics","dir":"Articles","previous_headings":"Main workflow > Step 1: importation, check and normalization / transformation of data if needed > What types of data can be analyzed using DRomics ?","what":"Description of the classical types of data handled by DRomics","title":"Overview of the DRomics package","text":"DRomics offers possibility work different types omics data (see following subsections description) also continuous anchoring data. working omics data, lines data frame (except first one coding doses concentrations) correspond type data (e.g. raw counts RNAseq data). working anchoring data, different lines (except first one coding doses concentrations) correspond different endpoints may correspond different types data (e.g. biomass, length,..), assumed continuous data compatible Gaussian (normal) error model (transformation needed, e.g. logarithmic transformation) selection modeling steps (see section least squares need reminder condition). Three types omics data may imported DRomics using following functions: RNAseqdata() used import RNAseq counts reads (details look example RNAseq data), microarraydata() used import single-channel microarray data log2 scale (details look example microarray data), continuousomicdata() used import continuous omics data metabolomics data, proteomics data (expressed intensity),…, scale enables use Gaussian error model (details look example metabolomic omics data). also possible import DRomics continuous anchoring data measured apical level, especially sake comparison benchmark doses (see Step 4 definition BMD) estimated different levels organization using metrics. Nevertheless, one keep mind DRomics workflow optimized automatic analysis high throughput omics data (especially implying selection modeling steps high-dimensional data) tools may better suited sole analysis apical dose-response data (details look example continuous apical data). Steps 1 2 count data internally analysed using functions Bioconductor package DESeq2, continuous omics data (microarray data continuous omics data) internally analysed using functions Bioconductor package limma continuous anchoring data internally analysed using classical lm() function.","code":""},{"path":"/articles/DRomics_vignette.html","id":"RNAseqexample","dir":"Articles","previous_headings":"","what":"Overview of the DRomics package","title":"Overview of the DRomics package","text":"RNAseq data, imperatively imported raw counts (counts come Kallisto Salmon put add argument round.counts = TRUE order round ), choose transformation method used stabilize variance (“rlog” “vst”). example “vst” used make vignette quick compile, “rlog” recommended chosen default even computer intensive “vst” except number samples large (> 30) (encountered situ data example: see ?RNAseqdata section dedicated situ data details point). Whatever chosen method, data automatically normalized respect library size transformed log2 scale. plot output shows distribution signal contigs/genes, sample, normalization transformation data.","code":"RNAseqfilename <- system.file(\"extdata\", \"RNAseq_sample.txt\", package = \"DRomics\") # RNAseqfilename <- \"yourchosenname.txt\" # for a local file (o.RNAseq <- RNAseqdata(RNAseqfilename, transfo.method = \"vst\")) ## Elements of the experimental design in order to check the coding of the data: ## Tested doses and number of replicates for each dose: ## ## 0 0.22 0.67 2 6 ## 2 3 3 3 3 ## Number of items: 999 ## Identifiers of the first 20 items: ## [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" ## [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" ## [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" ## [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" ## Data were normalized with respect to library size and tranformed using ## the following method: vst plot(o.RNAseq, cex.main = 0.8, col = \"green\")"},{"path":"/articles/DRomics_vignette.html","id":"microarrayexample","dir":"Articles","previous_headings":"","what":"Overview of the DRomics package","title":"Overview of the DRomics package","text":"single-channel microarray data, imperatively imported log scale (classical recommended log2 scale), can choose array normalization methods (“cyclicloess”, “quantile”, “scale” “none”). example , “quantile” used make vignette quick compile, “cyclicloess” recommended chosen default even computer intensive others (see ?microarraydata details). plot output shows distribution signal probes, sample, normalization data.","code":"microarrayfilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package = \"DRomics\") # microarrayfilename <- \"yourchosenname.txt\" # for a local file (o.microarray <- microarraydata(microarrayfilename, norm.method = \"quantile\")) ## Elements of the experimental design in order to check the coding of the data: ## Tested doses and number of replicates for each dose: ## ## 0 0.69 1.223 2.148 3.774 6.631 ## 5 5 5 5 5 5 ## Number of items: 1000 ## Identifiers of the first 20 items: ## [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" ## [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" ## Data were normalized between arrays using the following method: quantile plot(o.microarray, cex.main = 0.8, col = \"green\")"},{"path":"/articles/DRomics_vignette.html","id":"metabolomicexample","dir":"Articles","previous_headings":"","what":"Overview of the DRomics package","title":"Overview of the DRomics package","text":"Neither normalization transformation provided function continuousomicdata(). pre-treatment metabolomic data must done importation data, data must imported log scale, can directly modelled using Gaussian (normal) error model. strong hypothesis required selection items dose-reponse modeling (see section least squares reminder needed). context multi-omics approach recommend use log2 transformation, instead classical log10 data, facilitate comparison results obtained transcriptomics data generally handled log2 scale. instance, basic procedure pre-treatment metabolomic data follow three steps described thereafter: ) removing metabolites proportion missing data (non detected) across samples high (20 50 percents according tolerance level); ii) retrieving missing values data using half minimum method (.e. half minimum value found metabolite across samples); iii) log-transformation values. scaling total intensity (normalization sum signals sample) another normalization necessary pertinent, recommend three previously described steps. plot output shows distribution signal metabolites, sample. deprecated metabolomicdata() function renamed continuousomicdata() recent versions package (keeping first name available) offer use continuous omic data proteomics data (expressed intensity) RT-qPCR data. metabolomic data, pre-treatment continuous omic data must done importation, data must imported scale enables use Gaussian error model strong hypothesis required selection items dose-response modeling.","code":"metabolofilename <- system.file(\"extdata\", \"metabolo_sample.txt\", package = \"DRomics\") # metabolofilename <- \"yourchosenname.txt\" # for a local file (o.metabolo <- continuousomicdata(metabolofilename)) ## Elements of the experimental design in order to check the coding of the data: ## Tested doses and number of replicates for each dose: ## ## 0 0.69 1.1 1.79 2.92 4.78 7.76 ## 10 6 2 2 2 6 2 ## Number of items: 109 ## Identifiers of the first 20 items: ## ## [1] \"P_2\" \"P_4\" \"P_5\" \"P_6\" \"P_7\" \"P_10\" \"P_11\" \"P_12\" \"P_14\" \"P_16\" ## [11] \"P_19\" \"P_21\" \"P_22\" \"P_26\" \"P_32\" \"P_34\" \"P_35\" \"P_36\" \"P_37\" \"P_38\" plot(o.metabolo, col = \"green\")"},{"path":"/articles/DRomics_vignette.html","id":"apicalexample","dir":"Articles","previous_headings":"","what":"Overview of the DRomics package","title":"Overview of the DRomics package","text":"transformation provided function continuousanchoringdata(). needed pre-treatment data must done importation data, can directly modelled using Gaussian error model. strong hypothesis required selection responsive endpoints dose-reponse modeling (see section least squares reminder needed). following example argument backgrounddose used specify doses equal 0.1 considered 0 DRomics workflow. Specifying argument necessary dose 0 data (see section situ data details point). data plot() function simply provides dose-response plot endpoint. default dose represented log scale, responses control (null dose, minus infinity log scale) appear half points Y-axis. can changed using argument dose_log_transfo .","code":"anchoringfilename <- system.file(\"extdata\", \"apical_anchoring.txt\", package = \"DRomics\") # anchoringfilename <- \"yourchosenname.txt\" # for a local file (o.anchoring <- continuousanchoringdata(anchoringfilename, backgrounddose = 0.1)) ## Elements of the experimental design in order to check the coding of the data: ## Tested doses and number of replicates for each dose: ## ## 0 2.4 3.8 6.2 10.1 16.5 26.8 43.5 70.7 ## 12 6 2 2 2 6 2 2 2 ## Number of endpoints: 2 ## Names of the endpoints: ## [1] \"growth\" \"photosynthesis\" plot(o.anchoring) + theme_bw() plot(o.anchoring, dose_log_transfo = FALSE) + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"specificdesigns","dir":"Articles","previous_headings":"Main workflow > Step 1: importation, check and normalization / transformation of data if needed > What types of data can be analyzed using DRomics ?","what":"Handling of data collected through specific designs","title":"Overview of the DRomics package","text":"DRomics workflow first developed data collected typical dose-response experiment, reasonable number tested doses (concentrations - least 4 addition control ideally 6 8) small number replicates per dose. Recently made modifications package make possible use designs 3 doses addition control even type design recommended dose-response modeling. also extended workflow handle situ (observational) data, replication, dose (concentration) controlled (see example situ data details). also now possible handle experimental data collected using design batch effect using DRomics together functions Bioconductor package sva correct batch effect selection modeling steps. also developed PCAplot() function help visualizing batch effect impact batch effect correction (BEC) data (see example RNAseq data experiment batch effect details handle case see ?PCAplot details specific function also used identify potential outlier samples).","code":""},{"path":"/articles/DRomics_vignette.html","id":"insitudata","dir":"Articles","previous_headings":"","what":"Overview of the DRomics package","title":"Overview of the DRomics package","text":"One problem may occur particular situ data, absence real control samples, corresponding strictly null exposure dose concentration. prevent hazardous calculation BMD (see Step 4 definition BMD) extrapolation case, one use argument backgrounddose define maximal measured dose can considered negligible dose. doses equal value given backgrounddose fixed 0, considered background level exposition. situ data (generally data large number samples), use rlog transformation RNAseqdata() recommended, speed reason likely encounter problem rlog transformation case outliers case (see https://support.bioconductor.org/p/105334/ explanation author DESeq2 want see example problems may appear outliers case, just force transfo.method \"rlog\" following example). plot output shows distribution signal contigs, sample (box plots stuck due large number samples), normalization transformation data.","code":"datafilename <- system.file(\"extdata\", \"insitu_RNAseq_sample.txt\", package=\"DRomics\") # Importation of data specifying that observed doses below the background dose # fixed here to 2e-2 will be considered as null dose to have a control (o.insitu <- RNAseqdata(datafilename, backgrounddose = 2e-2, transfo.method = \"vst\")) ## Elements of the experimental design in order to check the coding of the data: ## Tested doses and number of replicates for each dose: ## ## 0 0.0205 0.0216 0.0248 0.0272 0.0322 0.0339 0.0387 0.0432 0.0463 ## 25 1 1 1 1 1 1 1 1 1 ## 0.04866 0.0528 0.0726 0.101 0.112 0.1122 0.167 2.089 2.474 2.892 ## 1 1 3 1 1 1 1 1 1 1 ## 2.899 2.904 3.008 3.16 3.199 3.251 3.323 3.483 3.604 4.484 ## 1 1 1 1 1 1 1 1 1 1 ## 4.917 4.924 5.509 8.53 9.16 9.38 9.5 9.83 10.35 11.06 ## 1 1 1 1 1 1 1 1 1 1 ## 11.34 11.94 12.11 12.39 13.51 14.8 16.26 16.89 18.2 18.28 ## 1 1 1 1 1 1 1 1 1 1 ## 19.14 20.62 20.7 21.13 23.0348 23.69 24.15 27.06 28.05 29.88 ## 1 1 1 1 1 1 1 1 1 1 ## 36.28 ## 1 ## Number of items: 1000 ## Identifiers of the first 20 items: ## [1] \"N00000000001_c0_g1\" \"N00000000002_c0_g1\" \"N00000000003_c0_g1\" ## [4] \"N00000000004_c0_g1\" \"N00000000005_c0_g1\" \"N00000000006_c0_g1\" ## [7] \"N00000000008_c0_g1\" \"N10000_c0_g1\" \"N10000_c0_g1.1\" ## [10] \"N10001_c0_g1\" \"N10008_c1_g1\" \"N10010_c0_g1\" ## [13] \"N10010_c1_g1\" \"N10011_c0_g1\" \"N10012_c0_g1\" ## [16] \"N100156_c0_g1\" \"N1001_c0_g1\" \"N10021_c0_g1\" ## [19] \"N10021_c0_g1.1\" \"N10021_c0_g1.2\" ## Data were normalized with respect to library size and tranformed using ## the following method: vst plot(o.insitu)"},{"path":"/articles/DRomics_vignette.html","id":"batcheffect","dir":"Articles","previous_headings":"","what":"Overview of the DRomics package","title":"Overview of the DRomics package","text":"omics data collected design known potential batch effect, DRomics function PCAplot() can used example visualize impact batch effect data. seems necessary, functions specific packages can used perform batch effect correction (BEC). recommend use functions ComBat() ComBat_seq() Bioconductor sva package purpose, respectively microarray (continuous omic data) RNAseq data. sva Bioconductor package, must installed way DESeq2 limma previously loaded. needed look DRomics web page get good instruction install Bioconductor packages: https://lbbe.univ-lyon1.fr/fr/dromics). example using ComBat-seq() RNAseq data batch effect. sva package import RNAseq data format DRomics, necessary use DRomics function formatdata4DRomics() interoperate ComBat-seq DRomics functions (see section importation R object details function ?formatdata4DRomics). appears design data obtained using batches, controlled condition (null dose) appearing batches. PCA plot shows impact batch effect, clearly appears controls (red points) obtained two different batches. PCA plot BEC shows impact correction batch effect visible controls (red points).","code":"# Load of data data(zebraf) str(zebraf) ## List of 3 ## $ counts: int [1:1000, 1:16] 453 331 897 12 326 533 1948 904 583 154 ... ## ..- attr(*, \"dimnames\")=List of 2 ## .. ..$ : chr [1:1000] \"ENSDARG00000102141\" \"ENSDARG00000102123\" \"ENSDARG00000114503\" \"ENSDARG00000115971\" ... ## .. ..$ : chr [1:16] \"I10_05mG_E5\" \"I10_05mG_E6\" \"I10_05mG_E7\" \"I10_C5\" ... ## $ dose : num [1:16] 500 500 500 0 0 0 0 50000 50000 50000 ... ## $ batch : Factor w/ 2 levels \"I10\",\"I17\": 1 1 1 1 1 1 1 2 2 2 ... # Look at the design of this dataset xtabs(~ zebraf$dose + zebraf$batch) ## zebraf$batch ## zebraf$dose I10 I17 ## 0 4 3 ## 500 3 0 ## 5000 0 3 ## 50000 0 3 # Formating of data using the formatdata4DRomics() function data4DRomics <- formatdata4DRomics(signalmatrix = zebraf$counts, dose = zebraf$dose) # Importation of data just to use DRomics functions # As only raw data will be given to ComBat_seq after (o <- RNAseqdata(data4DRomics)) ## Just wait, the transformation using regularized logarithm (rlog) may ## take a few minutes. ## Elements of the experimental design in order to check the coding of the data: ## Tested doses and number of replicates for each dose: ## ## 0 500 5000 50000 ## 7 3 3 3 ## Number of items: 1000 ## Identifiers of the first 20 items: ## [1] \"ENSDARG00000102141\" \"ENSDARG00000102123\" \"ENSDARG00000114503\" ## [4] \"ENSDARG00000115971\" \"ENSDARG00000098311\" \"ENSDARG00000104839\" ## [7] \"ENSDARG00000100143\" \"ENSDARG00000102474\" \"ENSDARG00000104049\" ## [10] \"ENSDARG00000102226\" \"ENSDARG00000103095\" \"ENSDARG00000102128\" ## [13] \"ENSDARG00000110470\" \"ENSDARG00000100422\" \"ENSDARG00000104632\" ## [16] \"ENSDARG00000100660\" \"ENSDARG00000113107\" \"ENSDARG00000099787\" ## [19] \"ENSDARG00000112451\" \"ENSDARG00000070546\" ## Data were normalized with respect to library size and tranformed using ## the following method: rlog # PCA plot with the sample labels PCAdataplot(o, label = TRUE) + theme_bw() # PCA plot to visualize the batch effect PCAdataplot(o, batch = zebraf$batch) + theme_bw() # Batch effect correction using ComBat_seq{sva} require(sva) BECcounts <- ComBat_seq(as.matrix(o$raw.counts), batch = as.factor(zebraf$batch), group = as.factor(o$dose)) # Formating of data after batch effect correction BECdata4DRomics <- formatdata4DRomics(signalmatrix = BECcounts, dose = o$dose) o.BEC <- RNAseqdata(BECdata4DRomics) ## Just wait, the transformation using regularized logarithm (rlog) may ## take a few minutes. # PCA plot after batch effect correction PCAdataplot(o.BEC, batch = zebraf$batch) + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"step2","dir":"Articles","previous_headings":"Main workflow","what":"Step 2: selection of significantly responding items","title":"Overview of the DRomics package","text":"second step workflow, function itemselect() must used simply taking input first argument output function used step 1 (output RNAseqdata(), microarraydata(), continuousomicdata() continuousanchoringdata()). example microarray data. false discovery rate (FDR) corresponds expected proportion items falsely detected responsive. large data set important define selection step based FDR reduce number items processed, also remove noisy dose-response signals may impair quality results. recommend set value 0.001 0.1 depending initial number items. number high (several tens thousands), recommend FDR less 0.05 (0.001 0.01) increase robustness results (Larras et al. 2018). Concerning method used selection, recommend default choice (“quadratic”) typical omics dose-response design (many doses/concentrations replicates per condition). enables selection monotonic biphasic dose-response relationships. want focus monotonic dose-response relationships, “linear” method chosen. design small number doses/concentrations many replicates (optimal dose-response modeling), “ANOVA” method preferable. situ data (observational data without replicates due uncontrolled dose), trend tests proposed use ANOVA test absence replicates conditions reasonable. three methods proposed selection step based use simple model (quadratic,linear ANOVA-type) linking signal dose rank scale. model internally fitted data empirical Bayesian approach using respective packages DESeq2 limma RNAseq data microarray continuous omics data, classical linear regression using lm() function continuous anchoring data. adjustment p-values according specified FDR performed case, even continuous anchoring data, ensure unicity workflow independently type data. See ?itemselect details Larras et al. 2018 comparison three proposed methods example. easy, using example package VennDiagram, compare selection items obtained using two different methods, following example.","code":"(s_quad <- itemselect(o.microarray, select.method = \"quadratic\", FDR = 0.01)) ## Number of selected items using a quadratic trend test with an FDR of 0.01: 150 ## Identifiers of the first 20 most responsive items: ## [1] \"383.2\" \"384.2\" \"363.1\" \"383.1\" \"384.1\" \"363.2\" \"364.2\" \"364.1\" \"300.2\" ## [10] \"301.1\" \"300.1\" \"301.2\" \"263.2\" \"27.2\" \"25.1\" \"368.1\" \"351.1\" \"15\" ## [19] \"370\" \"350.2\" require(VennDiagram) s_lin <- itemselect(o.microarray, select.method = \"linear\", FDR = 0.01) index_quad <- s_quad$selectindex index_lin <- s_lin$selectindex plot(c(0,0), c(1,1), type = \"n\", xaxt = \"n\", yaxt = \"n\", bty = \"n\", xlab = \"\", ylab = \"\") draw.pairwise.venn(area1 = length(index_quad), area2 = length(index_lin), cross.area = length(which(index_quad %in% index_lin)), category = c(\"quadratic trend test\", \"linear trend test\"), cat.col=c(\"cyan3\", \"darkorange1\"), col=c(\"black\", \"black\"), fill = c(\"cyan3\", \"darkorange1\"), lty = \"blank\", cat.pos = c(1,11))"},{"path":[]},{"path":"/articles/DRomics_vignette.html","id":"fit-of-the-best-model","dir":"Articles","previous_headings":"Main workflow > Step 3: fit of dose-response models, choice of the best fit for each curve","what":"Fit of the best model","title":"Overview of the DRomics package","text":"Step 3 function drcfit() simply takes input first argument output itemselect(). item selected Step 2, model best fits dose-response data chosen among family five simple models built describe wide variety monotonic biphasic dose-response curves (DRC) (exclusively monotonic biphasic curves : flexible models polynomial third fourth order classical polynomial models deliberately considered). complete description models see last section Step 3 Larras et al. 2018. procedure used select best fit based information criterion described Larras et al. 2018 ?drcfit. classical former default option AIC (Akaike criterion - default information criterion used DRomics versions < 2.2-0) replaced default use AICc (second-order Akaike criterion) order prevent overfitting may occur dose-response designs small number data points, recommended now classically done regression (Hurvich Tsai, 1989; Burnham Anderson DR, 2004). call drcfit() function may take time number pre-selected items large, default progressbar provided. arguments function can used specify parallel computing accelerate computation (see ?drcfit details). following can see first lines output data frame example (see ?drcfit complete description columns output data frame.) output data frame provides information item, best-fit model, parameter values, standard residual error (SDres) (see section least squares definition), coordinates particular points, trend curve (among increasing, decreasing, U-shaped, bell-shaped). extensive description outputs complete DRomics workflow provided last section main workflow. Note number items successfully fitted (output Step 3) often smaller number items selected Step 2, selected items, models may fail converge fail significantly better describe data constant model.","code":"(f <- drcfit(s_quad, progressbar = FALSE)) ## Results of the fitting using the AICc to select the best fit model ## 22 dose-response curves out of 150 previously selected were removed ## because no model could be fitted reliably. ## Distribution of the chosen models among the 128 fitted dose-response curves: ## ## Hill linear exponential Gauss-probit ## 2 29 39 49 ## log-Gauss-probit ## 9 ## Distribution of the trends (curve shapes) among the 128 fitted dose-response curves: ## ## bell dec inc U ## 38 37 34 19 head(f$fitres) ## id irow adjpvalue model nbpar b c d e f SDres ## 1 383.2 725 2.08e-07 Gauss-probit 4 5.5836 8.58 8.58 1.70 3.62 0.157 ## 2 384.2 727 2.08e-07 exponential 3 -0.0298 NA 12.24 1.76 NA 0.160 ## 3 363.1 686 2.24e-07 exponential 3 -0.2058 NA 9.10 3.11 NA 0.218 ## 4 383.1 724 2.24e-07 Gauss-probit 4 5.4879 8.75 8.75 1.72 3.58 0.169 ## 5 384.1 726 3.41e-07 Gauss-probit 4 6.8453 7.26 7.26 1.77 5.01 0.158 ## 6 363.2 687 7.01e-07 exponential 3 -0.1467 NA 9.10 2.77 NA 0.206 ## typology trend y0 yatdosemax yrange maxychange xextrem yextrem ## 1 GP.bell bell 12.0 11.03 1.17 1.004 1.70 12.2 ## 2 E.dec.concave dec 12.2 10.99 1.25 1.251 NA NA ## 3 E.dec.concave dec 9.1 7.58 1.53 1.527 NA NA ## 4 GP.bell bell 12.2 11.15 1.18 1.012 1.72 12.3 ## 5 GP.bell bell 12.1 11.15 1.11 0.949 1.77 12.3 ## 6 E.dec.concave dec 9.1 7.64 1.46 1.461 NA NA"},{"path":"/articles/DRomics_vignette.html","id":"plot-of-fitted-curves","dir":"Articles","previous_headings":"Main workflow > Step 3: fit of dose-response models, choice of the best fit for each curve","what":"Plot of fitted curves","title":"Overview of the DRomics package","text":"default plot() function used output drcfit() function provides first 20 fitted curves (ones specify using argument items) observed points. Fitted curves represented red, replicates represented open circles means replicates dose/concentration represented solid circles. fitted curves may saved pdf file using plotfit2pdf() function (see ?drcfit). fitted curves default represented using log scale dose/concentration, suited common cases range observed doses/concentrations wide /tested doses/concentrations obtained dilutions. observations control appear differently observations, half circles y-axis, remind true value minus infinity log scale. Use dose_log_transfo = FALSE keep raw scale doses (see ). Another specific plot function named targetplot() can used plot targeted items, whether selected step 2 fitted step 3. See example details ?targetplot. example, default arbitrary space y-axis (points control) points first non null doses enlarged fixing limits x-axis :","code":"plot(f) targetitems <- c(\"88.1\", \"1\", \"3\", \"15\") targetplot(targetitems, f = f) + scale_x_log10(limits = c(0.2, 10))"},{"path":"/articles/DRomics_vignette.html","id":"residuals","dir":"Articles","previous_headings":"Main workflow > Step 3: fit of dose-response models, choice of the best fit for each curve","what":"Plot of residuals","title":"Overview of the DRomics package","text":"check assumption Gaussian error model (see section least squares), two types residual plots can used, \"dose_residuals\" plot residuals observed doses/concentrations, \"fitted_residuals\" plot residuals fitted values modeled signal. residual plots items may also saved pdf file using plotfit2pdf() function (see ?drcfit).","code":"plot(f, plot.type = \"dose_residuals\")"},{"path":"/articles/DRomics_vignette.html","id":"models","dir":"Articles","previous_headings":"Main workflow > Step 3: fit of dose-response models, choice of the best fit for each curve","what":"Description of the family of dose-response models fitted in DRomics","title":"Overview of the DRomics package","text":"best fit model chosen among five following models describing observed signal yy function xx dose (concentration): linear model: y=d+b×xy = d + b \\times x 2 parameters, bb slope dd mean signal control. e>0e>0 dose response curve - DRC - increasing b>0b>0 decreasing b<0b<0, asymptote high doses. e<0e<0 DRC increasing b<0b<0 decreasing b>0b>0, asymptote d−bd-b high doses. Hill model: y=c+d−c1+(xe)= c + \\frac{d-c}{1+(\\frac{x}{e})^b} 4 parameters,bb (>0>0) shape parameter, cc asymtotic signal high doses, dd mean signal control, ee (>0>0) dose inflection point sigmoid. Gauss-probit model built sum Gauss probit part sharing parameters defined :y=f×exp(−0.5(x−eb)2)+d+(c−d)×Φ(x−eb)y = f \\times exp\\left(-0.5 \\left(\\frac{x-e}{b}\\right)^2\\right) +d+(c-d) \\times \\Phi\\left(\\frac{x-e}{b}\\right) 5 parameters,bb (>0>0) shape parameter corresponding standard deviation Gauss part model, cc asymtotic signal high doses, dd asymptotic signal left DRC (generally corresponding fictive negative dose), ee (>0>0) shape parameter corresponding mean Gauss part model, ff amplitude sign Gauss part model (model U-shaped f<0f<0 bell-shaped f<0f<0).Φ\\Phi represents cumulative distribution function (CDF) standard Gauss (also named normal Gaussian) distribution. model encompasses two simplifed versions 4 parameters, one monotonic (f=0f=0) one symmetrical asymptotes (c=dc=d). log-Gauss-probit model, variant previous one log scale dose: y=f×exp(−0.5(ln(x)−ln(e)b)2)+d+(c−d)×Φ(ln(x)−ln(e)b)y = f \\times exp\\left(-0.5\\left(\\frac{ln(x)-ln(e)}{b}\\right)^2\\right) +d+(c-d) \\times \\Phi\\left(\\frac{ln(x)-ln(e)}{b}\\right) 5 parameters,bb (>0>0) shape parameter corresponding standard deviation Gauss part model, cc asymtotic signal high dose, dd asymptotic signal left DRC, reached control (ln(x)=ln(0)=−∞ln(x) = ln(0) = -\\infty), ln(e)ln(e) (>0>0) shape parameter corresponding mean Gauss part model, ff amplitude sign Gauss part model (model U-shaped f<0f<0 bell-shaped f<0f<0).Φ\\Phi represents cumulative distribution function (CDF) standard Gauss distribution. previous one, model encompasses two simplifed versions 4 parameters, one monotonic (f=0f=0) one symmetrical asymptotes (c=dc=d). family five models built able describe wide range monotonic biphasic DRC. following plot represented typologies curves can described using models, depending definition parameters. following plot curves represented signal y-axis raw dose x-axis. range tested observed doses often large, decided plot model fits default using log scale doses. shape models transformed log x-scale, especially linear model appear straight line .","code":""},{"path":"/articles/DRomics_vignette.html","id":"leastsquares","dir":"Articles","previous_headings":"Main workflow > Step 3: fit of dose-response models, choice of the best fit for each curve","what":"Reminder on least squares regression","title":"Overview of the DRomics package","text":"important using DRomics mind dose-response models fitted using least squares regression, assuming additive Gaussian (normal) error model observed signal. scale import data important: log (pseudo-log) transformation may necessary meet use conditions model types data. Let us recall formulation Gaussian model defining signal (transformation needed) yy function dose (concentration) xx, ff one five models previously described, θ\\theta vector parameters (length 2 5). y=f(x,θ)+ϵy = f(x, \\theta) + \\epsilon ϵ∼N(0,σ)\\epsilon \\sim N(0, \\sigma)N(0,σ)N(0, \\sigma) representing Gaussian (normal) distribution mean 00 standard deviation (SD) σ\\sigma. model, residual standard deviation σ\\sigma assumed constant. classical “homoscedasticity” hypothesis (see following figure illustration). examination residuals (see section plot residuals) good way check error model strongly violated data.","code":""},{"path":[]},{"path":"/articles/DRomics_vignette.html","id":"calculation-of-bmd","dir":"Articles","previous_headings":"Main workflow > Step 4: calculation of benchmark doses (BMD)","what":"Calculation of BMD","title":"Overview of the DRomics package","text":"two types benchmark doses (BMD-zSD BMD-xfold) proposed EFSA (2017) systematically calculated fitted dose-response curve using function bmdcalc() output drcfit() function first argument, strongly recommend use first one (BMD-zSD) reasons explained Larras et al. 2018 (see ?bmdcalc details function). recommended BMD-zSD,argument zz, default 1, used define BMD-zSD dose response reaching BMR (benchmark response) defined BMR=y0±z×SDBMR = y_0 \\pm z \\times SD y0y_0 level control given dose-response fitted model SDSD residual standard deviation dose-response fitted model (also named σ\\sigma previous mathematical definition Gaussian model). less recommended BMD-xfold, argument xx, default 10 (10%), used define BMD-xfold dose response reaching BMR defined BMR=y0±x100×y0BMR = y_0 \\pm \\frac{x}{100} \\times y_0. second BMD version take account residual standard deviation, strongly dependent magnitude y0y_0, may problem signal control close 0, rare omics data classically handled log scale. following can see first lines output data frame function bmdcalc() example. BMD values coded NA BMR stands within range response values defined model outside range tested doses, NaN BMR stands outside range response values defined model due asymptotes. low BMD values obtained extrapolation 0 smallest non null tested dose, correspond sensitive items (want exclude), thresholded minBMD, argument default fixed smallest non null tested dose divided 100, can fixed user considers negligible dose. extensive description outputs complete DRomics workflow provided last section main workflow. can also see ?bmdcalc complete description arguments columns output data frame.","code":"(r <- bmdcalc(f, z = 1, x = 10)) ## 1 BMD-xfold values and 0 BMD-zSD values are not defined (coded NaN as ## the BMR stands outside the range of response values defined by the model). ## 60 BMD-xfold values and 0 BMD-zSD values could not be calculated (coded ## NA as the BMR stands within the range of response values defined by the ## model but outside the range of tested doses). head(r$res) ## id irow adjpvalue model nbpar b c d e f SDres ## 1 383.2 725 2.08e-07 Gauss-probit 4 5.5836 8.58 8.58 1.70 3.62 0.157 ## 2 384.2 727 2.08e-07 exponential 3 -0.0298 NA 12.24 1.76 NA 0.160 ## 3 363.1 686 2.24e-07 exponential 3 -0.2058 NA 9.10 3.11 NA 0.218 ## 4 383.1 724 2.24e-07 Gauss-probit 4 5.4879 8.75 8.75 1.72 3.58 0.169 ## 5 384.1 726 3.41e-07 Gauss-probit 4 6.8453 7.26 7.26 1.77 5.01 0.158 ## 6 363.2 687 7.01e-07 exponential 3 -0.1467 NA 9.10 2.77 NA 0.206 ## typology trend y0 yatdosemax yrange maxychange xextrem yextrem BMD.zSD ## 1 GP.bell bell 12.0 11.03 1.17 1.004 1.70 12.2 1.33 ## 2 E.dec.concave dec 12.2 10.99 1.25 1.251 NA NA 3.26 ## 3 E.dec.concave dec 9.1 7.58 1.53 1.527 NA NA 2.25 ## 4 GP.bell bell 12.2 11.15 1.18 1.012 1.72 12.3 1.52 ## 5 GP.bell bell 12.1 11.15 1.11 0.949 1.77 12.3 1.41 ## 6 E.dec.concave dec 9.1 7.64 1.46 1.461 NA NA 2.43 ## BMR.zSD BMD.xfold BMR.xfold ## 1 12.19 NA 10.83 ## 2 12.08 6.59 11.02 ## 3 8.89 5.26 8.19 ## 4 12.33 NA 10.95 ## 5 12.26 NA 10.89 ## 6 8.90 5.47 8.19"},{"path":"/articles/DRomics_vignette.html","id":"plots-of-the-bmd-distribution","dir":"Articles","previous_headings":"Main workflow > Step 4: calculation of benchmark doses (BMD)","what":"Plots of the BMD distribution","title":"Overview of the DRomics package","text":"default plot output bmdcalc() function provides distribution benchmark doses ECDF (Empirical Cumulative Density Function) plot chosen BMD (“zSD”” “xfold”). See example . Different alternative plots proposed (see ?bmdcalc details) can obtained using argument plottype choose type plot (“ecdf”, “hist” “density”) argument split plot example “trend”. can also use bmdplot() function make ECDF plot BMDs personalize (see ?bmdplot details). BMD ECDF plot one can add color gradient item coding intensity signal (shift control signal 0) function dose (see ?bmdplotwithgradient details example ). generally necessary use argument line.size manually adjust width lines plot default value always give satisfactory resut. also recommended (mandatory default option argument scaling) scale signal order focus shape dose-reponse curves amplitude signal change.","code":"plot(r, BMDtype = \"zSD\", plottype = \"ecdf\") + theme_bw() bmdplotwithgradient(r$res, BMDtype = \"zSD\", facetby = \"trend\", shapeby = \"model\", line.size = 1.2, scaling = TRUE)"},{"path":"/articles/DRomics_vignette.html","id":"bootstrap","dir":"Articles","previous_headings":"Main workflow > Step 4: calculation of benchmark doses (BMD)","what":"Calculation of confidence intervals on the BMDs by bootstrap","title":"Overview of the DRomics package","text":"Confidence intervals BMD values can calculated bootstrap. call function may take much time, default progressbar provided arguments can used specify parallel computing accelerate computation (see ?bmdboot details). example , small number iterations used just make vignette quick compile, default value argument niter (1000) considered minimal value obtain stable results. function gives output corresponding output bmdcalc() function completed bounds BMD confidence intervals (default 95% confidence intervals) number bootstrap iterations model successfully fitted data. extensive description outputs complete DRomics workflow provided last section main workflow. plot() function applied output bmdboot() function gives ECDF plot chosen BMD confidence interval BMD (see ?bmdcalc examples). default BMDs infinite confidence interval bound plotted.","code":"(b <- bmdboot(r, niter = 50, progressbar = FALSE)) ## Bootstrap confidence interval computation failed on 17 items among 128 ## due to lack of convergence of the model fit for a fraction of the ## bootstrapped samples greater than 0.5. ## For 4 BMD.zSD values and 68 BMD.xfold values among 128 at least one ## bound of the 95 percent confidence interval could not be computed due ## to some bootstrapped BMD values not reachable due to model asymptotes ## or reached outside the range of tested doses (bounds coded Inf)). head(b$res) ## id irow adjpvalue model nbpar b c d e f SDres ## 1 383.2 725 2.08e-07 Gauss-probit 4 5.5836 8.58 8.58 1.70 3.62 0.157 ## 2 384.2 727 2.08e-07 exponential 3 -0.0298 NA 12.24 1.76 NA 0.160 ## 3 363.1 686 2.24e-07 exponential 3 -0.2058 NA 9.10 3.11 NA 0.218 ## 4 383.1 724 2.24e-07 Gauss-probit 4 5.4879 8.75 8.75 1.72 3.58 0.169 ## 5 384.1 726 3.41e-07 Gauss-probit 4 6.8453 7.26 7.26 1.77 5.01 0.158 ## 6 363.2 687 7.01e-07 exponential 3 -0.1467 NA 9.10 2.77 NA 0.206 ## typology trend y0 yatdosemax yrange maxychange xextrem yextrem BMD.zSD ## 1 GP.bell bell 12.0 11.03 1.17 1.004 1.70 12.2 1.33 ## 2 E.dec.concave dec 12.2 10.99 1.25 1.251 NA NA 3.26 ## 3 E.dec.concave dec 9.1 7.58 1.53 1.527 NA NA 2.25 ## 4 GP.bell bell 12.2 11.15 1.18 1.012 1.72 12.3 1.52 ## 5 GP.bell bell 12.1 11.15 1.11 0.949 1.77 12.3 1.41 ## 6 E.dec.concave dec 9.1 7.64 1.46 1.461 NA NA 2.43 ## BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower ## 1 12.19 NA 10.83 0.489 3.93 Inf ## 2 12.08 6.59 11.02 1.724 4.58 6.40 ## 3 8.89 5.26 8.19 1.366 3.52 4.61 ## 4 12.33 NA 10.95 0.423 4.17 Inf ## 5 12.26 NA 10.89 0.481 4.36 Inf ## 6 8.90 5.47 8.19 1.334 3.31 4.75 ## BMD.xfold.upper nboot.successful ## 1 Inf 36 ## 2 Inf 47 ## 3 5.96 50 ## 4 Inf 36 ## 5 Inf 26 ## 6 6.00 50"},{"path":"/articles/DRomics_vignette.html","id":"bmdfilter","dir":"Articles","previous_headings":"Main workflow > Step 4: calculation of benchmark doses (BMD)","what":"Filtering BMDs according to estimation quality","title":"Overview of the DRomics package","text":"Using bmdfilter() function, possible use one three filters proposed retain items associated best estimated BMD values. default retained items BMD confidence interval defined (using \"CIdefined\") (excluding items bootstrap procedure failed). One can even restrictive retaining items BMD confidence interval within range tested/observed doses (using \"CIfinite\"), less restrictive (using \"BMDdefined\") requiring BMD point estimate must defined within range tested/observed doses. Let us recall bmdcalc() output, case BMD coded NA NaN. example application different filters based BMD-xfold values, chosen just better illustrate way filters work, far bad BMD-xfold estimations bad BMD-zSD estimations.","code":"# Plot of BMDs with no filtering subres <- bmdfilter(b$res, BMDfilter = \"none\") bmdplot(subres, BMDtype = \"xfold\", point.size = 2, point.alpha = 0.4, add.CI = TRUE, line.size = 0.4) + theme_bw() # Plot of items with defined BMD point estimate subres <- bmdfilter(b$res, BMDtype = \"xfold\", BMDfilter = \"definedBMD\") bmdplot(subres, BMDtype = \"xfold\", point.size = 2, point.alpha = 0.4, add.CI = TRUE, line.size = 0.4) + theme_bw() # Plot of items with defined BMD point estimate and CI bounds subres <- bmdfilter(b$res, BMDtype = \"xfold\", BMDfilter = \"definedCI\") bmdplot(subres, BMDtype = \"xfold\", point.size = 2, point.alpha = 0.4, add.CI = TRUE, line.size = 0.4) + theme_bw() # Plot of items with finite BMD point estimate and CI bounds subres <- bmdfilter(b$res, BMDtype = \"xfold\", BMDfilter = \"finiteCI\") bmdplot(subres, BMDtype = \"xfold\", point.size = 2, point.alpha = 0.4, add.CI = TRUE, line.size = 0.4) + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"plot-of-fitted-curves-with-bmd-values-and-confidence-intervals","dir":"Articles","previous_headings":"Main workflow > Step 4: calculation of benchmark doses (BMD)","what":"Plot of fitted curves with BMD values and confidence intervals","title":"Overview of the DRomics package","text":"possible add output bmdcalc() (bmdboot()) argument BMDoutput plot() function drcfit(), order add BMD values (defined) vertical line fitted curve, bounds confidence intervals (successfully calculated) two dashed lines. Horizontal dotted lines corresponding two BMR potential values also added. See example . fitted curves may also saved way pdf file using plotfit2pdf() function (see ?drcfit).","code":"# If you do not want to add the confidence intervals just replace b # the output of bmdboot() by r the output of bmdcalc() plot(f, BMDoutput = b)"},{"path":"/articles/DRomics_vignette.html","id":"plot-of-all-the-fitted-curves-in-one-figure-with-points-at-bmd-bmr-values","dir":"Articles","previous_headings":"Main workflow > Step 4: calculation of benchmark doses (BMD)","what":"Plot of all the fitted curves in one figure with points at BMD-BMR values","title":"Overview of the DRomics package","text":"possible use curvesplot() function plot fitted curves one figure add use curvesplot() function extensively described next parts corresponding help page. default plot curves scaled focus shape dose-response amplitude (add scaling = FALSE see curves without scaling) log dose scale used. following plot also added vertical lines corresponding tested doses plot add transparency visualize density curves shapes similar (especially case linear shapes). use package plotly make plot interactive can interesting example get identifiant curve choose group curves eliminate focus . can try following code get interactive version previous figure.","code":"tested.doses <- unique(f$omicdata$dose) g <- curvesplot(r$res, xmax = max(tested.doses), colorby = \"trend\", line.size = 0.8, line.alpha = 0.3, point.size = 2, point.alpha = 0.6) + geom_vline(xintercept = tested.doses, linetype = 2) + theme_bw() print(g) if (require(plotly)) { ggplotly(g) }"},{"path":"/articles/DRomics_vignette.html","id":"outputs","dir":"Articles","previous_headings":"Main workflow > Step 4: calculation of benchmark doses (BMD)","what":"Description of the outputs of the complete DRomics workflow","title":"Overview of the DRomics package","text":"output complete DRomics workflow, given b$res b output bmdboot(), output bmdfilter(b$res) (see previous section description BMD filtering options) data frame reporting results fit BMD computation selected item sorted ascending order adjusted p-values returned item selection step. columns data frame : id: item identifier irow: row number initial dataset adjpvalue: adjusted p-values returned item selection step model: best model fitted nbpar: number parameters best model (may smaller maximal number parameters model simplified version chosen) b, c, d, e, f, model parameter values SDres: residual standard deviation best model “H.inc” increasing Hill curves “H.dec” decreasing Hill curves “L.inc” increasing linear curves “L.dec” decreasing linear curves “E.inc.convex” increasing convex exponential curves “E.dec.concave” decreasing concave exponential curves “E.inc.concave” increasing concave exponential curves “E.dec.convex” decreasing convex exponential curves “GP.U” U-shape Gauss-probit curves “GP.bell” bell-shape Gauss-probit curves “GP.inc” increasing Gauss-probit curves “GP.dec” decreasing Gauss-probit curves “lGP.U” U-shape log-Gauss-probit curves “lGP.bell” bell-shape log-Gauss-probit curves “lGP.inc” increasing log-Gauss-probit curves “lGP.decreasing” decreasing log-Gauss-probit curves U shape bell shape increasing decreasing y0: y theoretical value control yatdosemax: theoretical y value maximal dose yrange: theoretical y range x within range tested doses maxychange: maximal absolute y change () control xextrem: biphasic curves, x value extremum reached yextrem: corresponding y value extremum BMD.zSD: BMD-zSD value BMR.zSD: corresponding BMR-zSD value BMD.xfold: BMD-xfold value BMR.xfold: corresponding BMR-xfold value nboot.successful: number bootstrap iterations model successfully fitted data incomplete version data frame also given end Step 3 (f$fitres f output drcfit()) bootstrap calculation BMD values (r$res r output bmdcalc()).","code":"str(b$res) ## 'data.frame': 128 obs. of 28 variables: ## $ id : chr \"383.2\" \"384.2\" \"363.1\" \"383.1\" ... ## $ irow : int 725 727 686 724 726 687 689 688 568 569 ... ## $ adjpvalue : num 2.08e-07 2.08e-07 2.24e-07 2.24e-07 3.41e-07 ... ## $ model : Factor w/ 5 levels \"Hill\",\"linear\",..: 4 3 3 4 4 3 3 3 3 3 ... ## $ nbpar : num 4 3 3 4 4 3 3 3 3 3 ... ## $ b : num 5.5836 -0.0298 -0.2058 5.4879 6.8453 ... ## $ c : num 8.58 NA NA 8.75 7.26 ... ## $ d : num 8.58 12.24 9.1 8.75 7.26 ... ## $ e : num 1.7 1.76 3.11 1.72 1.77 ... ## $ f : num 3.62 NA NA 3.58 5.01 ... ## $ SDres : num 0.157 0.16 0.218 0.169 0.158 ... ## $ typology : Factor w/ 12 levels \"E.dec.concave\",..: 5 1 1 5 5 1 1 1 2 2 ... ## $ trend : Factor w/ 4 levels \"bell\",\"dec\",\"inc\",..: 1 2 2 1 1 2 2 2 2 2 ... ## $ y0 : num 12 12.2 9.1 12.2 12.1 ... ## $ yatdosemax : num 11.03 10.99 7.58 11.15 11.15 ... ## $ yrange : num 1.17 1.25 1.53 1.18 1.11 ... ## $ maxychange : num 1.004 1.251 1.527 1.012 0.949 ... ## $ xextrem : num 1.7 NA NA 1.72 1.77 ... ## $ yextrem : num 12.2 NA NA 12.3 12.3 ... ## $ BMD.zSD : num 1.33 3.26 2.25 1.52 1.41 ... ## $ BMR.zSD : num 12.19 12.08 8.89 12.33 12.26 ... ## $ BMD.xfold : num NA 6.59 5.26 NA NA ... ## $ BMR.xfold : num 10.83 11.02 8.19 10.95 10.89 ... ## $ BMD.zSD.lower : num 0.489 1.724 1.366 0.423 0.481 ... ## $ BMD.zSD.upper : num 3.93 4.58 3.52 4.17 4.36 ... ## $ BMD.xfold.lower : num Inf 6.4 4.61 Inf Inf ... ## $ BMD.xfold.upper : num Inf Inf 5.96 Inf Inf ... ## $ nboot.successful: num 36 47 50 36 26 50 50 50 50 50 ..."},{"path":"/articles/DRomics_vignette.html","id":"interpreter","dir":"Articles","previous_headings":"","what":"Help for biological interpretation of DRomics outputs","title":"Overview of the DRomics package","text":"section illustrates functions developed DRomics help biological interpretation outputs. idea augment output data frame new column bringing biological information, generally provided biological annotation items (e.g. kegg pathway classes GO terms), use information organize visualisation DRomics output. shiny application DRomicsInterpreter-shiny can used implement steps described vignette without coding R. case, biological annotation items selected first DRomics workflow must previously done outside DRomics using database Gene Ontology (GO) kegg databases. section first present simple example metabolomic dataset [example two molecular levels] (#multilevels) using metabolomic transcriptomic data experiment, illustrate compare responses different experimental levels (example different molecular levels). different experimental levels also different time points, different experimental settings, different species, …","code":""},{"path":[]},{"path":"/articles/DRomics_vignette.html","id":"augmentation","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Interpretation of DRomics results in a simple case with only one data set obtained in one experimental condition","what":"Augmentation of the data frame of DRomics results with biological annotation","title":"Overview of the DRomics package","text":"augmentation done using DRomics functions, using simple R functions merge(). Nevertheless possible perform augmentation without coding R, using shiny application DRomicsInterpreter-shiny. Report introduction section see launch shiny application. example proceed: Import data frame DRomics results: output $res bmdcalc() bmdboot() functions Step 4 main DRomics workflow. step necessary previous steps done directly R, using DRomics package, described previously vignette (see section describing output data frame). example, order take real example took long time completely run, results stored package. Import data frame biological annotation (descriptor/category want use), KEGG pathway classes item present ‘res’ file. Examples embedded DRomics package, cautious, generally file must produced user. item may one annotation (.e. one line). items annotated whatever selected DRomics workflow , previously reduce dimension annotation file selecting items present DRomics output least one biological annotation. annotation stands one word, surround quotes, use tab column separator annotation file, import adding sep = \"\\t\" arguments read.table(). Merging previous data frames order obtain -called ‘extendedres’ data frame gathering, item, metrics derived DRomics workflow biological annotation. Arguments .x .y merge() function indicate column name res annot data frames respectively, must used merging.","code":"# code to import the file for this example stored in our package resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package = \"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) # to see the first lines of this data frame head(res) ## id irow adjpvalue model nbpar b c d e f ## 1 NAP_2 2 6.23e-05 exponential 3 0.4598 NA 5.94 -1.648 NA ## 2 NAP_23 21 1.11e-05 linear 2 -0.0595 NA 5.39 NA NA ## 3 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA ## 4 NAP_38 34 1.89e-03 exponential 3 0.6011 NA 6.86 -0.321 NA ## 5 NAP_42 38 4.16e-03 exponential 3 0.6721 NA 6.21 -0.323 NA ## 6 NAP_52 47 3.92e-02 log-Gauss-probit 5 0.4501 7.2 7.29 1.309 -0.144 ## SDres typology trend y0 yrange maxychange xextrem yextrem BMD.zSD ## 1 0.1260 E.dec.convex dec 5.94 0.456 0.456 NA NA 0.528 ## 2 0.0793 L.dec dec 5.39 0.461 0.461 NA NA 1.333 ## 3 0.0520 L.dec dec 7.86 0.350 0.350 NA NA 1.154 ## 4 0.2338 E.dec.convex dec 6.86 0.601 0.601 NA NA 0.158 ## 5 0.2897 E.dec.convex dec 6.21 0.672 0.672 NA NA 0.182 ## 6 0.0709 lGP.U U 7.29 0.191 0.191 1.46 7.1 0.732 ## BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower ## 1 5.82 NA 5.35 0.2001 1.110 Inf ## 2 5.31 NA 4.85 0.8534 1.746 7.611 ## 3 7.81 NA 7.07 0.7519 1.465 Inf ## 4 6.62 NA 6.17 0.0554 0.680 0.561 ## 5 5.92 0.832 5.59 0.0810 0.794 0.329 ## 6 7.22 NA 8.02 0.4247 1.052 Inf ## BMD.xfold.upper nboot.successful ## 1 Inf 957 ## 2 Inf 1000 ## 3 Inf 1000 ## 4 Inf 648 ## 5 Inf 620 ## 6 Inf 872 # code to import the file for this example in our package annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package = \"DRomics\") # annotfilename <- \"yourchosenname.txt\" # for a local file annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) # to see the first lines of this data frame head(annot) ## metab.code path_class ## 1 NAP_2 Lipid metabolism ## 2 NAP_23 Carbohydrate metabolism ## 3 NAP_30 Carbohydrate metabolism ## 4 NAP_30 Biosynthesis of other secondary metabolites ## 5 NAP_30 Membrane transport ## 6 NAP_30 Signal transduction # Merging extendedres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") # to see the first lines of the merged data frame head(extendedres) ## id irow adjpvalue model nbpar b c d e f SDres ## 1 NAP_2 2 6.23e-05 exponential 3 0.4598 NA 5.94 -1.65 NA 0.1260 ## 2 NAP_23 21 1.11e-05 linear 2 -0.0595 NA 5.39 NA NA 0.0793 ## 3 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## 4 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## 5 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## 6 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## typology trend y0 yrange maxychange xextrem yextrem BMD.zSD BMR.zSD ## 1 E.dec.convex dec 5.94 0.456 0.456 NA NA 0.528 5.82 ## 2 L.dec dec 5.39 0.461 0.461 NA NA 1.333 5.31 ## 3 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## 4 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## 5 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## 6 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## BMD.xfold BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower ## 1 NA 5.35 0.200 1.11 Inf ## 2 NA 4.85 0.853 1.75 7.61 ## 3 NA 7.07 0.752 1.46 Inf ## 4 NA 7.07 0.752 1.46 Inf ## 5 NA 7.07 0.752 1.46 Inf ## 6 NA 7.07 0.752 1.46 Inf ## BMD.xfold.upper nboot.successful path_class ## 1 Inf 957 Lipid metabolism ## 2 Inf 1000 Carbohydrate metabolism ## 3 Inf 1000 Carbohydrate metabolism ## 4 Inf 1000 Biosynthesis of other secondary metabolites ## 5 Inf 1000 Membrane transport ## 6 Inf 1000 Signal transduction"},{"path":[]},{"path":"/articles/DRomics_vignette.html","id":"bmdplot","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Interpretation of DRomics results in a simple case with only one data set obtained in one experimental condition > Various plots of results by biological group","what":"BMD ECDF plots split by group defined from biological annotation","title":"Overview of the DRomics package","text":"Using function bmdplot() argument facetby, BMD ECDF plot can split group (KEGG pathway class). Confidence intervals can added plot color coding trend example (See ?bmdplot options). function ecdfplotwithCI() can also used alternative provide plot differing coloring intervals . (See ?ecdfplotwithCI options.) Using function bmdplotwithgradient() argument facetby, BMD plot color gradient can split KEGG pathway class. (See ?bmdplotwithgradient options). One can focus group interest, instance group “Lipid metabolism”, add labels items using argument add.label display item identifiers instead points. case can useful control limits color gradient limits x-axis order use x-scale signal-scale global previous plot, following example (see ?bmdplotwithgradient details).","code":"bmdplot(extendedres, BMDtype = \"zSD\", add.CI = TRUE, facetby = \"path_class\", colorby = \"trend\") + theme_bw() ecdfplotwithCI(variable = extendedres$BMD.zSD, CI.lower = extendedres$BMD.zSD.lower, CI.upper = extendedres$BMD.zSD.upper, by = extendedres$path_class, CI.col = extendedres$trend) + labs(col = \"trend\") bmdplotwithgradient(extendedres, BMDtype = \"zSD\", scaling = TRUE, facetby = \"path_class\", shapeby = \"trend\") extendedres_lipid <- extendedres[extendedres$path_class == \"Lipid metabolism\",] bmdplotwithgradient(extendedres_lipid, BMDtype = \"zSD\", scaling = TRUE, facetby = \"path_class\", add.label = TRUE, xmin = 0, xmax = 6, label.size = 3, line.size = 2)"},{"path":"/articles/DRomics_vignette.html","id":"sensitivityplot","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Interpretation of DRomics results in a simple case with only one data set obtained in one experimental condition > Various plots of results by biological group","what":"Sensitivity plot of biological groups","title":"Overview of the DRomics package","text":"also possible visualize sensitivity biological group using sensitivityplot() function, choosing BMD summary argument BMDsummary fixed \"first.quartile\", \"median\" \"median..IQR\" (medians interquartile range interval). Moreover, function provide information number items involved pathway/category (coding size points). (See ?sensitivityplot options). example, ECDF plot 25th quantiles BMD-zSD calculated pathway class. possible use medians BMD values represent order groups sensitivity plot optionally add interquartile range line plot, : can customize sensitivity plot position pathway class labels next point instead y-axis, using ggplot2 functions.","code":"sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\") + theme_bw() sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"median.and.IQR\") + theme_bw() psens <- sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\") psens + theme_bw() + theme(axis.text.y = element_blank(), axis.ticks.y = element_blank()) + geom_text(aes(label = paste(\" \", psens$data$groupby, \" \")), size = 3, hjust = \"inward\")"},{"path":"/articles/DRomics_vignette.html","id":"trendplot","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Interpretation of DRomics results in a simple case with only one data set obtained in one experimental condition > Various plots of results by biological group","what":"Trend plot per biological group","title":"Overview of the DRomics package","text":"possible represent repartition trends biological group using trendplot() function (see ?trendplot details).","code":"trendplot(extendedres, group = \"path_class\") + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"curvesplot","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Interpretation of DRomics results in a simple case with only one data set obtained in one experimental condition > Various plots of results by biological group","what":"Plot of dose-response curves per biological group","title":"Overview of the DRomics package","text":"function curvesplot() can show dose-response curves obtained different groups (one chosen group). use bmdplotwithgradient(), scaling curves can used default used focus shape , amplitude signal change. use function define dose range want computation dose-response fitted curves, strongly recommend choose range corresponding range tested/observed doses dataset. code plot dose-response curves split biological group (argument facetby) colored trend (argument colorby). also possible add point BMD-BMR values curve (See ?curvesplot options). also possible using function visualize modeled response item one biological group, :","code":"# Plot of all the scaled dose-reponse curves split by path class curvesplot(extendedres, facetby = \"path_class\", scaling = TRUE, npoints = 100, colorby = \"trend\", xmax = 6.5) + theme_bw() # Plot of the unscaled dose-reponses for one chosen group, split by metabolite LMres <- extendedres[extendedres$path_class == \"Lipid metabolism\", ] curvesplot(LMres, facetby = \"id\", npoints = 100, point.size = 1.5, line.size = 1, colorby = \"trend\", scaling = FALSE, xmax = 6.5) + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"multilevels","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs","what":"Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach","title":"Overview of the DRomics package","text":"section illustrates use DRomics functions help interpretation outputs different data sets obtained different experimental levels (different molecular levels, different time points, different experimental settings, …). idea perform augmentation DRomics output data frame obtained experimental level (previously described one level), bind augmented data frames add column coding experimental level use column organize visualisation DRomics output make possible comparison responses experimental levels. used example corresponding multi-omics approach, experimental level corresponding molecular level, transcriptomic (microarray) metabolomic data set issued experiment. example uses metabolomics transcriptomics data Scenedesmus triclosan published Larras et al. 2020. possible perform without R coding within shiny application DRomicsInterpreter-shiny. Report introduction section see launch shiny application.","code":""},{"path":"/articles/DRomics_vignette.html","id":"augmentation-of-the-data-frames-of-dromics-results-with-biological-annotation","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach","what":"Augmentation of the data frames of DRomics results with biological annotation","title":"Overview of the DRomics package","text":"Following steps described one level, example R code import DRomics results microarray data, merge data frame giving biological annotation selected items. previouly created metabolomics data frame (extended results biological annotation) renamed sake homogeneity.","code":"# 1. Import the data frame with DRomics results to be used contigresfilename <- system.file(\"extdata\", \"triclosanSVcontigres.txt\", package = \"DRomics\") contigres <- read.table(contigresfilename, header = TRUE, stringsAsFactors = TRUE) # 2. Import the data frame with biological annotation (or any other descriptor/category # you want to use, here KEGG pathway classes) contigannotfilename <- system.file(\"extdata\", \"triclosanSVcontigannot.txt\", package = \"DRomics\") # contigannotfilename <- \"yourchosenname.txt\" # for a local file contigannot <- read.table(contigannotfilename, header = TRUE, stringsAsFactors = TRUE) # 3. Merging of both previous data frames contigextendedres <- merge(x = contigres, y = contigannot, by.x = \"id\", by.y = \"contig\") # to see the first lines of the data frame head(contigextendedres) ## id irow adjpvalue model nbpar b c d e f SDres ## 1 c00134 2802 2.76e-04 linear 2 -0.21794 NA 10.9 NA NA 0.417 ## 2 c00276 39331 9.40e-07 exponential 3 1.49944 NA 12.4 -2.20 NA 0.287 ## 3 c00281 41217 2.89e-06 exponential 3 1.40817 NA 12.4 -2.41 NA 0.281 ## 4 c00322 52577 1.88e-03 exponential 3 0.00181 NA 16.4 1.15 NA 0.145 ## 5 c00323 52590 1.83e-03 exponential 3 1.48605 NA 15.3 -2.31 NA 0.523 ## 6 c00380 53968 4.16e-04 exponential 3 1.31958 NA 14.5 -2.52 NA 0.395 ## typology trend y0 yrange maxychange xextrem yextrem BMD.zSD BMR.zSD ## 1 L.dec dec 10.9 1.445 1.445 NA NA 1.913 10.4 ## 2 E.dec.convex dec 12.4 1.426 1.426 NA NA 0.467 12.1 ## 3 E.dec.convex dec 12.4 1.319 1.319 NA NA 0.536 12.1 ## 4 E.inc.convex inc 16.4 0.567 0.567 NA NA 5.073 16.6 ## 5 E.dec.convex dec 15.3 1.402 1.402 NA NA 1.004 14.8 ## 6 E.dec.convex dec 14.5 1.225 1.225 NA NA 0.896 14.1 ## BMD.xfold BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower ## 1 4.98 9.77 1.255 2.759 3.94 ## 2 3.88 11.19 0.243 0.825 2.32 ## 3 5.13 11.17 0.282 0.925 2.79 ## 4 NA 18.05 2.650 5.573 Inf ## 5 NA 13.80 0.388 2.355 3.06 ## 6 NA 13.08 0.366 2.090 4.58 ## BMD.xfold.upper nboot.successful path_class ## 1 Inf 500 Energy metabolism ## 2 Inf 497 Nucleotide metabolism ## 3 Inf 495 Nucleotide metabolism ## 4 Inf 332 Translation ## 5 Inf 466 Metabolism of cofactors and vitamins ## 6 Inf 469 Folding, sorting and degradation metabextendedres <- extendedres"},{"path":"/articles/DRomics_vignette.html","id":"binding","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach","what":"Binding of the data frames corresponding the results at each experimental level","title":"Overview of the DRomics package","text":"next step bind augmented data frames results obtained different levels (transcriptomics metabolomics data frames) add variable (named level) coding level (factor two levels, metabolites contigs).","code":"extendedres <- rbind(metabextendedres, contigextendedres) extendedres$explevel <- factor(c(rep(\"metabolites\", nrow(metabextendedres)), rep(\"contigs\", nrow(contigextendedres)))) # to see the first lines of the data frame head(extendedres) ## id irow adjpvalue model nbpar b c d e f SDres ## 1 NAP_2 2 6.23e-05 exponential 3 0.4598 NA 5.94 -1.65 NA 0.1260 ## 2 NAP_23 21 1.11e-05 linear 2 -0.0595 NA 5.39 NA NA 0.0793 ## 3 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## 4 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## 5 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## 6 NAP_30 28 1.03e-05 linear 2 -0.0451 NA 7.86 NA NA 0.0520 ## typology trend y0 yrange maxychange xextrem yextrem BMD.zSD BMR.zSD ## 1 E.dec.convex dec 5.94 0.456 0.456 NA NA 0.528 5.82 ## 2 L.dec dec 5.39 0.461 0.461 NA NA 1.333 5.31 ## 3 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## 4 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## 5 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## 6 L.dec dec 7.86 0.350 0.350 NA NA 1.154 7.81 ## BMD.xfold BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower ## 1 NA 5.35 0.200 1.11 Inf ## 2 NA 4.85 0.853 1.75 7.61 ## 3 NA 7.07 0.752 1.46 Inf ## 4 NA 7.07 0.752 1.46 Inf ## 5 NA 7.07 0.752 1.46 Inf ## 6 NA 7.07 0.752 1.46 Inf ## BMD.xfold.upper nboot.successful path_class ## 1 Inf 957 Lipid metabolism ## 2 Inf 1000 Carbohydrate metabolism ## 3 Inf 1000 Carbohydrate metabolism ## 4 Inf 1000 Biosynthesis of other secondary metabolites ## 5 Inf 1000 Membrane transport ## 6 Inf 1000 Signal transduction ## explevel ## 1 metabolites ## 2 metabolites ## 3 metabolites ## 4 metabolites ## 5 metabolites ## 6 metabolites"},{"path":"/articles/DRomics_vignette.html","id":"comparisonR","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach","what":"Comparison of results obtained at the different experimental levels using basic R functions","title":"Overview of the DRomics package","text":"examples illustrations can made using basic R functions globally compare results obtained different experimental levels, example compute plot frequencies pathways molecular levels . plot proportions, just apply function prop.table() table frequencies t.pathways. ggplot2 grammar used plot ECDF BMD_zSD using different colors different molecular levels, removing redundant lines corresponding items corresponding one pathway.","code":"(t.pathways <- table(extendedres$path_class, extendedres$explevel)) ## ## contigs metabolites ## Amino acid metabolism 54 14 ## Biosynthesis of other secondary metabolites 0 8 ## Carbohydrate metabolism 76 10 ## Energy metabolism 49 5 ## Lipid metabolism 46 12 ## Membrane transport 13 13 ## Metabolism of other amino acids 26 7 ## Signal transduction 13 8 ## Translation 68 7 ## Folding, sorting and degradation 35 0 ## Glycan biosynthesis and metabolism 14 0 ## Metabolism of cofactors and vitamins 57 0 ## Metabolism of terpenoids and polyketides 15 0 ## Nucleotide metabolism 30 0 ## Replication and repair 9 0 ## Transcription 23 0 ## Transport and catabolism 14 0 ## Xenobiotics biodegradation and metabolism 20 0 original.par <- par() par(las = 2, mar = c(4,13,1,1)) barplot(t(t.pathways), beside = TRUE, horiz = TRUE, cex.names = 0.7, legend.text = TRUE, main = \"Frequencies of pathways\") par(original.par) unique.items <- unique(extendedres$id) ggplot(extendedres[match(unique.items, extendedres$id), ], aes(x = BMD.zSD, color = explevel)) + stat_ecdf(geom = \"step\") + ylab(\"ECDF\") + theme_bw()"},{"path":[]},{"path":"/articles/DRomics_vignette.html","id":"ecdf-plot-of-bmd-values-per-group-and-experimental-level-using-dromics-functions","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach > Comparison of results obtained at the different experimental levels using DRomics functions","what":"ECDF plot of BMD values per group and experimental level using DRomics functions","title":"Overview of the DRomics package","text":"Using function bmdplot() ECDF plot BMD-zSD values can colored split experimental level /split group (KEGG pathway class) . (See ?bmdplot options, example add confidence intervals, …, previous section presenting bmdplot()).","code":"# BMD ECDF plot split by molecular level, after removing items redundancy bmdplot(extendedres[match(unique.items, extendedres$id), ], BMDtype = \"zSD\", facetby = \"explevel\", point.alpha = 0.4) + theme_bw() # BMD ECDF plot colored by molecular level and split by path class bmdplot(extendedres, BMDtype = \"zSD\", facetby = \"path_class\", colorby = \"explevel\", point.alpha = 0.4) + labs(col = \"molecular level\") + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"plot-of-the-trend-repartition-per-group-and-experimental-level","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach > Comparison of results obtained at the different experimental levels using DRomics functions","what":"Plot of the trend repartition per group and experimental level","title":"Overview of the DRomics package","text":"Using function trendplot() arguments facetby possible show repartition trends responses biological group experimental levels.","code":"# Preliminary optional alphabetic ordering of path_class groups extendedres$path_class <- factor(extendedres$path_class, levels = sort(levels(extendedres$path_class), decreasing = TRUE)) # Trend plot trendplot(extendedres, group = \"path_class\", facetby = \"explevel\") + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"sensitivity-plot-per-group-and-experimental-level","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach > Comparison of results obtained at the different experimental levels using DRomics functions","what":"Sensitivity plot per group and experimental level","title":"Overview of the DRomics package","text":"Using function sensitivityplot() arguments group colorby, possible show summary BMD values size points coding number items group example, 25th quartiles BMD values represented per KEGG pathway class molecular level. (See ?sensitivityplot options).","code":"sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", colorby = \"explevel\", BMDsummary = \"first.quartile\") + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"selectgroups","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach > Comparison of results obtained at the different experimental levels using DRomics functions","what":"Selection of groups on which to focus using the selectgroups() function","title":"Overview of the DRomics package","text":"number biological groups obtained annotation items high, may useful select groups focus, enhance visibility plots. can done example using results enrichment procedures case enrichment possible (e.g. sequenced organisms). One also use selection criteria based number items biological group (argument nitems, select represented groups, represented nitems) /BMD summary value (argument BMDmax, select sensitive groups, BMDmax). selectgroups() function can used purpose example (see ?selectgroups details). using function may optionally choose keep results experimental levels (comparison purpose) soon criteria met group least one experimental level (example fixing argument keepallexplev TRUE).","code":"selectedres <- selectgroups(extendedres, group = \"path_class\", explev = \"explevel\", BMDmax = 0.75, BMDtype = \"zSD\", BMDsummary = \"first.quartile\", nitems = 3, keepallexplev = TRUE) # BMDplot on this selection bmdplot(selectedres, BMDtype = \"zSD\", add.CI = TRUE, facetby = \"path_class\", facetby2 = \"explevel\", colorby = \"trend\") + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"bmd-ecdf-plot-with-color-gradient-split-by-group-and-experimental-level","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach > Comparison of results obtained at the different experimental levels using DRomics functions","what":"BMD ECDF plot with color gradient split by group and experimental level","title":"Overview of the DRomics package","text":"Using function bmdplotwithgradient() arguments facetby facetby2, BMD plot color gradient can split group experimental level, example manual selection pathway classes present molecular levels.(See ?bmdplotwithgradient options). Especially metabolomic data transcriptomic data imported DRomics scale (log2 transcriptomics log10 metabolomics), use scaling option dose-response curve interesting . option focuses shape responses, skipping amplitude changes control.","code":"# Manual selection of groups on which to focus chosen_path_class <- c(\"Nucleotide metabolism\", \"Membrane transport\", \"Lipid metabolism\", \"Energy metabolism\") selectedres2 <- extendedres[extendedres$path_class %in% chosen_path_class, ] bmdplotwithgradient(selectedres2, BMDtype = \"zSD\", scaling = TRUE, facetby = \"path_class\", facetby2 = \"explevel\")"},{"path":"/articles/DRomics_vignette.html","id":"plot-of-the-dose-response-curves-for-a-selection-of-groups","dir":"Articles","previous_headings":"Help for biological interpretation of DRomics outputs > Comparison of DRomics results obtained at different experimental levels, for example in a multi-omics approach > Comparison of results obtained at the different experimental levels using DRomics functions","what":"Plot of the dose-response curves for a selection of groups","title":"Overview of the DRomics package","text":"Using function curvesplot(), specific dose-response curves can explored. following example, results related “lipid metabolism” pathway class explored, using argument facetby split experimental level. second example, plot split biological group using argument facetby experimental level using argument facetby2 . (See ?curvesplot options). scaling curves used second plot can interesting focus shapes curves, skipping amplitude changes control. helps evaluate homogeneity shapes responses within group. may example interesting observe example, transcriptomics responses (contigs) gathering shape (use scaling option - done default) just differing sign (increasing / decreasing, U-shape/bell-shape), clearly appear dose-response curves scaled (first plot).","code":"# Plot of the unscaled dose-response curves for the \"lipid metabolism\" path class # using transparency to get an idea of density of curves with the shame shape LMres <- extendedres[extendedres$path_class == \"Lipid metabolism\", ] curvesplot(LMres, facetby = \"explevel\", free.y.scales = TRUE, npoints = 100, line.alpha = 0.4, line.size = 1, colorby = \"trend\", xmax = 6.5) + labs(col = \"DR trend\") + theme_bw() # Plot of the scaled dose-response curves for previously chosen path classes curvesplot(selectedres2, scaling = TRUE, facetby = \"path_class\", facetby2 = \"explevel\", npoints = 100, line.size = 1, line.alpha = 0.4, colorby = \"trend\", xmax = 6.5) + labs(col = \"DR trend\") + theme_bw()"},{"path":"/articles/DRomics_vignette.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Overview of the DRomics package","text":"Burnham, KP, Anderson DR (2004). Multimodel inference: understanding AIC BIC model selection. Sociological methods & research, 33(2), 261-304. Delignette-Muller ML, Siberchicot , Larras F, Billoir E (2023). DRomics, workflow exploit dose-response omics data ecotoxicology. Peer Community Journal. doi : 10.24072/pcjournal.325. https://peercommunityjournal.org/articles/10.24072/pcjournal.325/ EFSA Scientific Committee, Hardy , Benford D, Halldorsson T, Jeger MJ, Knutsen KH, … & Schlatter JR (2017). Update: use benchmark dose approach risk assessment. EFSA Journal, 15(1), e04658.https://efsa.onlinelibrary.wiley.com/doi/full/10.2903/j.efsa.2017.4658 Hurvich, CM, Tsai, CL (1989). Regression time series model selection small samples. Biometrika, 76(2), 297-307.https://www.stat.berkeley.edu/~binyu/summer08/Hurvich.AICc.pdf Larras F, Billoir E, Baillard V, Siberchicot , Scholz S, Wubet T, Tarkka M, Schmitt-Jansen M Delignette-Muller ML (2018). DRomics : turnkey tool support use dose-response framework omics data ecological risk assessment. Environmental Science & Technology. https://pubs.acs.org/doi/10.1021/acs.est.8b04752. can also find article : https://hal.science/hal-02309919 Larras F, Billoir E, Scholz S, Tarkka M, Wubet T, Delignette-Muller ML, Schmitt-Jansen M (2020). multi-omics concentration-response framework uncovers novel understanding triclosan effects chlorophyte Scenedesmus vacuolatus. Journal Hazardous Materials. https://doi.org/10.1016/j.jhazmat.2020.122727.","code":""},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Marie-Laure Delignette-Muller. Author. Elise Billoir. Author. Floriane Larras. Contributor. Aurelie Siberchicot. Author, maintainer.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Delignette-Muller, Marie Laure; Siberchicot, Aurélie; Larras, Floriane; Billoir, Elise. DRomics, workflow exploit dose-response omics data ecotoxicology. Peer Community Journal, Volume 3 (2023). doi : 10.24072/pcjournal.325. https://peercommunityjournal.org/articles/10.24072/pcjournal.325/ Larras F, Billoir E, Baillard V, Siberchicot , Scholz S, Wubet T, Tarkka M, Schmitt-Jansen M Delignette-Muller ML (2018). DRomics: turnkey tool support use dose-response framework omics data ecological risk assessment. Environmental science & technology. URL https://doi.org/10.1021/acs.est.8b04752.","code":"@Article{, title = {DRomics: a turnkey tool to support the use of the dose-response framework for omics data in ecological risk assessment}, author = {{Marie Laure Delignette-Muller} and {Aurélie Siberchicot} and {Floriane Larras} and {Elise Billoir}}, journal = {Peer Community Journal,}, year = {2023}, doi = {10.24072/pcjournal.325}, url = {https://peercommunityjournal.org/articles/10.24072/pcjournal.325/}, } @Article{, title = {DRomics: a turnkey tool to support the use of the dose-response framework for omics data in ecological risk assessment}, author = {{Floriane Larras} and {Elise Billoir} and {Vincent Baillard} and {Aurélie Siberchicot} and {Stefan Scholz} and {Wubet Tefaye} and {Mika Tarkka} and {Mechthild Schmitt-Jansen} and {Marie Laure Delignette-Muller}}, journal = {Environmental Science & Technology}, year = {2018}, doi = {10.1021/acs.est.8b04752}, url = {https://doi.org/10.1021/acs.est.8b04752}, }"},{"path":"/index.html","id":"dromics-dose-response-for-omics-","dir":"","previous_headings":"","what":"Dose Response for Omics","title":"Dose Response for Omics","text":"LBBE Lyon, one M1 one M2 internship offer 2024-2025. Please note! Since June 2024, repository belonged lbbe-software organization. avoid confusion, strongly recommend updating existing local clones point new repository URL. can using git remote command line: git remote set-url origin git@github.com:lbbe-software/DRomics.git git remote set-url origin https://github.com/lbbe-software/DRomics.git DRomics freely available tool dose-response (concentration-response) characterization omics data. especially dedicated omics data obtained using typical dose-response design, favoring great number tested doses (concentrations) rather great number replicates (need replicates use DRomics). first step consists importing, checking needed normalizing/transforming data (step 1), aim proposed workflow select monotonic /biphasic significantly responsive items (e.g. probes, contigs, metabolites) (step 2), choose best-fit model among predefined family monotonic biphasic models describe response selected item (step 3), derive benchmark dose concentration fitted curve (step 4). steps can performed R using DRomics functions, using shiny application named DRomics-shiny. available version, DRomics supports single-channel microarray data (log2 scale), RNAseq data (raw counts) continuous omics data metabolomics proteomics (log scale). order link responses across biological levels based common method, DRomics also handles continuous apical data long meet use conditions least squares regression (homoscedastic Gaussian regression). built environmental risk assessment context omics data often collected non-sequenced species species communities, DRomics provide annotation pipeline. annotation items selected DRomics may complex context, must done outside DRomics using databases KEGG Gene Ontology. DRomics functions can used help interpretation workflow results view biological annotation. enables multi-omics approach, comparison responses different levels organization (view common biological annotation). can also used compare responses one organization level, measured different experimental conditions (e.g. different time points). interpretation can performed R using DRomics functions, using second shiny application DRomicsInterpreter-shiny. informations DRomics can also found https://lbbe.univ-lyon1.fr/fr/dromics. Keywords : dose response modelling / benchmark dose (BMD) / environmental risk assessment / transcriptomics / proteomics / metabolomics / toxicogenomics / multi-omics","code":""},{"path":"/index.html","id":"the-package","dir":"","previous_headings":"","what":"The package","title":"Dose Response for Omics","text":"limma DESeq2 packages Bioconductor must installed use DRomics (can take long time): stable version DRomics can installed CRAN using: development version DRomics can installed GitHub (remotes needed): Finally load package current R session following R command:","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) { install.packages(\"BiocManager\") } else { BiocManager::install(ask = FALSE, update = TRUE) } BiocManager::install(c(\"limma\", \"DESeq2\")) install.packages(\"DRomics\") if (!requireNamespace(\"remotes\", quietly = TRUE)) install.packages(\"remotes\") remotes::install_github(\"lbbe-software/DRomics\") require(\"DRomics\")"},{"path":"/index.html","id":"vignette-and-cheat-sheet","dir":"","previous_headings":"","what":"Vignette and cheat sheet","title":"Dose Response for Omics","text":"vignette attached DRomics package. vignette intended help users start using DRomics package. complementary reference manual can find details function package. first part vignette (Main workflow, steps 1 4) also help users first shiny application DRomics-shiny. second part (Help biological interpretation DRomics outputs) also help users second shiny application DRomicsInterpreter-shiny. vignette can reached : Note , default, vignette installed package installed GitHub. following command (rather long execute large size vignette) allow access vignette development version package installed GitHub: cheat sheet sum DRomics workflow also available.","code":"vignette(\"DRomics_vignette\") remotes::install_github(\"lbbe-software/DRomics\", build_vignettes = TRUE)"},{"path":"/index.html","id":"two-shiny-apps","dir":"","previous_headings":"","what":"Two shiny apps","title":"Dose Response for Omics","text":"two shiny apps (DRomics-shiny DRomicsInterpreter-shiny) work DRomics available : https://lbbe-shiny.univ-lyon1.fr/DRomics/inst/DRomics-shiny/ https://lbbe-shiny.univ-lyon1.fr/DRomics/inst/DRomicsInterpreter-shiny/ https://biosphere.france-bioinformatique.fr/catalogue/appliance/176/ DRomics-shiny https://biosphere.france-bioinformatique.fr/catalogue/appliance/209/ DRomicsInterpreter-shiny install.packages(c(\"shiny\", \"shinyBS\", \"shinycssloaders\", \"shinyjs\", \"shinyWidgets\", \"sortable\")) shiny::runApp(system.file(\"DRomics-shiny\", package = \"DRomics\")) shiny::runApp(system.file(\"DRomicsInterpreter-shiny\", package = \"DRomics\")) shiny apps runing development version DRomics.","code":""},{"path":"/index.html","id":"authors--contacts","dir":"","previous_headings":"","what":"Authors & Contacts","title":"Dose Response for Omics","text":"need yet covered, feedback package / Shiny app, training needs, feel free email us dromics@univ-lyon1.fr . Issues can reported https://github.com/lbbe-software/DRomics/issues . Elise Billoir: elise.billoir@univ-lorraine.fr Marie-Laure Delignette-Muller: marielaure.delignettemuller@vetagro-sup.fr Floriane Larras: floriane.larras@kreatis.eu Mechthild Schmitt-Jansen: mechthild.schmitt@ufz.de Aurélie Siberchicot: aurelie.siberchicot@univ-lyon1.fr","code":""},{"path":"/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Dose Response for Omics","text":"use Dromics, cite: Delignette-Muller ML, Siberchicot , Larras F, Billoir E (2023). DRomics, workflow exploit dose-response omics data ecotoxicology. Peer Community Journal. https://peercommunityjournal.org/articles/10.24072/pcjournal.325/ Larras F, Billoir E, Baillard V, Siberchicot , Scholz S, Wubet T, Tarkka M, Schmitt-Jansen M Delignette-Muller ML (2018). DRomics : turnkey tool support use dose-response framework omics data ecological risk assessment. Environmental Science & Technology. https://pubs.acs.org/doi/10.1021/acs.est.8b04752 can find article : https://hal.science/hal-02309919 can also look following citation complete example use: Larras F, Billoir E, Scholz S, Tarkka M, Wubet T, Delignette-Muller ML, Schmitt-Jansen M (2020). multi-omics concentration-response framework uncovers novel understanding triclosan effects chlorophyte Scenedesmus vacuolatus. Journal Hazardous Materials. https://doi.org/10.1016/j.jhazmat.2020.122727.","code":""},{"path":"/reference/PCAdataplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs and plots the results of a PCA on omic data — PCAdataplot","title":"Performs and plots the results of a PCA on omic data — PCAdataplot","text":"Provides two dimensional plot (two first components) principal component analysis (PCA) performed omic data normalization /transformation, check promiximity samples exposed dose optionally presence/absence potential batch effect.","code":""},{"path":"/reference/PCAdataplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs and plots the results of a PCA on omic data — PCAdataplot","text":"","code":"PCAdataplot(omicdata, batch, label)"},{"path":"/reference/PCAdataplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs and plots the results of a PCA on omic data — PCAdataplot","text":"omicdata object class \"microarraydata\", \"RNAseqdata\" \"continuousomicdata\" respectively returned functions microarraydata, RNAseqdata continuousomicdata. batch Optionnally factor coding potential batch effect (factor length number samples dataset). label FALSE (default choice), TRUE character vector defining sample names. two last cases, points replaced labels samples (batch identified shape points, may appear sample names.","code":""},{"path":"/reference/PCAdataplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs and plots the results of a PCA on omic data — PCAdataplot","text":"ggplot object.","code":""},{"path":"/reference/PCAdataplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Performs and plots the results of a PCA on omic data — PCAdataplot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/PCAdataplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs and plots the results of a PCA on omic data — PCAdataplot","text":"","code":"# (1) on a microarray dataset # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package=\"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. print(o) #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 100 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"6.1\" \"7.1\" \"8.1\" \"9.1\" \"10.1\" #> [11] \"11.1\" \"12.1\" \"13.1\" \"14.1\" \"15\" \"16.1\" \"17.1\" \"18.1\" \"19.1\" \"20.1\" #> Data were normalized between arrays using the following method: cyclicloess plot(o) PCAdataplot(o) PCAdataplot(o, label = TRUE) samplenames <- paste0(\"sample\", 1:ncol(o$data)) PCAdataplot(o, label = samplenames) # \\donttest{ # (2) an example on an RNAseq dataset with a potential batch effect # data(zebraf) str(zebraf) #> List of 3 #> $ counts: int [1:1000, 1:16] 453 331 897 12 326 533 1948 904 583 154 ... #> ..- attr(*, \"dimnames\")=List of 2 #> .. ..$ : chr [1:1000] \"ENSDARG00000102141\" \"ENSDARG00000102123\" \"ENSDARG00000114503\" \"ENSDARG00000115971\" ... #> .. ..$ : chr [1:16] \"I10_05mG_E5\" \"I10_05mG_E6\" \"I10_05mG_E7\" \"I10_C5\" ... #> $ dose : num [1:16] 500 500 500 0 0 0 0 50000 50000 50000 ... #> $ batch : Factor w/ 2 levels \"I10\",\"I17\": 1 1 1 1 1 1 1 2 2 2 ... data4DRomics <- formatdata4DRomics(signalmatrix = zebraf$counts, dose = zebraf$dose) o <- RNAseqdata(data4DRomics, transfo.method = \"vst\") #> converting counts to integer mode #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. PCAdataplot(o, batch = zebraf$batch) PCAdataplot(o, label = TRUE) # }"},{"path":"/reference/RNAseqdata.html","id":null,"dir":"Reference","previous_headings":"","what":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"RNAseq data raw counts (integer values) imported .txt file (internally imported using function read.table), checked R object class data.frame (see description argument file required format data), normalized respect library size tranformed log2 scale using variance stabilizing transformation regularized logarithm.","code":""},{"path":"/reference/RNAseqdata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"","code":"RNAseqdata(file, backgrounddose, check = TRUE, transfo.method, transfo.blind = TRUE, round.counts = FALSE) # S3 method for class 'RNAseqdata' print(x, ...) # S3 method for class 'RNAseqdata' plot(x, range4boxplot = 0, ...)"},{"path":"/reference/RNAseqdata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"file name .txt file (e.g. \"mydata.txt\") containing one row per item, first column corresponding identifier item, columns giving responses item replicate dose concentration. first line, name identifier column, must tested doses concentrations numeric format corresponding replicate (example, triplicates treatment, first line \"item\", 0, 0, 0, 0.1, 0.1, 0.1, etc.). file imported within function using function read.table default field separator (sep argument) default decimal separator (dec argument \".\"). Alternatively R object class data.frame can directly given input, corresponding output read.table(file, header = FALSE) file described . two alternatives illustrated examples. backgrounddose argument must used dose zero data, prevent calculation BMD extrapolation. doses equal value given backgrounddose fixed 0, considered background level exposition. check TRUE format input file checked. transfo.method method chosen transform raw counts log2 scale using DESeq2: \"rlog\" regularized logarithm \"vst\" variance stabilizing transformation. missing, default value defined \"rlog\" datasets less 30 samples \"vst\" transfo.blind Argument given function rlog vst, see rlog vst explaination, default TRUE DESeq2 package . round.counts Put TRUE counts come Kallisto Salmon order round treatment DESeq2. x object class \"RNAseqdata\". range4boxplot argument passed boxplot(), fixed default 0 prevent producing large plot files due many outliers. Can put 1.5 obtain classical boxplots. ... arguments passed print plot functions.","code":""},{"path":"/reference/RNAseqdata.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"function imports data, checks format (see description argument file required format data) gives print information help user check coding data correct : tested doses (concentrations) number replicates dose, number items, identifiers first 20 items. Data normalized respect library size tranformed using functions rlog vst DESeq2 package depending specified method : \"rlog\" (recommended default choice) \"vst\".","code":""},{"path":"/reference/RNAseqdata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"RNAseqdata returns object class \"RNAseqdata\", list 9 components: data numeric matrix normalized transformed responses item replicate (one line per item, one column per replicate) dose numeric vector tested doses concentrations corresponding column data item character vector identifiers items, corresponding line data design table experimental design (tested doses number replicates dose) control user data.mean numeric matrix mean responses item per dose (mean corresponding replicates) (one line per item, one column per unique value dose) data.sd numeric matrix standard deviations response item per dose (sd corresponding replicates, NA replicate) (one line per item, one column per unique value dose) transfo.method transformation method specified input raw.counts numeric matrix non transformed responses (raw counts) item replicate (one line per item, one column per replicate) normalization containsNA always FALSE RNAseq data allowed contain NA values print RNAseqdata object gives tested doses (concentrations) number replicates dose, number items, identifiers first 20 items (check good coding data) tranformation method. plot RNAseqdata object shows data distribution dose concentration replicate normalization tranformation.","code":""},{"path":[]},{"path":"/reference/RNAseqdata.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"Love MI, Huber W, Anders S (2014), Moderated estimation fold change dispersion RNA-seq data DESeq2. Genome biology, 15(12), 550.","code":""},{"path":"/reference/RNAseqdata.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/RNAseqdata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import, check and normalization and transformation of RNAseq data — RNAseqdata","text":"","code":"# (1) import, check, normalization and transformation of RNAseq data # An example on a subsample of a data set published by Zhou et al. 2017 # Effect on mouse kidney transcriptomes of tetrachloroethylene # (see ? Zhou for details) # datafilename <- system.file(\"extdata\", \"RNAseq_sample.txt\", package=\"DRomics\") (o <- RNAseqdata(datafilename, check = TRUE, transfo.method = \"vst\")) #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 999 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: vst plot(o) # If you want to use your own data set just replace datafilename, # the first argument of RNAseqdata(), # by the name of your data file (e.g. \"mydata.txt\") # # You should take care that the field separator of this data file is one # of the default field separators recognised by the read.table() function # when it is used with its default field separator (sep argument) # Tabs are recommended. # Use of an R object of class data.frame # below the same example taking a subsample of the data set # Zhou_kidney_pce (see ?Zhou for details) data(Zhou_kidney_pce) subsample <- Zhou_kidney_pce[1:1000, ] (o <- RNAseqdata(subsample, check = TRUE, transfo.method = \"vst\")) #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 999 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: vst plot(o) PCAdataplot(o) # (2) transformation with two methods on the whole data set # \\donttest{ data(Zhou_kidney_pce) # variance stabilizing tranformation (o1 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = \"vst\")) #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 33394 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: vst plot(o1) # regularized logarithm (o2 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = \"rlog\")) #> Just wait, the transformation using regularized logarithm (rlog) may #> take a few minutes. #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 33394 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: rlog plot(o2) # variance stabilizing tranformation (blind to the experimental design) (o3 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = \"vst\", transfo.blind = TRUE)) #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 33394 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: vst plot(o3) # regularized logarithm (o4 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = \"rlog\", transfo.blind = TRUE)) #> Just wait, the transformation using regularized logarithm (rlog) may #> take a few minutes. #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 33394 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: rlog plot(o4) # }"},{"path":"/reference/Scenedesmus.html","id":null,"dir":"Reference","previous_headings":"","what":"Concentration-response effect of triclosan in Scenedesmus vacuolatus — Scenedesmus","title":"Concentration-response effect of triclosan in Scenedesmus vacuolatus — Scenedesmus","text":"Metabolomic apical data sets effect triclosan chlorophyte Scenedesmus vacuolatus.","code":""},{"path":"/reference/Scenedesmus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Concentration-response effect of triclosan in Scenedesmus vacuolatus — Scenedesmus","text":"","code":"data(Scenedesmus_metab) data(Scenedesmus_apical)"},{"path":"/reference/Scenedesmus.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Concentration-response effect of triclosan in Scenedesmus vacuolatus — Scenedesmus","text":"Scenedesmus_metab contains one row per metabolite, first column corresponding identifier metabolite, columns giving log10 tranformed area curve replicate concentration. first line, name identifier column, tested concentrations corresponding replicate. Scenedesmus_apical contains one row per apical endpoint, first column corresponding identifier endpoint, columns giving measured value endpoint replicate concentration. first line, name identifier column, tested concentrations corresponding replicate.","code":""},{"path":"/reference/Scenedesmus.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Concentration-response effect of triclosan in Scenedesmus vacuolatus — Scenedesmus","text":"Larras, F., Billoir, E., Scholz, S., Tarkka, M., Wubet, T., Delignette-Muller, M. L., & Schmitt-Jansen, M. (2020). multi-omics concentration-response framework uncovers novel understanding triclosan effects chlorophyte Scenedesmus vacuolatus. Journal Hazardous Materials, 122727.","code":""},{"path":"/reference/Scenedesmus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Concentration-response effect of triclosan in Scenedesmus vacuolatus — Scenedesmus","text":"","code":"# (1.1) load of metabolomics data # data(Scenedesmus_metab) head(Scenedesmus_metab) #> V1 V2 V3 V4 V5 V6 V7 V8 #> 1 metab.code 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 7.760000 #> 2 NAP_1 4.338845 4.727077 4.664407 4.741994 4.338845 4.667462 4.338845 #> 3 NAP_2 5.923194 5.997305 5.897229 6.092802 5.966068 5.733371 5.548711 #> 4 NAP_3 4.780252 4.890248 5.103817 5.060089 5.037458 4.829921 4.948354 #> 5 NAP_4 4.027370 4.457973 4.027370 4.027370 4.350887 4.027370 4.027370 #> 6 NAP_5 5.269317 4.660272 5.407287 5.282763 4.660272 4.660272 5.306268 #> V9 V10 V11 V12 V13 V14 V15 V16 #> 1 4.780000 2.920000 1.790000 1.100000 0.690000 7.760000 7.760000 4.780000 #> 2 4.639875 4.684765 4.338845 4.338845 4.855040 4.338845 4.927042 4.338845 #> 3 5.478389 5.708228 5.585534 5.832640 5.853180 5.425401 5.590360 5.478412 #> 4 4.863668 4.923078 4.922019 4.870656 5.071359 4.869461 5.115907 5.135603 #> 5 4.027370 4.027370 4.027370 4.027370 4.027370 4.027370 4.027370 4.027370 #> 6 4.660272 5.342616 5.295892 4.660272 5.319847 5.104808 4.660272 5.219089 #> V17 V18 V19 V20 V21 V22 V23 V24 #> 1 4.780000 2.920000 2.920000 1.790000 1.790000 1.100000 1.100000 0.690000 #> 2 4.338845 4.338845 4.733983 4.338845 4.338845 4.338845 5.078072 4.338845 #> 3 5.460895 5.485156 5.448148 5.582259 5.700495 5.976869 5.435696 5.875684 #> 4 5.002352 5.325395 4.479222 4.979134 5.164020 5.067967 5.279681 5.166167 #> 5 4.027370 4.027370 4.027370 4.521230 4.328400 4.422097 4.689859 4.492537 #> 6 4.660272 4.660272 4.961302 4.660272 4.660272 4.660272 5.455795 5.462184 #> V25 #> 1 0.690000 #> 2 4.703429 #> 3 5.656397 #> 4 5.018734 #> 5 4.027370 #> 6 4.660272 str(Scenedesmus_metab) #> 'data.frame':\t225 obs. of 25 variables: #> $ V1 : chr \"metab.code\" \"NAP_1\" \"NAP_2\" \"NAP_3\" ... #> $ V2 : num 0 4.34 5.92 4.78 4.03 ... #> $ V3 : num 0 4.73 6 4.89 4.46 ... #> $ V4 : num 0 4.66 5.9 5.1 4.03 ... #> $ V5 : num 0 4.74 6.09 5.06 4.03 ... #> $ V6 : num 0 4.34 5.97 5.04 4.35 ... #> $ V7 : num 0 4.67 5.73 4.83 4.03 ... #> $ V8 : num 7.76 4.34 5.55 4.95 4.03 ... #> $ V9 : num 4.78 4.64 5.48 4.86 4.03 ... #> $ V10: num 2.92 4.68 5.71 4.92 4.03 ... #> $ V11: num 1.79 4.34 5.59 4.92 4.03 ... #> $ V12: num 1.1 4.34 5.83 4.87 4.03 ... #> $ V13: num 0.69 4.86 5.85 5.07 4.03 ... #> $ V14: num 7.76 4.34 5.43 4.87 4.03 ... #> $ V15: num 7.76 4.93 5.59 5.12 4.03 ... #> $ V16: num 4.78 4.34 5.48 5.14 4.03 ... #> $ V17: num 4.78 4.34 5.46 5 4.03 ... #> $ V18: num 2.92 4.34 5.49 5.33 4.03 ... #> $ V19: num 2.92 4.73 5.45 4.48 4.03 ... #> $ V20: num 1.79 4.34 5.58 4.98 4.52 ... #> $ V21: num 1.79 4.34 5.7 5.16 4.33 ... #> $ V22: num 1.1 4.34 5.98 5.07 4.42 ... #> $ V23: num 1.1 5.08 5.44 5.28 4.69 ... #> $ V24: num 0.69 4.34 5.88 5.17 4.49 ... #> $ V25: num 0.69 4.7 5.66 5.02 4.03 ... # \\donttest{ # (1.2) import and check of metabolomics data # (o_metab <- continuousomicdata(Scenedesmus_metab)) #> Warning: #> We recommend you to check that your omic data were correctly pretreated #> before importation. In particular data (e.g. metabolomic signal) should #> have been log-transformed, without replacing 0 values by NA values #> (consider using the half minimum method instead for example). #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.1 1.79 2.92 4.78 7.76 #> 6 3 3 3 3 3 3 #> Number of items: 224 #> Identifiers of the first 20 items: #> #> [1] \"NAP_1\" \"NAP_2\" \"NAP_3\" \"NAP_4\" \"NAP_5\" \"NAP_6\" \"NAP_7\" \"NAP_8\" #> [9] \"NAP_9\" \"NAP_11\" \"NAP_13\" \"NAP_14\" \"NAP_15\" \"NAP_16\" \"NAP_17\" \"NAP_18\" #> [17] \"NAP_19\" \"NAP_20\" \"NAP_21\" \"NAP_22\" plot(o_metab) # (2.1) load of apical data # data(Scenedesmus_apical) head(Scenedesmus_apical) #> V1 V2 V3 V4 V5 V6 V7 V8 #> 1 endpoint 0.10000 0.10000 0.10000 0.10000 0.10000 0.10000 0.1000 #> 2 growth 4.05405 -3.86402 -0.40118 0.21115 4.78474 -0.28645 -1.9627 #> 3 photosynthesis 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.0000 #> V9 V10 V11 V12 V13 V14 V15 V16 #> 1 0.10000 0.10000 0.10000 0.10000 0.10000 2.40000 2.40000 2.40000 #> 2 -2.53559 8.21172 -4.63598 -1.64234 -1.93339 -7.98142 7.33857 3.34705 #> 3 0.00000 0.00000 0.00000 0.00000 0.00000 -7.18853 -4.50187 -8.42766 #> V17 V18 V19 V20 V21 V22 V23 V24 #> 1 2.40000 2.40000 2.40000 3.80000 3.80000 6.20000 6.20000 10.10000 #> 2 -2.55707 -8.68987 -11.48974 1.41102 -10.93750 -4.08453 -12.98564 2.45072 #> 3 -5.63618 -11.47670 -6.96251 -7.53671 -8.01109 -6.37088 -8.79645 -7.67328 #> V25 V26 V27 V28 V29 V30 V31 V32 #> 1 10.10000 16.50000 16.50000 16.50000 16.50000 16.50000 16.50000 26.80000 #> 2 -3.71622 27.30152 29.05854 17.60842 14.03268 18.95971 16.65211 64.81147 #> 3 -7.28024 -6.78936 -12.60403 -5.77177 -5.86055 -18.67683 -14.58052 -5.76963 #> V33 V34 V35 V36 V37 #> 1 26.8000 43.50000 43.50000 70.70000 70.70000 #> 2 58.3826 77.06083 72.71959 72.94341 81.89225 #> 3 5.8997 22.08560 19.35689 21.28191 35.79860 str(Scenedesmus_apical) #> 'data.frame':\t3 obs. of 37 variables: #> $ V1 : chr \"endpoint\" \"growth\" \"photosynthesis\" #> $ V2 : num 0.1 4.05 0 #> $ V3 : num 0.1 -3.86 0 #> $ V4 : num 0.1 -0.401 0 #> $ V5 : num 0.1 0.211 0 #> $ V6 : num 0.1 4.78 0 #> $ V7 : num 0.1 -0.286 0 #> $ V8 : num 0.1 -1.96 0 #> $ V9 : num 0.1 -2.54 0 #> $ V10: num 0.1 8.21 0 #> $ V11: num 0.1 -4.64 0 #> $ V12: num 0.1 -1.64 0 #> $ V13: num 0.1 -1.93 0 #> $ V14: num 2.4 -7.98 -7.19 #> $ V15: num 2.4 7.34 -4.5 #> $ V16: num 2.4 3.35 -8.43 #> $ V17: num 2.4 -2.56 -5.64 #> $ V18: num 2.4 -8.69 -11.48 #> $ V19: num 2.4 -11.49 -6.96 #> $ V20: num 3.8 1.41 -7.54 #> $ V21: num 3.8 -10.94 -8.01 #> $ V22: num 6.2 -4.08 -6.37 #> $ V23: num 6.2 -13 -8.8 #> $ V24: num 10.1 2.45 -7.67 #> $ V25: num 10.1 -3.72 -7.28 #> $ V26: num 16.5 27.3 -6.79 #> $ V27: num 16.5 29.1 -12.6 #> $ V28: num 16.5 17.61 -5.77 #> $ V29: num 16.5 14.03 -5.86 #> $ V30: num 16.5 19 -18.7 #> $ V31: num 16.5 16.7 -14.6 #> $ V32: num 26.8 64.81 -5.77 #> $ V33: num 26.8 58.4 5.9 #> $ V34: num 43.5 77.1 22.1 #> $ V35: num 43.5 72.7 19.4 #> $ V36: num 70.7 72.9 21.3 #> $ V37: num 70.7 81.9 35.8 # (2.2) import and check of apical data # (o_apical <- continuousanchoringdata(Scenedesmus_apical, backgrounddose = 0.1)) #> Warning: #> We recommend you to check that your anchoring data are continuous and #> defined in a scale that enable the use of a normal error model (needed #> at each step of the workflow including the selection step). #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 2.4 3.8 6.2 10.1 16.5 26.8 43.5 70.7 #> 12 6 2 2 2 6 2 2 2 #> Number of endpoints: 2 #> Names of the endpoints: #> [1] \"growth\" \"photosynthesis\" # It is here necessary to define the background dose as there is no dose at 0 in the data # The BMD cannot be computed without defining the background level plot(o_apical) #> Warning: log-10 transformation introduced infinite values. # (2.3) selection of responsive endpoints on apical data # (s_apical <- itemselect(o_apical, select.method = \"quadratic\", FDR = 0.05)) #> Number of selected items using a quadratic trend test with an FDR of 0.05: 2 #> Identifiers of the responsive items: #> [1] \"growth\" \"photosynthesis\" # (2.4) fit of dose-response models on apical data # (f_apical <- drcfit(s_apical, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |=================================== | 50% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> Distribution of the chosen models among the 2 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 0 0 0 2 #> log-Gauss-probit #> 0 #> Distribution of the trends (curve shapes) among the 2 fitted dose-response curves: #> #> U #> 2 f_apical$fitres #> id irow adjpvalue model nbpar b c #> 1 growth 1 4.120696e-17 Gauss-probit 4 12.20929 77.03040 #> 2 photosynthesis 2 2.669437e-13 Gauss-probit 4 16.87413 28.65168 #> d e f SDres typology trend y0 yatdosemax #> 1 77.03040 5.38810 -84.19789 5.236305 GP.U U 0.644969 77.03035 #> 2 28.65168 12.88431 -40.04502 3.788006 GP.U U -1.267411 28.53860 #> yrange maxychange xextrem yextrem #> 1 84.19784 76.38538 5.38810 -7.167487 #> 2 39.93195 29.80601 12.88431 -11.393343 plot(f_apical) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. plot(f_apical, dose_log_trans = TRUE) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. plot(f_apical, plot.type = \"dose_residuals\") #> Warning: log-10 transformation introduced infinite values. # (2.5) Benchmark dose calculation on apical data # r_apical <- bmdcalc(f_apical, z = 1) r_apical$res #> id irow adjpvalue model nbpar b c #> 1 growth 1 4.120696e-17 Gauss-probit 4 12.20929 77.03040 #> 2 photosynthesis 2 2.669437e-13 Gauss-probit 4 16.87413 28.65168 #> d e f SDres typology trend y0 yatdosemax #> 1 77.03040 5.38810 -84.19789 5.236305 GP.U U 0.644969 77.03035 #> 2 28.65168 12.88431 -40.04502 3.788006 GP.U U -1.267411 28.53860 #> yrange maxychange xextrem yextrem BMD.zSD BMR.zSD BMD.xfold #> 1 84.19784 76.38538 5.38810 -7.167487 2.344346 -4.591336 0.02400000 #> 2 39.93195 29.80601 12.88431 -11.393343 2.978888 -5.055417 0.09375678 #> BMR.xfold #> 1 0.5804721 #> 2 -1.3941523 # }"},{"path":"/reference/Zhou.html","id":null,"dir":"Reference","previous_headings":"","what":"Dose-response kidney transcriptomic effect of Tetrachloroethylene in mouse — Zhou","title":"Dose-response kidney transcriptomic effect of Tetrachloroethylene in mouse — Zhou","text":"RNAseq data set effect Tetrachloroethylene (PCE) mouse kidney. environmental contaminant administered gavage aqueous vehicle male B6C3F1/J mice, within dose-reponse design including five doses plus control.","code":""},{"path":"/reference/Zhou.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dose-response kidney transcriptomic effect of Tetrachloroethylene in mouse — Zhou","text":"","code":"data(Zhou_kidney_pce)"},{"path":"/reference/Zhou.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Dose-response kidney transcriptomic effect of Tetrachloroethylene in mouse — Zhou","text":"Zhou_kidney_pce contains one row per transcript, first column corresponding identifier transcript, columns giving count reads replicate dose. first line, name identifier column, tested doses corresponding replicate.","code":""},{"path":"/reference/Zhou.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Dose-response kidney transcriptomic effect of Tetrachloroethylene in mouse — Zhou","text":"Zhou, Y. H., Cichocki, J. ., Soldatow, V. Y., Scholl, E. H., Gallins, P. J., Jima, D., ... & Rusyn, . 2017. Comparative dose-response analysis liver kidney transcriptomic effects trichloroethylene tetrachloroethylene B6C3F1 mouse. Toxicological sciences, 160(1), 95-110.","code":""},{"path":"/reference/Zhou.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dose-response kidney transcriptomic effect of Tetrachloroethylene in mouse — Zhou","text":"","code":"# (1) load of data # data(Zhou_kidney_pce) head(Zhou_kidney_pce) #> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 #> 1 RefSeq 0 0 0.22 0.22 0.22 0.67 0.67 0.67 2 #> 2 NM_144958 2072 2506 2519.00 2116.00 1999.00 2113.00 2219.00 2322.00 2359 #> 3 NR_102758 0 0 0.00 0.00 0.00 0.00 0.00 0.00 0 #> 4 NM_172405 198 265 250.00 245.00 212.00 206.00 227.00 246.00 265 #> 5 NM_029777 18 29 25.00 19.00 19.00 13.00 22.00 19.00 19 #> 6 NM_001130188 0 0 0.00 0.00 0.00 0.00 0.00 1.00 0 #> V11 V12 V13 V14 V15 #> 1 2 2 6 6 6 #> 2 1932 1705 2110 2311 2140 #> 3 0 0 0 0 0 #> 4 205 175 288 315 242 #> 5 26 16 26 32 33 #> 6 0 0 1 0 1 str(Zhou_kidney_pce) #> 'data.frame':\t33395 obs. of 15 variables: #> $ V1 : chr \"RefSeq\" \"NM_144958\" \"NR_102758\" \"NM_172405\" ... #> $ V2 : int 0 2072 0 198 18 0 0 3 0 61 ... #> $ V3 : int 0 2506 0 265 29 0 0 1 0 65 ... #> $ V4 : num 0.22 2519 0 250 25 ... #> $ V5 : num 0.22 2116 0 245 19 ... #> $ V6 : num 0.22 1999 0 212 19 ... #> $ V7 : num 0.67 2113 0 206 13 ... #> $ V8 : num 0.67 2219 0 227 22 ... #> $ V9 : num 0.67 2322 0 246 19 ... #> $ V10: int 2 2359 0 265 19 0 0 0 0 91 ... #> $ V11: int 2 1932 0 205 26 0 0 0 0 59 ... #> $ V12: int 2 1705 0 175 16 0 0 0 0 47 ... #> $ V13: int 6 2110 0 288 26 1 0 0 0 42 ... #> $ V14: int 6 2311 0 315 32 0 0 0 0 60 ... #> $ V15: int 6 2140 0 242 33 1 0 2 1 58 ... # \\donttest{ # (2) import, check, normalization and transformation of a sample # of one of those datasets # d <- Zhou_kidney_pce[1:501, ] (o <- RNAseqdata(d)) #> Just wait, the transformation using regularized logarithm (rlog) may #> take a few minutes. #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 500 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: rlog plot(o) # (3) analysis of the whole dataset (for kidney and PCE) # (may be long to run) d <- Zhou_kidney_pce (o <- RNAseqdata(d)) #> Just wait, the transformation using regularized logarithm (rlog) may #> take a few minutes. #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.22 0.67 2 6 #> 2 3 3 3 3 #> Number of items: 33394 #> Identifiers of the first 20 items: #> [1] \"NM_144958\" \"NR_102758\" \"NM_172405\" \"NM_029777\" \"NM_001130188\" #> [6] \"NM_207141\" \"NM_001162368\" \"NM_008117\" \"NM_001168290\" \"NM_010910\" #> [11] \"NM_001004147\" \"NM_001146318\" \"NM_145597\" \"NM_001161797\" \"NM_021483\" #> [16] \"NR_002862\" \"NR_033520\" \"NM_134027\" \"NM_010381\" \"NM_019388\" #> Data were normalized with respect to library size and tranformed using #> the following method: rlog plot(o) (s <- itemselect(o, select.method = \"quadratic\", FDR = 0.01)) #> converting counts to integer mode #> the design formula contains one or more numeric variables with integer values, #> specifying a model with increasing fold change for higher values. #> did you mean for this to be a factor? if so, first convert #> this variable to a factor using the factor() function #> the design formula contains one or more numeric variables that have mean or #> standard deviation larger than 5 (an arbitrary threshold to trigger this message). #> Including numeric variables with large mean can induce collinearity with the intercept. #> Users should center and scale numeric variables in the design to improve GLM convergence. #> estimating size factors #> estimating dispersions #> gene-wise dispersion estimates #> mean-dispersion relationship #> final dispersion estimates #> fitting model and testing #> Number of selected items using a quadratic trend test with an FDR of 0.01: 930 #> Identifiers of the first 20 most responsive items: #> [1] \"NM_012055\" \"NM_026929\" \"NM_134188\" \"NM_175093\" \"NM_008638\" #> [6] \"NM_180678\" \"NM_012006\" \"NM_011076\" \"NM_001302163\" \"NM_007918\" #> [11] \"NM_028994\" \"NM_172015\" \"NM_146200\" \"NM_001081318\" \"NM_011704\" #> [16] \"NM_017399\" \"NM_007822\" \"NM_010011\" \"NM_144869\" \"NM_146230\" (f <- drcfit(s, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | 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|======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 25 dose-response curves out of 930 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 905 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 1 769 26 86 #> log-Gauss-probit #> 23 #> Distribution of the trends (curve shapes) among the 905 fitted dose-response curves: #> #> U bell dec inc #> 67 41 363 434 head(f$fitres) #> id irow adjpvalue model nbpar b c d #> 1 NM_012055 22032 6.756994e-42 Hill 4 2.70352170 9.81098 7.546171 #> 2 NM_026929 14409 4.585837e-37 linear 2 0.31249103 NA 5.726443 #> 3 NM_134188 986 5.247894e-36 linear 2 0.23123224 NA 10.418319 #> 4 NM_175093 26225 3.843143e-33 exponential 3 0.23781018 NA 5.356573 #> 5 NM_008638 30943 1.187148e-32 linear 2 0.27254307 NA 7.543362 #> 6 NM_180678 14173 9.154882e-29 linear 2 0.08641914 NA 11.552080 #> e f SDres typology trend y0 yatdosemax yrange #> 1 1.885806 NA 0.21185102 H.inc inc 7.546171 9.716030 2.1698591 #> 2 NA NA 0.25622487 L.inc inc 5.726443 7.601389 1.8749462 #> 3 NA NA 0.17560544 L.inc inc 10.418319 11.805713 1.3873934 #> 4 2.906675 NA 0.16172244 E.inc.convex inc 5.356573 6.992494 1.6359211 #> 5 NA NA 0.23350538 L.inc inc 7.543362 9.178620 1.6352584 #> 6 NA NA 0.04747107 L.inc inc 11.552080 12.070595 0.5185148 #> maxychange xextrem yextrem #> 1 2.1698591 NA NA #> 2 1.8749462 NA NA #> 3 1.3873934 NA NA #> 4 1.6359211 NA NA #> 5 1.6352584 NA NA #> 6 0.5185148 NA NA plot(f) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. plot(f, dose_log_trans = TRUE) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. plot(f, plot.type = \"dose_residuals\") #> Warning: log-10 transformation introduced infinite values. r <- bmdcalc(f, z = 1) plot(r) plot(r, by = \"trend\") head(r$res) #> id irow adjpvalue model nbpar b c d #> 1 NM_012055 22032 6.756994e-42 Hill 4 2.70352170 9.81098 7.546171 #> 2 NM_026929 14409 4.585837e-37 linear 2 0.31249103 NA 5.726443 #> 3 NM_134188 986 5.247894e-36 linear 2 0.23123224 NA 10.418319 #> 4 NM_175093 26225 3.843143e-33 exponential 3 0.23781018 NA 5.356573 #> 5 NM_008638 30943 1.187148e-32 linear 2 0.27254307 NA 7.543362 #> 6 NM_180678 14173 9.154882e-29 linear 2 0.08641914 NA 11.552080 #> e f SDres typology trend y0 yatdosemax yrange #> 1 1.885806 NA 0.21185102 H.inc inc 7.546171 9.716030 2.1698591 #> 2 NA NA 0.25622487 L.inc inc 5.726443 7.601389 1.8749462 #> 3 NA NA 0.17560544 L.inc inc 10.418319 11.805713 1.3873934 #> 4 2.906675 NA 0.16172244 E.inc.convex inc 5.356573 6.992494 1.6359211 #> 5 NA NA 0.23350538 L.inc inc 7.543362 9.178620 1.6352584 #> 6 NA NA 0.04747107 L.inc inc 11.552080 12.070595 0.5185148 #> maxychange xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold #> 1 2.1698591 NA NA 0.8140636 7.758022 1.458975 8.300788 #> 2 1.8749462 NA NA 0.8199431 5.982668 1.832514 6.299087 #> 3 1.3873934 NA NA 0.7594332 10.593925 4.505565 11.460151 #> 4 1.6359211 NA NA 1.5080487 5.518295 3.428164 5.892230 #> 5 1.6352584 NA NA 0.8567651 7.776867 2.767769 8.297698 #> 6 0.5185148 NA NA 0.5493120 11.599551 NA 12.707288 # }"},{"path":"/reference/bmdboot.html","id":null,"dir":"Reference","previous_headings":"","what":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"Computes 95 percent confidence intervals x-fold z-SD benchmark doses bootstrap.","code":""},{"path":"/reference/bmdboot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"","code":"bmdboot(r, items = r$res$id, niter = 1000, conf.level = 0.95, tol = 0.5, progressbar = TRUE, parallel = c(\"no\", \"snow\", \"multicore\"), ncpus) # S3 method for class 'bmdboot' print(x, ...) # S3 method for class 'bmdboot' plot(x, BMDtype = c(\"zSD\", \"xfold\"), remove.infinite = TRUE, by = c(\"none\", \"trend\", \"model\", \"typology\"), CI.col = \"blue\", BMD_log_transfo = TRUE, ...)"},{"path":"/reference/bmdboot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"r object class \"bmdcalc\" returned function bmdcalc. items character vector specifying identifiers items want computation confidence intervals. omitted computation done items. niter number samples drawn bootstrap. conf.level Confidence level intervals. tol tolerance term proportion bootstrap samples fit model successful (proportion tolerance, NA values given limits confidence interval. progressbar TRUE progress bar used follow bootstrap process. parallel type parallel operation used, \"snow\" \"multicore\" (second one available Windows), \"\" parallel operation. ncpus Number processes used parallel operation : typically one fix number available CPUs. x object class \"bmdboot\". BMDtype type BMD plot, \"zSD\" (default choice) \"xfold\". remove.infinite TRUE confidence intervals non finite upper bound plotted. \"none\" plot split indicated factor (\"trend\", \"model\" \"typology\"). CI.col color draw confidence intervals. BMD_log_transfo TRUE, default option, log transformation BMD used plot. ... arguments passed graphical print functions.","code":""},{"path":"/reference/bmdboot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"Non-parametric bootstrapping used, mean centered residuals bootstrapped. item, bootstrapped parameter estimates obtained fitting model resampled data sets. fitting procedure fails converge tol*100% cases, NA values given confidence interval. Otherwise, bootstraped BMD computed bootstrapped parameter estimates using method bmdcalc. Confidence intervals BMD computed using percentiles bootstrapped BMDs. example 95 percent confidence intervals computed using 2.5 97.5 percentiles bootstrapped BMDs. cases bootstrapped BMD estimated reached highest tested dose reachable due model asymptotes, given infinite value Inf, enable computation lower limit BMD confidence interval sufficient number bootstrapped BMD values estimated finite values.","code":""},{"path":"/reference/bmdboot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"bmdboot returns object class \"bmdboot\", list 3 components: res data frame reporting results fit, BMD computation bootstrap specified item sorted ascending order adjusted p-values. different columns correspond identifier item (id), row number item initial data set (irow), adjusted p-value selection step (adjpvalue), name best fit model (model), number fitted parameters (nbpar), values parameters b, c, d, e f, (NA non used parameters), residual standard deviation (SDres), typology curve (typology, (16 class typology described help drcfit function)), rough trend curve (trend) defined four classes (U, bell, increasing decreasing shape), theoretical y value control (y0), theoretical y value maximal dose yatdosemax), theoretical y range x within range tested doses (yrange), maximal absolute y change () control(maxychange) biphasic curves x value extremum reached (xextrem) corresponding y value (yextrem), BMD-zSD value (BMD.zSD) corresponding BMR-zSD value (reached , BMR.zSD) BMD-xfold value (BMD.xfold) corresponding BMR-xfold value (reached , BMR.xfold), BMD.zSD.lower BMD.zSD.upper lower upper bounds confidence intervals BMD-zSD value, BMD.xfold.lower BMD.xfold.upper lower upper bounds confidence intervals BMD-xfold value nboot.successful number successful fits bootstrapped samples item. z Value z given input define BMD-zSD. x Value x given input percentage define BMD-xfold. tol tolerance given input term tolerated proportion failures fit bootstrapped samples. niter number samples drawn bootstrap (given input).","code":""},{"path":[]},{"path":"/reference/bmdboot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"Huet S, Bouvier , Poursat M-, Jolivet E (2003) Statistical tools nonlinear regression: practical guide S-PLUS R examples. Springer, Berlin, Heidelberg, New York.","code":""},{"path":"/reference/bmdboot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/bmdboot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computation of confidence interval on benchmark doses by bootstrap — bmdboot","text":"","code":"# (1) a toy example (a very small subsample of a microarray data set) # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package = \"DRomics\") # to test the package on a small but not very small data set # use the following commented line # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package = \"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.001) #> Removing intercept from test coefficients f <- drcfit(s_quad, progressbar = TRUE) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |======= | 10% | |============== | 20% | |===================== | 30% | |============================ | 40% | |=================================== | 50% | |========================================== | 60% | |================================================= | 70% | |======================================================== | 80% | |=============================================================== | 90% | |======================================================================| 100% r <- bmdcalc(f) set.seed(1234) # to get reproducible results with a so small number of iterations (b <- bmdboot(r, niter = 5)) # with a non reasonable value for niter #> Warning: #> A small number of iterations (less than 1000) may not be sufficient to #> ensure a good quality of bootstrap confidence intervals. #> The bootstrap may be long if the number of items and the number of #> bootstrap iterations is high. #> | | | 0% | |======= | 10% | |============== | 20% | |===================== | 30% | |============================ | 40% | |=================================== | 50% | |========================================== | 60% | |================================================= | 70% | |======================================================== | 80% | |=============================================================== | 90% | |======================================================================| 100% #> Bootstrap confidence interval computation was successful on 10 items among10. #> For 0 BMD.zSD values and 4 BMD.xfold values among 10 at least one bound #> of the 95 percent confidence interval could not be computed due to some #> bootstrapped BMD values not reachable due to model asymptotes or #> reached outside the range of tested doses (bounds coded Inf)). # !!!! TO GET CORRECT RESULTS # !!!! niter SHOULD BE FIXED FAR LARGER , e.g. to 1000 # !!!! but the run will be longer b$res #> id irow adjpvalue model nbpar b c d #> 1 15 15 1.546048e-05 exponential 3 0.071422368 NA 7.740153 #> 2 12.1 12 2.869315e-05 Gauss-probit 5 0.414151082 8.903415 7.564374 #> 3 27.1 27 3.087292e-05 linear 2 -0.108446801 NA 15.608419 #> 4 25.1 25 1.597308e-04 exponential 3 -0.128807120 NA 15.142111 #> 5 4 4 2.302448e-04 Gauss-probit 4 3.079188189 9.851210 9.851210 #> 6 70 70 2.323292e-04 exponential 3 -0.007515088 NA 6.682254 #> 7 7.1 7 2.712029e-04 Gauss-probit 4 2.384260578 9.122630 9.122630 #> 8 88.1 88 4.566344e-04 Gauss-probit 4 2.103260654 11.157946 11.157946 #> 9 92 92 4.566344e-04 Gauss-probit 4 8.381660245 -27.802234 -27.802234 #> 10 81 81 6.448977e-04 exponential 3 -0.025717119 NA 6.713592 #> e f SDres typology trend y0 yatdosemax #> 1 2.276377 NA 0.3183292 E.inc.convex inc 7.740153 8.983706 #> 2 1.131878 0.7204105 0.3802228 GP.bell bell 7.585780 8.903415 #> 3 NA NA 0.1648041 L.dec dec 15.608419 14.889308 #> 4 3.404150 NA 0.2142472 E.dec.concave dec 15.142111 14.367457 #> 5 1.959459 -1.6121603 0.2994936 GP.U U 8.534547 9.341173 #> 6 1.074077 NA 1.1263294 E.dec.concave dec 6.682254 3.082935 #> 7 1.735801 -0.9436037 0.2594717 GP.U U 8.398699 9.007963 #> 8 1.755034 0.7172904 0.2131311 GP.bell bell 11.664352 11.206771 #> 9 2.557094 37.5298696 1.1492863 GP.bell bell 8.021106 5.546334 #> 10 1.357555 NA 1.1925115 E.dec.concave dec 6.713592 3.338828 #> yrange maxychange xextrem yextrem BMD.zSD BMR.zSD BMD.xfold #> 1 1.2435537 1.2435537 NA NA 3.8627796 8.058482 5.6254804 #> 2 1.5579862 1.5579862 1.438980 9.143766 0.5682965 7.966002 0.7867277 #> 3 0.7191107 0.7191107 NA NA 1.5196769 15.443615 NA #> 4 0.7746535 0.7746535 NA NA 3.3346126 14.927864 NA #> 5 1.1021236 0.8066266 1.959459 8.239050 4.9146813 8.834040 NA #> 6 3.5993187 3.5993187 NA NA 5.3880607 5.555924 4.8321611 #> 7 0.8289366 0.6092640 1.735801 8.179027 4.5746396 8.658171 NA #> 8 0.6684651 0.4575814 1.755034 11.875236 4.5680110 11.451221 NA #> 9 4.1813022 2.4747723 2.557094 9.727636 1.1073035 9.170392 0.7057605 #> 10 3.3747644 3.3747644 NA NA 5.2374434 5.521081 4.4795770 #> BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper #> 1 8.514168 2.5419700 5.4286722 5.2096765 6.3162984 #> 2 8.344358 0.3962742 0.6113321 0.7585065 0.8436521 #> 3 14.047577 1.2880859 1.8703357 Inf Inf #> 4 13.627900 1.7779710 4.3860469 Inf Inf #> 5 9.388001 0.8764847 4.6482205 6.2736049 6.5002416 #> 6 6.014028 5.5427871 5.8066178 4.8002579 5.2522007 #> 7 9.238569 0.8306627 5.0191965 Inf Inf #> 8 10.497917 0.8761450 1.3426819 Inf Inf #> 9 8.823216 0.6833626 1.7755504 0.5927639 0.9811746 #> 10 6.042233 4.3970380 5.6776339 3.2853517 5.1984168 #> nboot.successful #> 1 5 #> 2 3 #> 3 5 #> 4 5 #> 5 3 #> 6 3 #> 7 4 #> 8 5 #> 9 3 #> 10 4 plot(b) # plot of BMD.zSD after removing of BMDs with infinite upper bounds # \\donttest{ # same plot in raw scale (without log transformation of BMD values) plot(b, BMD_log_transfo = FALSE) # plot of BMD.zSD without removing of BMDs # with infinite upper bounds plot(b, remove.infinite = FALSE) # } # bootstrap on only a subsample of items # with a greater number of iterations # \\donttest{ chosenitems <- r$res$id[1:5] (b.95 <- bmdboot(r, items = chosenitems, niter = 1000, progressbar = TRUE)) #> The bootstrap may be long if the number of items and the number of #> bootstrap iterations is high. #> | | | 0% | |============== | 20% | |============================ | 40% | |========================================== | 60% | |======================================================== | 80% | |======================================================================| 100% #> Bootstrap confidence interval computation was successful on 5 items among5. #> For 0 BMD.zSD values and 3 BMD.xfold values among 5 at least one bound #> of the 95 percent confidence interval could not be computed due to some #> bootstrapped BMD values not reachable due to model asymptotes or #> reached outside the range of tested doses (bounds coded Inf)). b.95$res #> id irow adjpvalue model nbpar b c d #> 1 15 15 1.546048e-05 exponential 3 0.07142237 NA 7.740153 #> 2 12.1 12 2.869315e-05 Gauss-probit 5 0.41415108 8.903415 7.564374 #> 3 27.1 27 3.087292e-05 linear 2 -0.10844680 NA 15.608419 #> 4 25.1 25 1.597308e-04 exponential 3 -0.12880712 NA 15.142111 #> 5 4 4 2.302448e-04 Gauss-probit 4 3.07918819 9.851210 9.851210 #> e f SDres typology trend y0 yatdosemax #> 1 2.276377 NA 0.3183292 E.inc.convex inc 7.740153 8.983706 #> 2 1.131878 0.7204105 0.3802228 GP.bell bell 7.585780 8.903415 #> 3 NA NA 0.1648041 L.dec dec 15.608419 14.889308 #> 4 3.404150 NA 0.2142472 E.dec.concave dec 15.142111 14.367457 #> 5 1.959459 -1.6121603 0.2994936 GP.U U 8.534547 9.341173 #> yrange maxychange xextrem yextrem BMD.zSD BMR.zSD BMD.xfold #> 1 1.2435537 1.2435537 NA NA 3.8627796 8.058482 5.6254804 #> 2 1.5579862 1.5579862 1.438980 9.143766 0.5682965 7.966002 0.7867277 #> 3 0.7191107 0.7191107 NA NA 1.5196769 15.443615 NA #> 4 0.7746535 0.7746535 NA NA 3.3346126 14.927864 NA #> 5 1.1021236 0.8066266 1.959459 8.239050 4.9146813 8.834040 NA #> BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper #> 1 8.514168 1.9234542 5.3597201 4.288446 6.313835 #> 2 8.344358 0.2467196 0.8196903 0.507739 1.068040 #> 3 14.047577 0.9228184 2.1008927 Inf Inf #> 4 13.627900 1.5729443 5.0433579 Inf Inf #> 5 9.388001 0.5446487 5.1515178 5.792727 Inf #> nboot.successful #> 1 932 #> 2 559 #> 3 1000 #> 4 931 #> 5 745 # Plot of fits with BMD values and confidence intervals # with the default BMD.zSD plot(f, items = chosenitems, BMDoutput = b.95, BMDtype = \"zSD\") #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # with the default BMD.xfold plot(f, items = chosenitems, BMDoutput = b.95, BMDtype = \"xfold\") #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: Removed 3 rows containing missing values or values outside the scale range #> (`geom_vline()`). # same bootstrap but changing the default confidence level (0.95) to 0.90 (b.90 <- bmdboot(r, items = chosenitems, niter = 1000, conf.level = 0.9, progressbar = TRUE)) #> The bootstrap may be long if the number of items and the number of #> bootstrap iterations is high. #> | | | 0% | |============== | 20% | |============================ | 40% | |========================================== | 60% | |======================================================== | 80% | |======================================================================| 100% #> Bootstrap confidence interval computation was successful on 5 items among5. #> For 0 BMD.zSD values and 3 BMD.xfold values among 5 at least one bound #> of the 95 percent confidence interval could not be computed due to some #> bootstrapped BMD values not reachable due to model asymptotes or #> reached outside the range of tested doses (bounds coded Inf)). b.90$res #> id irow adjpvalue model nbpar b c d #> 1 15 15 1.546048e-05 exponential 3 0.07142237 NA 7.740153 #> 2 12.1 12 2.869315e-05 Gauss-probit 5 0.41415108 8.903415 7.564374 #> 3 27.1 27 3.087292e-05 linear 2 -0.10844680 NA 15.608419 #> 4 25.1 25 1.597308e-04 exponential 3 -0.12880712 NA 15.142111 #> 5 4 4 2.302448e-04 Gauss-probit 4 3.07918819 9.851210 9.851210 #> e f SDres typology trend y0 yatdosemax #> 1 2.276377 NA 0.3183292 E.inc.convex inc 7.740153 8.983706 #> 2 1.131878 0.7204105 0.3802228 GP.bell bell 7.585780 8.903415 #> 3 NA NA 0.1648041 L.dec dec 15.608419 14.889308 #> 4 3.404150 NA 0.2142472 E.dec.concave dec 15.142111 14.367457 #> 5 1.959459 -1.6121603 0.2994936 GP.U U 8.534547 9.341173 #> yrange maxychange xextrem yextrem BMD.zSD BMR.zSD BMD.xfold #> 1 1.2435537 1.2435537 NA NA 3.8627796 8.058482 5.6254804 #> 2 1.5579862 1.5579862 1.438980 9.143766 0.5682965 7.966002 0.7867277 #> 3 0.7191107 0.7191107 NA NA 1.5196769 15.443615 NA #> 4 0.7746535 0.7746535 NA NA 3.3346126 14.927864 NA #> 5 1.1021236 0.8066266 1.959459 8.239050 4.9146813 8.834040 NA #> BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper #> 1 8.514168 2.1636242 5.1282797 4.4988439 6.218120 #> 2 8.344358 0.2862136 0.7639272 0.5873191 1.026054 #> 3 14.047577 0.9791835 2.0226321 Inf Inf #> 4 13.627900 1.8299433 4.8873535 Inf Inf #> 5 9.388001 0.6371109 4.9854763 5.9397256 Inf #> nboot.successful #> 1 942 #> 2 580 #> 3 1000 #> 4 949 #> 5 769 # } # (2) an example on a microarray data set (a subsample of a greater data set) # # \\donttest{ datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 1000 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" #> [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" #> Data were normalized between arrays using the following method: cyclicloess (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.001)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.001: 78 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |= | 1% | |== | 3% | |=== | 4% | |==== | 5% | |==== | 6% | |===== | 8% | |====== | 9% | |======= | 10% | |======== | 12% | |========= | 13% | |========== | 14% | |=========== | 15% | |============ | 17% | |============= | 18% | |============= | 19% | |============== | 21% | |=============== | 22% | |================ | 23% | |================= | 24% | |================== | 26% | |=================== | 27% | |==================== | 28% | |===================== | 29% | |====================== | 31% | |====================== | 32% | |======================= | 33% | |======================== | 35% | |========================= | 36% | |========================== | 37% | |=========================== | 38% | |============================ | 40% | |============================= | 41% | |============================== | 42% | |=============================== | 44% | 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|=============================================================== | 90% | |================================================================ | 91% | |================================================================= | 92% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |===================================================================== | 99% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 11 dose-response curves out of 78 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 67 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 0 11 30 23 #> log-Gauss-probit #> 3 #> Distribution of the trends (curve shapes) among the 67 fitted dose-response curves: #> #> U bell dec inc #> 6 20 22 19 (r <- bmdcalc(f)) #> 1 BMD-xfold values and 0 BMD-zSD values are not defined (coded NaN as #> the BMR stands outside the range of response values defined by the model). #> 28 BMD-xfold values and 0 BMD-zSD values could not be calculated (coded #> NA as the BMR stands within the range of response values defined by the #> model but outside the range of tested doses). (b <- bmdboot(r, niter = 100)) # niter to put at 1000 for a better precision #> Warning: #> A small number of iterations (less than 1000) may not be sufficient to #> ensure a good quality of bootstrap confidence intervals. #> The bootstrap may be long if the number of items and the number of #> bootstrap iterations is high. #> | | | 0% | |= | 1% | |== | 3% | |=== | 4% | |==== | 6% | |===== | 7% | |====== | 9% | |======= | 10% | |======== | 12% | |========= | 13% | |========== | 15% | |=========== | 16% | |============= | 18% | |============== | 19% | |=============== | 21% | |================ | 22% | |================= | 24% | |================== | 25% | |=================== | 27% | |==================== | 28% | |===================== | 30% | |====================== | 31% | |======================= | 33% | |======================== | 34% | |========================= | 36% | |========================== | 37% | |=========================== | 39% | |============================ | 40% | |============================= | 42% | |============================== | 43% | |=============================== | 45% | |================================ | 46% | |================================= | 48% | |================================== | 49% | |==================================== | 51% | |===================================== | 52% | |====================================== | 54% | |======================================= | 55% | |======================================== | 57% | |========================================= | 58% | |=========================================== | 61% | |============================================ | 63% | |============================================= | 64% | |============================================== | 66% | |=============================================== | 67% | |================================================ | 69% | |================================================= | 70% | |================================================== | 72% | |=================================================== | 73% | |==================================================== | 75% | |===================================================== | 76% | |====================================================== | 78% | |======================================================== | 81% | |========================================================= | 82% | |============================================================ | 85% | |============================================================= | 87% | |============================================================== | 88% | |=============================================================== | 90% | |================================================================ | 91% | |================================================================= | 93% | |================================================================== | 94% | |==================================================================== | 97% | |===================================================================== | 99% | |======================================================================| 100% #> Bootstrap confidence interval computation failed on 4 items among 67 #> due to lack of convergence of the model fit for a fraction of the #> bootstrapped samples greater than 0.5. #> For 0 BMD.zSD values and 36 BMD.xfold values among 67 at least one #> bound of the 95 percent confidence interval could not be computed due #> to some bootstrapped BMD values not reachable due to model asymptotes #> or reached outside the range of tested doses (bounds coded Inf)). # different plots of BMD-zSD plot(b) #> Warning: #> 4 BMD values for which lower and upper bounds were coded NA or with #> lower or upper infinite bounds were removed before plotting. plot(b, by = \"trend\") #> Warning: #> 4 BMD values for which lower and upper bounds were coded NA or with #> lower or upper infinite bounds were removed before plotting. plot(b, by = \"model\") #> Warning: #> 4 BMD values for which lower and upper bounds were coded NA or with #> lower or upper infinite bounds were removed before plotting. plot(b, by = \"typology\") #> Warning: #> 4 BMD values for which lower and upper bounds were coded NA or with #> lower or upper infinite bounds were removed before plotting. # a plot of BMD-xfold (by default BMD-zSD is plotted) plot(b, BMDtype = \"xfold\") #> Warning: #> 40 BMD values for which lower and upper bounds were coded NA or with #> lower or upper infinite bounds were removed before plotting. # } # (3) Comparison of parallel and non parallel implementations # # \\donttest{ # to be tested with a greater number of iterations if(!requireNamespace(\"parallel\", quietly = TRUE)) { if(parallel::detectCores() > 1) { system.time(b1 <- bmdboot(r, niter = 100, progressbar = TRUE)) system.time(b2 <- bmdboot(r, niter = 100, progressbar = FALSE, parallel = \"snow\", ncpus = 2)) }} # }"},{"path":"/reference/bmdcalc.html","id":null,"dir":"Reference","previous_headings":"","what":"Computation of benchmark doses for responsive items — bmdcalc","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"Computes x-fold z-SD benchmark doses responsive item using best fit dose-reponse model.","code":""},{"path":"/reference/bmdcalc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"","code":"bmdcalc(f, z = 1, x = 10, minBMD, ratio2switchinlog = 100) # S3 method for class 'bmdcalc' print(x, ...) # S3 method for class 'bmdcalc' plot(x, BMDtype = c(\"zSD\", \"xfold\"), plottype = c(\"ecdf\", \"hist\", \"density\"), by = c(\"none\", \"trend\", \"model\", \"typology\"), hist.bins = 30, BMD_log_transfo = TRUE, ...)"},{"path":"/reference/bmdcalc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"f object class \"drcfit\" returned function drcfit. z Value z defining BMD-zSD dose response reaching y0 +/- z * SD, y0 level control given dose-response fitted model SD residual standard deviation dose-response fitted model. x Value x given percentage defining BMD-xfold dose response reaching y0 +/- (x/100) * y0, y0 level control given dose-response fitted model. print plot functions, object class \"bmdcalc\". minBMD minimal value calculated BMDs, value considered negligible compared tested doses. given user argument fixed minimal non null tested dose divided 100. ratio2switchinlog ratio maximal minimal tested doses numerical computation (use uniroot necessary) BMD performed log scale dose. BMDtype type BMD plot, \"zSD\" (default choice) \"xfold\". plottype type plot, \"ecdf\" empirical cumulative distribution plot (default choice), \"hist\" histogram \"density\" density plot. different \"none\" plot split trend (\"trend\"), model (\"model\") typology (\"typology\"). hist.bins number bins, used histogram(s). BMD_log_transfo TRUE, default option, log transformation BMD used plot. ... arguments passed graphical print functions.","code":""},{"path":"/reference/bmdcalc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"two types benchmark doses (BMD) proposed EFSA (2017) computed responsive item using best fit dose-reponse model previously obtained using drcfit function (see Larras et al. 2018 details): BMD-zSD defined dose response reaching y0 +/- z * SD, y0 level control given dose-response model, SD residual standard deviation dose response model fit z given input (z fixed 1 default), BMD-xfold defined dose response reaching y0 +/- (x/100) * y0, y0 level control given dose-response fitted model x percentage given input (x fixed 10 default.) analytical solution BMD, numerically searched along fitted curve using uniroot function. cases BMD reached due asymptote high doses, NaN returned. cases BMD reached highest tested dose, NA returned. low BMD values obtained extrapolation 0 smallest non null tested dose, correspond sensitive items (want exclude), thresholded minBMD, argument default fixed smallest non null tested dose divided 100, can fixed user considers negligible dose.","code":""},{"path":"/reference/bmdcalc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"bmdcalc returns object class \"bmdcalc\", list 4 components: res data frame reporting results fit BMD computation selected item sorted ascending order adjusted p-values returned function itemselect. different columns correspond identifier item (id), row number item initial data set (irow), adjusted p-value selection step (adjpvalue), name best fit model (model), number fitted parameters (nbpar), values parameters b, c, d, e f, (NA non used parameters), residual standard deviation (SDres), typology curve (typology, (16 class typology described help drcfit function)), rough trend curve (trend) defined four classes (U, bell, increasing decreasing shape), theoretical y value control (y0), theoretical y value maximal dose yatdosemax), theoretical y range x within range tested doses (yrange), maximal absolute y change () control(maxychange) biphasic curves x value extremum reached (xextrem) corresponding y value (yextrem), BMD-zSD value (BMD.zSD) corresponding BMR-zSD value (reached , BMR.zSD) BMD-xfold value (BMD.xfold) corresponding BMR-xfold value (reached , BMR.xfold). z Value z given input define BMD-zSD. x Value x given input percentage define BMD-xfold. minBMD minimal value calculated BMDs given input fixed minimal non null tested dose divided 100. ratio2switchinlog ratio maximal minimal tested doses numerical computations performed log scale (given input). omicdata corresponding object given input (component itemselect).","code":""},{"path":[]},{"path":"/reference/bmdcalc.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"EFSA Scientific Committee, Hardy , Benford D, Halldorsson T, Jeger MJ, Knutsen KH, ... & Schlatter JR (2017). Update: use benchmark dose approach risk assessment. EFSA Journal, 15(1), e04658. Larras F, Billoir E, Baillard V, Siberchicot , Scholz S, Wubet T, Tarkka M, Schmitt-Jansen M Delignette-Muller ML (2018). DRomics: turnkey tool support use dose-response framework omics data ecological risk assessment. Environmental science & technology.doi:10.1021/acs.est.8b04752","code":""},{"path":"/reference/bmdcalc.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"Marie-Laure Delignette-Muller Elise Billoir","code":""},{"path":"/reference/bmdcalc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computation of benchmark doses for responsive items — bmdcalc","text":"","code":"# (1) a toy example (a very small subsample of a microarray data set) # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package=\"DRomics\") # to test the package on a small (for a quick calculation) but not very small data set # use the following commented line # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 100 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"6.1\" \"7.1\" \"8.1\" \"9.1\" \"10.1\" #> [11] \"11.1\" \"12.1\" \"13.1\" \"14.1\" \"15\" \"16.1\" \"17.1\" \"18.1\" \"19.1\" \"20.1\" #> Data were normalized between arrays using the following method: cyclicloess (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.01)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.01: 17 #> Identifiers of the responsive items: #> [1] \"15\" \"12.1\" \"27.1\" \"25.1\" \"4\" \"70\" \"7.1\" \"88.1\" \"92\" \"81\" #> [11] \"13.1\" \"74.1\" \"83.1\" \"84.1\" \"54.1\" \"85.1\" \"67.1\" (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |==== | 6% | |======== | 12% | |============ | 18% | |================ | 24% | |===================== | 29% | |========================= | 35% | |============================= | 41% | |================================= | 47% | |===================================== | 53% | |========================================= | 59% | |============================================= | 65% | |================================================= | 71% | |====================================================== | 76% | |========================================================== | 82% | |============================================================== | 88% | |================================================================== | 94% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> Distribution of the chosen models among the 17 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 0 4 6 6 #> log-Gauss-probit #> 1 #> Distribution of the trends (curve shapes) among the 17 fitted dose-response curves: #> #> U bell dec inc #> 4 3 6 4 (r <- bmdcalc(f)) #> 9 BMD-xfold values and 0 BMD-zSD values could not be calculated (coded #> NA as the BMR stands within the range of response values defined by the #> model but outside the range of tested doses). plot(r) # \\donttest{ # same plot in raw scale of BMD (without log transformation of BMD values) plot(r, BMD_log_transfo = FALSE) # changing the values of z and x for BMD calculation (rb <- bmdcalc(f, z = 2, x = 50)) #> 2 BMD-xfold values and 0 BMD-zSD values are not defined (coded NaN as #> the BMR stands outside the range of response values defined by the model). #> 13 BMD-xfold values and 2 BMD-zSD values could not be calculated (coded #> NA as the BMR stands within the range of response values defined by the #> model but outside the range of tested doses). plot(rb) #> Warning: #> 2 BMD coded NA or NaN were removed before plotting. # } # Plot of fits with BMD values # \\donttest{ # example with the BMD-1SD plot(f, BMDoutput = r, BMDtype = \"zSD\") #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # example with the BMD-2SD plot(f, BMDoutput = rb, BMDtype = \"zSD\") #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: Removed 2 rows containing missing values or values outside the scale range #> (`geom_vline()`). # example with the BMD-xfold with x = 10 percent plot(f, BMDoutput = r, BMDtype = \"xfold\") #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: Removed 9 rows containing missing values or values outside the scale range #> (`geom_vline()`). # } # (2) an example on a microarray data set (a subsample of a greater data set) # # \\donttest{ datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") # to test the package on a small (for a quick calculation) but not very small data set # use the following commented line # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 1000 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" #> [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" #> Data were normalized between arrays using the following method: cyclicloess (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.01)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.01: 183 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | 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93% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 30 dose-response curves out of 183 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 153 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 1 44 44 59 #> log-Gauss-probit #> 5 #> Distribution of the trends (curve shapes) among the 153 fitted dose-response curves: #> #> U bell dec inc #> 29 35 40 49 (r <- bmdcalc(f)) #> 2 BMD-xfold values and 0 BMD-zSD values are not defined (coded NaN as #> the BMR stands outside the range of response values defined by the model). #> 82 BMD-xfold values and 0 BMD-zSD values could not be calculated (coded #> NA as the BMR stands within the range of response values defined by the #> model but outside the range of tested doses). plot(r) # different plots of BMD-zSD plot(r, plottype = \"hist\") plot(r, plottype = \"density\") plot(r, plottype = \"density\", by = \"trend\") plot(r, plottype = \"ecdf\", by = \"trend\") plot(r, plottype = \"ecdf\", by = \"model\") plot(r, plottype = \"ecdf\", by = \"typology\") # a plot of BMD-xfold (by default BMD-zSD is plotted) plot(r, BMDtype = \"xfold\", plottype = \"hist\", by = \"typology\", hist.bins = 10) #> Warning: #> 84 BMD coded NA or NaN were removed before plotting. # }"},{"path":"/reference/bmdfilter.html","id":null,"dir":"Reference","previous_headings":"","what":"Filtering BMDs according to estimation quality — bmdfilter","title":"Filtering BMDs according to estimation quality — bmdfilter","text":"Filtering BMDs DRomics workflow output according estimation quality, retain best estimated BMDs subsequent biological annotation interpretation.","code":""},{"path":"/reference/bmdfilter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filtering BMDs according to estimation quality — bmdfilter","text":"","code":"bmdfilter(res, BMDfilter = c(\"definedCI\", \"finiteCI\", \"definedBMD\", \"none\"), BMDtype = c(\"zSD\", \"xfold\"))"},{"path":"/reference/bmdfilter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filtering BMDs according to estimation quality — bmdfilter","text":"res dataframe results provided bmdboot bmdcalc (res) subset data frame. Even function intended used just calculation BMD values, biological annotation, can also used within interpretation workflow, extended dataframe additional columns coming example biological annotation items, lines replicated items one annotation. case dataframe must least contain column giving BMD values (BMD.zSD BMD.xfold depending chosen BMDtype), identification curve (id), BMDfilter set \"CIdefined\" \"CIfinite\", columns BMD.zSD.lower, BMD.zSD.upper BMD.xfold.lower, BMD.xfold.upper depending argument BMDtype. BMDfilter \"none\", type filter applied, based BMD estimation. \"definedCI\" (default choice), items point interval estimates BMD successfully calculated retained (items bootstrap procedure failed excluded). \"finiteCI\", items point interval estimates BMD successfully calculated gave values within range tested/observed doses retained. \"definedBMD\", items point estimate BMD estimated value within range tested/observed doses retained. BMDtype type BMD used previously described filtering procedure, \"zSD\" (default choice) \"xfold\".","code":""},{"path":"/reference/bmdfilter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filtering BMDs according to estimation quality — bmdfilter","text":"Using argument BMDfilter three filters proposed retain items associated best estimated BMD values. default recommend retain items BMD confidence interval defined (using \"CIdefined\") (excluding items bootstrap procedure failed). One can even restrictive retaining items BMD confidence interval within range tested/observed doses (using \"CIfinite\"), less restrictive (using \"BMDIdefined\") requiring BMD point estimate must defined within range tested/observed doses (let us recall bmdcalc output, case BMD coded NA). propose option \"none\" case, future, add filters based BMD.","code":""},{"path":"/reference/bmdfilter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filtering BMDs according to estimation quality — bmdfilter","text":"dataframe corresponding subset res given input, can used biological annotation exploration.","code":""},{"path":[]},{"path":"/reference/bmdfilter.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Filtering BMDs according to estimation quality — bmdfilter","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/bmdfilter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filtering BMDs according to estimation quality — bmdfilter","text":"","code":"# (1) a toy example # on a very small subsample of a microarray data set # and a very smal number of bootstrap iterations # (clearly not sufficient, but it is just for illustration) # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package = \"DRomics\") # to test the package on a small but not very small data set # use the following commented line # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package = \"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.05) #> Removing intercept from test coefficients f <- drcfit(s_quad, progressbar = TRUE) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% r <- bmdcalc(f) set.seed(1234) # to get reproducible results with a so small number of iterations (b <- bmdboot(r, niter = 10)) # with a non reasonable value for niter #> Warning: #> A small number of iterations (less than 1000) may not be sufficient to #> ensure a good quality of bootstrap confidence intervals. #> The bootstrap may be long if the number of items and the number of #> bootstrap iterations is high. #> | | | 0% | |==== | 5% | |======= | 10% | |========== | 15% | |============== | 20% | |================== | 25% | |===================== | 30% | |======================== | 35% | |============================ | 40% | |=================================== | 50% | |====================================== | 55% | |========================================== | 60% | |============================================== | 65% | |==================================================== | 75% | |======================================================== | 80% | |============================================================ | 85% | |=============================================================== | 90% | |================================================================== | 95% | |======================================================================| 100% #> Bootstrap confidence interval computation failed on 2 items among 20 #> due to lack of convergence of the model fit for a fraction of the #> bootstrapped samples greater than 0.5. #> For 3 BMD.zSD values and 12 BMD.xfold values among 20 at least one #> bound of the 95 percent confidence interval could not be computed due #> to some bootstrapped BMD values not reachable due to model asymptotes #> or reached outside the range of tested doses (bounds coded Inf)). # !!!! TO GET CORRECT RESULTS # !!!! niter SHOULD BE FIXED FAR LARGER , e.g. to 1000 # !!!! but the run will be longer ### (1.a) Examples on BMD.xfold (with some undefined BMD.xfold values) # Plot of BMDs with no filtering subres <- bmdfilter(b$res, BMDfilter = \"none\") bmdplot(subres, BMDtype = \"xfold\", point.size = 3, add.CI = TRUE) #> Warning: Removed 10 rows containing missing values or values outside the scale range #> (`geom_point()`). #> Warning: Removed 2 rows containing missing values or values outside the scale range #> (`geom_errorbarh()`). # Plot of items with defined BMD point estimate subres <- bmdfilter(b$res, BMDtype = \"xfold\", BMDfilter = \"definedBMD\") bmdplot(subres, BMDtype = \"xfold\", point.size = 3, add.CI = TRUE) #> Warning: Removed 1 row containing missing values or values outside the scale range #> (`geom_errorbarh()`). # Plot of items with defined BMD point estimate and CI bounds subres <- bmdfilter(b$res, BMDtype = \"xfold\", BMDfilter = \"definedCI\") bmdplot(subres, BMDtype = \"xfold\", point.size = 3, add.CI = TRUE) # Plot of items with finite BMD point estimate and CI bounds subres <- bmdfilter(b$res, BMDtype = \"xfold\", BMDfilter = \"finiteCI\") bmdplot(subres, BMDtype = \"xfold\", point.size = 3, add.CI = TRUE) # \\donttest{ ### (1.b) Examples on BMD.zSD (with no undefined BMD.zSD values) # Plot of BMDs with no filtering subres <- bmdfilter(b$res, BMDfilter = \"none\") bmdplot(subres, BMDtype = \"zSD\", point.size = 3, add.CI = TRUE) #> Warning: Removed 2 rows containing missing values or values outside the scale range #> (`geom_errorbarh()`). # Plot items with defined BMD point estimate (the same on this ex.) subres <- bmdfilter(b$res, BMDtype = \"zSD\", BMDfilter = \"definedBMD\") bmdplot(subres, BMDtype = \"zSD\", point.size = 3, add.CI = TRUE) #> Warning: Removed 2 rows containing missing values or values outside the scale range #> (`geom_errorbarh()`). # Plot of items with defined BMD point estimate and CI bounds subres <- bmdfilter(b$res, BMDtype = \"zSD\", BMDfilter = \"definedCI\") bmdplot(subres, BMDtype = \"zSD\", point.size = 3, add.CI = TRUE) # Plot of items with finite BMD point estimate and CI bounds subres <- bmdfilter(b$res, BMDtype = \"zSD\", BMDfilter = \"finiteCI\") bmdplot(subres, BMDtype = \"zSD\", point.size = 3, add.CI = TRUE) # }"},{"path":"/reference/bmdplot.html","id":null,"dir":"Reference","previous_headings":"","what":"BMD plot optionally with confidence intervals on BMD — bmdplot","title":"BMD plot optionally with confidence intervals on BMD — bmdplot","text":"Provides ECDF plot BMD values optionally confidence intervals BMD value /labels items.","code":""},{"path":"/reference/bmdplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BMD plot optionally with confidence intervals on BMD — bmdplot","text":"","code":"bmdplot(extendedres, BMDtype = c(\"zSD\", \"xfold\"), add.CI = FALSE, facetby, facetby2, shapeby, colorby, point.size = 1.5, point.alpha = 0.8, line.size = 0.5, line.alpha = 0.8, ncol4faceting, add.label = FALSE, label.size = 2, BMD_log_transfo = TRUE)"},{"path":"/reference/bmdplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BMD plot optionally with confidence intervals on BMD — bmdplot","text":"extendedres dataframe results provided plot.bmdcalc plot.bmdboot (res) subset data frame (selected lines). dataframe can extended additional columns coming example functional annotation items, lines can replicated corresponding item one annotation. dataframe must least contain column giving BMD values (BMD.zSD BMD.xfold depending chosen BMDtype), identification curve (id), add.CI TRUE, columns BMD.zSD.lower, BMD.zSD.upper BMD.xfold.lower, BMD.xfold.upper depending argument BMDtype. BMDtype type BMD plot, \"zSD\" (default choice) \"xfold\". add.CI TRUE (default choice FALSE) item confidence interval added. facetby optional argument naming column extendedres chosen split plot facets using ggplot2::facet_wrap (split omitted). facetby2 optional argument naming column extendedres chosen additional argument split plot facets using ggplot2::facet_grid, columns defined facetby rows defined facetby2 (split omitted). shapeby optional argument naming column extendedres chosen shape BMD points (difference shapeby omitted). colorby optional argument naming column extendedres chosen color BMD points (difference colorby omitted). point.size Size BMD points. point.alpha Transparency points. line.size Width lines. line.alpha Transparency lines. ncol4faceting Number columns facetting (used facetby2 also provided. add.label Points replaced labels items TRUE. label.size Size labels add.label TRUE. BMD_log_transfo TRUE, default option, log transformation BMD used plot.","code":""},{"path":"/reference/bmdplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BMD plot optionally with confidence intervals on BMD — bmdplot","text":"BMD values plotted ECDF plot, plot.bmdcalc using \"ecdf\" plottype confidence intervals BMD value /labels items requested. optional use columns code shape /facets item particularly intended give view dose-response per group (e.g. metabolic pathways). groups must coded column extendedres. case one item allocated one group annotation process, line item must replicated extendedres many times number annotation groups allocated.","code":""},{"path":"/reference/bmdplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"BMD plot optionally with confidence intervals on BMD — bmdplot","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/bmdplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BMD plot optionally with confidence intervals on BMD — bmdplot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/bmdplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BMD plot optionally with confidence intervals on BMD — bmdplot","text":"","code":"# (1) # Plot of BMD values with color dose-response gradient # faceted by metabolic pathway (from annotation of the selected items) # and shaped by dose-response trend # An example from the paper published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # A example of plot obtained with this function is in Figure 5 in Larras et al. 2020 # the dataframe with metabolomic results (output $res of bmdcalc() or bmdboot() functions) resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # the dataframe with annotation of each item identified in the previous file # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe metabextendedres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(metabextendedres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport ### (1.a) BMDplot by pathway shaped by trend bmdplot(metabextendedres, BMDtype = \"zSD\", facetby = \"path_class\", shapeby = \"trend\") # \\donttest{ ### (1.b) BMDplot by pathway with items labels bmdplot(metabextendedres, BMDtype = \"zSD\", facetby = \"path_class\", add.label = TRUE, label.size = 2) ### (1.c) BMDplot by pathway with confidence intervals bmdplot(metabextendedres, BMDtype = \"zSD\", facetby = \"path_class\", add.CI = TRUE) ### (1.d) BMDplot by pathway with confidence intervals # in BMD raw scale (not default log scale) bmdplot(metabextendedres, BMDtype = \"zSD\", facetby = \"path_class\", add.CI = TRUE, BMD_log_transfo = FALSE) ### (1.e) BMDplot by pathway with confidence intervals # colored by trend and playing with graphical parameters bmdplot(metabextendedres, BMDtype = \"zSD\", facetby = \"path_class\", add.CI = TRUE, colorby = \"trend\", point.size = 2, point.alpha = 0.5, line.size = 0.8, line.alpha = 0.5) # (2) # An example with two molecular levels # # Import the dataframe with transcriptomic results contigresfilename <- system.file(\"extdata\", \"triclosanSVcontigres.txt\", package = \"DRomics\") contigres <- read.table(contigresfilename, header = TRUE, stringsAsFactors = TRUE) str(contigres) #> 'data.frame':\t447 obs. of 27 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... # Import the dataframe with functional annotation (or any other descriptor/category # you want to use, here KEGG pathway classes) contigannotfilename <- system.file(\"extdata\", \"triclosanSVcontigannot.txt\", package = \"DRomics\") contigannot <- read.table(contigannotfilename, header = TRUE, stringsAsFactors = TRUE) str(contigannot) #> 'data.frame':\t562 obs. of 2 variables: #> $ contig : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ path_class: Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... # Merging of both previous dataframes contigextendedres <- merge(x = contigres, y = contigannot, by.x = \"id\", by.y = \"contig\") # to see the structure of this dataframe str(contigextendedres) #> 'data.frame':\t562 obs. of 28 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... #> $ path_class : Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... ### Merge metabolomic and transcriptomic results extendedres <- rbind(metabextendedres, contigextendedres) extendedres$molecular.level <- factor(c(rep(\"metabolites\", nrow(metabextendedres)), rep(\"contigs\", nrow(contigextendedres)))) str(extendedres) #> 'data.frame':\t646 obs. of 29 variables: #> $ id : Factor w/ 478 levels \"NAP47_51\",\"NAP_2\",..: 1 2 3 4 4 4 4 5 6 7 ... #> $ irow : int 46 2 21 28 28 28 28 34 38 47 ... #> $ adjpvalue : num 7.16e-04 6.23e-05 1.11e-05 1.03e-05 1.03e-05 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 3 3 3 3 3 2 2 4 ... #> $ nbpar : int 2 3 2 2 2 2 2 3 3 5 ... #> $ b : num -0.056 0.4598 -0.0595 -0.0451 -0.0451 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 7.34 5.94 5.39 7.86 7.86 ... #> $ e : num NA -1.65 NA NA NA ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.1245 0.126 0.0793 0.052 0.052 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 7 7 7 7 7 2 2 9 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 3 3 3 3 1 ... #> $ y0 : num 7.34 5.94 5.39 7.86 7.86 ... #> $ yrange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ maxychange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 2.224 0.528 1.333 1.154 1.154 ... #> $ BMR.zSD : num 7.22 5.82 5.31 7.81 7.81 ... #> $ BMD.xfold : num NA NA NA NA NA ... #> $ BMR.xfold : num 6.61 5.35 4.85 7.07 7.07 ... #> $ BMD.zSD.lower : num 0.979 0.2 0.853 0.752 0.752 ... #> $ BMD.zSD.upper : num 4.07 1.11 1.75 1.46 1.46 ... #> $ BMD.xfold.lower : num Inf Inf 7.61 Inf Inf ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 1000 957 1000 1000 1000 1000 1000 648 620 872 ... #> $ path_class : Factor w/ 18 levels \"Amino acid metabolism\",..: 5 5 3 3 2 6 8 5 5 5 ... #> $ molecular.level : Factor w/ 2 levels \"contigs\",\"metabolites\": 2 2 2 2 2 2 2 2 2 2 ... ### BMD plot per pathway with molecular level coding for color bmdplot(extendedres, BMDtype = \"zSD\", facetby = \"path_class\", colorby = \"molecular.level\", point.alpha = 0.3) ### BMD plot per pathway and per molecular level # for a selection of pathways chosen_path_class <- c(\"Membrane transport\", \"Lipid metabolism\") ischosen <- is.element(extendedres$path_class, chosen_path_class) bmdplot(extendedres[ischosen, ], BMDtype = \"zSD\", facetby = \"path_class\", facetby2 = \"molecular.level\", colorby = \"trend\", point.size = 2, add.CI = TRUE) # }"},{"path":"/reference/bmdplotwithgradient.html","id":null,"dir":"Reference","previous_headings":"","what":"BMD plot with color gradient — bmdplotwithgradient","title":"BMD plot with color gradient — bmdplotwithgradient","text":"Provides ECDF plot BMD values horizontal color gradient coding, item, theoretical signal function dose (concentration). idea display amplitude intensity response item BMD ECDF plot, addition BMD ordered values. plot interest especially much items presented. maximize lisibility plot, one can manually pre-select items based criteria (e.g. functional group interest).","code":""},{"path":"/reference/bmdplotwithgradient.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BMD plot with color gradient — bmdplotwithgradient","text":"","code":"bmdplotwithgradient(extendedres, BMDtype = c(\"zSD\", \"xfold\"), xmin, xmax, y0shift = TRUE, scaling = TRUE, facetby, facetby2, shapeby, npoints = 50, line.size, point.size = 1, ncol4faceting, limits4colgradient, lowercol = \"darkblue\", uppercol = \"darkred\", add.label, label.size = 2, BMD_log_transfo = TRUE)"},{"path":"/reference/bmdplotwithgradient.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BMD plot with color gradient — bmdplotwithgradient","text":"extendedres dataframe results provided bmdcalc (res) subset data frame (selected lines). dataframe can extended additional columns coming example functional annotation items, lines can replicated corresponding item one annotation. extended dataframe must least contain column giving BMD values (BMD.zSD BMD.xfold depending chosen BMDtype), identification curve (id), column model naming fitted model values parameters (columns b, c, d, e, f). BMDtype type BMD plot, \"zSD\" (default choice) \"xfold\". xmin Optional minimal dose/concentration definition x range. xmax Optional maximal dose/concentration definition x range (can defined max(f$omicdata$dose) f output drcfit() example). y0shift TRUE (default choice) item signal shifted theoretical signal control 0. scaling TRUE, default choice, item signal shifted theoretical signal control 0 scaled dividing maximal absolute signal change () signal control maxychange. facetby optional argument naming column extendedres chosen split plot facets using ggplot2::facet_wrap (split omitted). facetby2 optional argument naming column extendedres chosen additional argument split plot facets using ggplot2::facet_grid, columns defined facetby rows defined facetby2 (split omitted). shapeby optional argument naming column extendedres chosen shape BMD points (difference shapeby omitted). npoints Number points computed curve order define signal color gradient (= number doses concentrations theoretical signal computed fitted model item). line.size Size horizontal lines plotting signal color gradient. point.size Size BMD points. ncol4faceting Number columns facetting (used facetby2 also provided. limits4colgradient Optional vector giving minimal maximal value signal color gradient. lowercol Chosen color lower values signal. uppercol Chosen color upper values signal. add.label Points replaced labels items TRUE. label.size Size labels add.label TRUE. BMD_log_transfo TRUE, default option, log transformation BMD used plot. option used null value xmin input.","code":""},{"path":"/reference/bmdplotwithgradient.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BMD plot with color gradient — bmdplotwithgradient","text":"BMD values plotted ECDF plot, plot.bmdcalc using \"ecdf\" plottype. addition plotted horizontal color gradient item coding signal level dose (concentration). optional use columns code shape /facets item particularly intended give view dose-response per group (e.g. metabolic pathways). groups must coded column extendedres. case one item allocated one group annotation process, line item must replicated extendedres many times number annotation groups allocated. item extended dataframe, name model (column model) values parameters (columns b, c, d, e, f) used compute theoretical dose-response curves, corresponding signal color gradient, range [xmin ; xmax].","code":""},{"path":"/reference/bmdplotwithgradient.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"BMD plot with color gradient — bmdplotwithgradient","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/bmdplotwithgradient.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BMD plot with color gradient — bmdplotwithgradient","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/bmdplotwithgradient.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BMD plot with color gradient — bmdplotwithgradient","text":"","code":"# (1) # A toy example on a very small subsample of a microarray data set datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package=\"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.01) #> Removing intercept from test coefficients f <- drcfit(s_quad, progressbar = TRUE) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |==== | 6% | |======== | 12% | |============ | 18% | |================ | 24% | |===================== | 29% | |========================= | 35% | |============================= | 41% | |================================= | 47% | |===================================== | 53% | |========================================= | 59% | |============================================= | 65% | |================================================= | 71% | |====================================================== | 76% | |========================================================== | 82% | |============================================================== | 88% | |================================================================== | 94% | |======================================================================| 100% r <- bmdcalc(f) # Plot of all the BMD values with color dose-response gradient # bmdplotwithgradient(r$res, BMDtype = \"zSD\") #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # \\donttest{ # Same plot without signal scaling # bmdplotwithgradient(r$res, BMDtype = \"zSD\", scaling = FALSE) #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # Plot of all the BMD values with color dose-response gradient # with definition of xmax from the maximal tested dose # bmdplotwithgradient(r$res, BMDtype = \"zSD\", xmax = max(f$omicdata$dose)) # Add of item labels bmdplotwithgradient(r$res, BMDtype = \"zSD\", xmax = max(f$omicdata$dose), add.label = TRUE) # The same plot in raw scale (we can fix xmin at 0 in this case) # bmdplotwithgradient(r$res, BMDtype = \"zSD\", xmin = 0, BMD_log_transfo = FALSE) #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # The same plot in log scale with defining xmin and xmax at a chosen values # bmdplotwithgradient(r$res, BMDtype = \"zSD\", xmin = min(f$omicdata$dose[f$omicdata$dose != 0] / 2), xmax = max(f$omicdata$dose), BMD_log_transfo = TRUE) # Plot of all the BMD values with color dose-response gradient # faceted by response trend and shaped by model # bmdplotwithgradient(r$res, BMDtype = \"zSD\", facetby = \"trend\", shapeby = \"model\") #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # same plot changing the names of the facets levels(r$res$trend) #> [1] \"U\" \"bell\" \"dec\" \"inc\" levels(r$res$trend) <- c(\"bell shape\", \"decreasing\", \"increasing\", \"U shape\") bmdplotwithgradient(r$res, BMDtype = \"zSD\", facetby = \"trend\", shapeby = \"model\") #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # same plot changing the labels of the legends # and inversing the two guides if (require(ggplot2)) bmdplotwithgradient(r$res, BMDtype = \"zSD\", facetby = \"trend\", shapeby = \"model\") + labs(col = \"signal value\", shape = \"model\") + guides(colour = guide_colourbar(order = 1), shape = guide_legend(order = 2)) #> Loading required package: ggplot2 #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # (2) # Plot of BMD values with color dose-response gradient # faceted by metabolic pathway (from annotation of the selected items) # and shaped by dose-response trend # An example from the paper published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # A example of plot obtained with this function is in Figure 5 in Larras et al. 2020 # the dataframe with metabolomic results (output $res of bmdcalc() or bmdboot() functions) resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # the dataframe with annotation of each item identified in the previous file # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe extendedres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(extendedres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport ### (2.a) BMDplot with gradient by pathway bmdplotwithgradient(extendedres, BMDtype = \"zSD\", facetby = \"path_class\", xmax = 7.76, # maximal tested dose in those data shapeby = \"trend\") ### (2.a) BMDplot with gradient by pathway without scaling bmdplotwithgradient(extendedres, BMDtype = \"zSD\", facetby = \"path_class\", xmax = 7.76, shapeby = \"trend\", scaling = FALSE) # (2.b) BMDplot with gradient by pathway # forcing the limits of the colour gradient at other # values than observed minimal and maximal values of the signal bmdplotwithgradient(extendedres, BMDtype = \"zSD\", facetby = \"path_class\", shapeby = \"trend\", limits4colgradient = c(-1, 1)) #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # (2.c) The same example changing the gradient colors and the line size bmdplotwithgradient(extendedres, BMDtype = \"zSD\", facetby = \"path_class\", shapeby = \"trend\", line.size = 3, lowercol = \"darkgreen\", uppercol = \"orange\") #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # (2.d) The same example with only lipid metabolism pathclass # and identification of the metabolites LMres <- extendedres[extendedres$path_class == \"Lipid metabolism\", ] bmdplotwithgradient(LMres, BMDtype = \"zSD\", line.size = 3, add.label = TRUE, label.size = 3) #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # (3) # An example on a microarray data set (a subsample of a greater data set) # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 1000 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" #> [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" #> Data were normalized between arrays using the following method: cyclicloess (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.001)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.001: 78 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |= | 1% | |== | 3% | |=== | 4% | |==== | 5% | |==== | 6% | |===== | 8% | |====== | 9% | |======= | 10% | |======== | 12% | |========= | 13% | |========== | 14% | |=========== | 15% | |============ | 17% | |============= | 18% | |============= | 19% | |============== | 21% | |=============== | 22% | |================ | 23% | |================= | 24% | |================== | 26% | |=================== | 27% | |==================== | 28% | |===================== | 29% | |====================== | 31% | |====================== | 32% | |======================= | 33% | |======================== | 35% | |========================= | 36% | |========================== | 37% | |=========================== | 38% | |============================ | 40% | |============================= | 41% | |============================== | 42% | |=============================== | 44% | 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|=============================================================== | 90% | |================================================================ | 91% | |================================================================= | 92% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |===================================================================== | 99% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 11 dose-response curves out of 78 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 67 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 0 11 30 23 #> log-Gauss-probit #> 3 #> Distribution of the trends (curve shapes) among the 67 fitted dose-response curves: #> #> U bell dec inc #> 6 20 22 19 (r <- bmdcalc(f)) #> 1 BMD-xfold values and 0 BMD-zSD values are not defined (coded NaN as #> the BMR stands outside the range of response values defined by the model). #> 28 BMD-xfold values and 0 BMD-zSD values could not be calculated (coded #> NA as the BMR stands within the range of response values defined by the #> model but outside the range of tested doses). bmdplotwithgradient(r$res, BMDtype = \"zSD\", facetby = \"trend\", shapeby = \"model\") #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # without scaling bmdplotwithgradient(r$res, BMDtype = \"zSD\", scaling = FALSE, facetby = \"trend\", shapeby = \"model\") #> Warning: #> By default xmax was fixed at the maximal BMD value, but you could also #> fix it at the maximal tested dose. # }"},{"path":"/reference/continuousanchoringdata.html","id":null,"dir":"Reference","previous_headings":"","what":"Import and check of continuous anchoring apical data — continuousanchoringdata","title":"Import and check of continuous anchoring apical data — continuousanchoringdata","text":"Continuous anchoring apical data imported .txt file (internally imported using function read.table) checked R object class data.frame (see description argument file required format data). transformation provided function. needed pretreatment data must done importation data, can directly modelled using normal error model. strong hypothesis required selection responsive endpoints dose-reponse modelling.","code":""},{"path":"/reference/continuousanchoringdata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import and check of continuous anchoring apical data — continuousanchoringdata","text":"","code":"continuousanchoringdata(file, backgrounddose, check = TRUE) # S3 method for class 'continuousanchoringdata' print(x, ...) # S3 method for class 'continuousanchoringdata' plot(x, dose_log_transfo = TRUE, ...)"},{"path":"/reference/continuousanchoringdata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import and check of continuous anchoring apical data — continuousanchoringdata","text":"file name .txt file (e.g. \"mydata.txt\") containing one row per endpoint, first column corresponding identifier endpoint, columns giving measured values endpoint replicate dose concentration. first line, name endpoint column, must tested doses concentrations numeric format corresponding replicate (example, triplicates treatment, first line \"endpoint\", 0, 0, 0, 0.1, 0.1, 0.1, etc.). file imported within function using function read.table default field separator (sep argument) default decimal separator (dec argument \".\"). Alternatively R object class data.frame can directly given input, corresponding output read.table(file, header = FALSE) file described . two alternatives illustrated examples. backgrounddose argument must used dose zero data, prevent calculation BMD extrapolation. doses equal value given backgrounddose fixed 0, considered background level exposition. check TRUE format input file checked. x object class \"continuousanchoringdata\". dose_log_transfo TRUE log transformation dose used plot. ... arguments passed print plot functions.","code":""},{"path":"/reference/continuousanchoringdata.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Import and check of continuous anchoring apical data — continuousanchoringdata","text":"function imports data, checks format (see description argument file required format data) gives print information help user check coding data correct : tested doses (concentrations) number replicates dose, number endpoints.","code":""},{"path":"/reference/continuousanchoringdata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import and check of continuous anchoring apical data — continuousanchoringdata","text":"continuousanchoringdata returns object class \"continuousanchoringdata\", list 7 components: data numeric matrix responses item replicate (one line per item, one column per replicate) dose numeric vector tested doses concentrations corresponding column data item character vector identifiers endpoints, corresponding line data design table experimental design (tested doses number replicates dose) control user data.mean numeric matrix mean responses item per dose (mean corresponding replicates) (one line per item, one column per unique value dose data.sd numeric matrix standard deviations response item per dose (sd corresponding replicates, NA replicate) (one line per item, one column per unique value dose) containsNA TRUE data set contains NA values print continuousanchoringdata object gives tested doses (concentrations) number replicates dose, number items, identifiers first 20 items (check good coding data) normalization method. plot continuousanchoringdata object shows data distribution dose concentration replicate.","code":""},{"path":[]},{"path":"/reference/continuousanchoringdata.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Import and check of continuous anchoring apical data — continuousanchoringdata","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/continuousanchoringdata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import and check of continuous anchoring apical data — continuousanchoringdata","text":"","code":"# (1) import and check of continuous anchoring data # (an example with two apical endpoints of an example given in the package (see ?Scenedesmus)) # datafilename <- system.file(\"extdata\", \"apical_anchoring.txt\", package = \"DRomics\") o <- continuousanchoringdata(datafilename, backgrounddose = 0.1, check = TRUE) #> Warning: #> We recommend you to check that your anchoring data are continuous and #> defined in a scale that enable the use of a normal error model (needed #> at each step of the workflow including the selection step). # It is here necessary to define the background dose as there is no dose at 0 in the data # The BMD cannot be computed without defining the background level print(o) #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 2.4 3.8 6.2 10.1 16.5 26.8 43.5 70.7 #> 12 6 2 2 2 6 2 2 2 #> Number of endpoints: 2 #> Names of the endpoints: #> [1] \"growth\" \"photosynthesis\" # \\donttest{ plot(o) #> Warning: log-10 transformation introduced infinite values. # } # If you want to use your own data set just replace datafilename, # the first argument of continuousanchoringdata(), # by the name of your data file (e.g. \"mydata.txt\") # # You should take care that the field separator of this data file is one # of the default field separators recognised by the read.table() function # when it is used with its default field separator (sep argument) # Tabs are recommended. # Use of an R object of class data.frame # on the same example (see ?Scenedesmus for details) data(Scenedesmus_apical) o <- continuousanchoringdata(Scenedesmus_apical, backgrounddose = 0.1) #> Warning: #> We recommend you to check that your anchoring data are continuous and #> defined in a scale that enable the use of a normal error model (needed #> at each step of the workflow including the selection step). print(o) #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 2.4 3.8 6.2 10.1 16.5 26.8 43.5 70.7 #> 12 6 2 2 2 6 2 2 2 #> Number of endpoints: 2 #> Names of the endpoints: #> [1] \"growth\" \"photosynthesis\" # \\donttest{ plot(o) #> Warning: log-10 transformation introduced infinite values. # }"},{"path":"/reference/curvesplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of fitted curves — curvesplot","title":"Plot of fitted curves — curvesplot","text":"Provides plot fitted curves dataframe main workflow results, possibly extended additional information (e.g. groups functional annotation) used color /split curves.","code":""},{"path":"/reference/curvesplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of fitted curves — curvesplot","text":"","code":"curvesplot(extendedres, xmin, xmax, y0shift = TRUE, scaling = TRUE, facetby, facetby2, free.y.scales = FALSE, ncol4faceting, colorby, removelegend = FALSE, npoints = 500, line.size = 0.5, line.alpha = 0.8, dose_log_transfo = TRUE, addBMD = TRUE, BMDtype = c(\"zSD\", \"xfold\"), point.size = 1, point.alpha = 0.8)"},{"path":"/reference/curvesplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of fitted curves — curvesplot","text":"extendedres dataframe results provided bmdcalc (res) subset data frame (selected lines). dataframe can extended additional columns coming example annotation items, lines can replicated corresponding item one annotation. extended dataframe must least contain column giving identification curve (id), column model naming fitted model values parameters (columns b, c, d, e, f) column coding chosen BMD, default BMD.zSD BMD.xfold BMDtype \"xfold\". xmin defined, value just maximum BMD values fixed x dose scale log, 0 otherwise. xmax Maximal dose/concentration definition x range (can defined max(f$omicdata$dose) f output drcfit()). defined, value just maximum BMD values taken. y0shift TRUE (default choice) curves shifted theoretical signal control 0. scaling TRUE, default choice, curves shifted theoretical signal control 0 y0 scaled dividing maximal absolute signal change () signal control maxychange. facetby optional argument naming column extendedres chosen split plot facets (split omitted). facetby2 optional argument naming column extendedres chosen additional argument split plot facets using ggplot2::facet_grid, columns defined facetby rows defined facetby2 (split omitted). free.y.scales TRUE y scales free different facets. ncol4faceting Number columns facetting (used facetby2 also provided. colorby optional argument naming column extendedres chosen color curves (color omitted). removelegend TRUE color legend removed (useful number colors great). npoints Number points computed curve plot . line.size Width lines plotting curves. line.alpha Transparency lines plotting curves. dose_log_transfo TRUE log transformation dose used plot. option needs definition strictly positive value xmin input. addBMD TRUE points added curve BMD-BMR values (requires BMD BMD values first argument extendedres). BMDtype type BMD add, \"zSD\" (default choice) \"xfold\". point.size Size BMD-BMR points added curves. point.alpha Transparency BMD-BMR points added curves.","code":""},{"path":"/reference/curvesplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot of fitted curves — curvesplot","text":"item extended dataframe, name model (column model) values parameters (columns b, c, d, e, f) used compute theoretical dose-response curves range [xmin ; xmax].","code":""},{"path":"/reference/curvesplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot of fitted curves — curvesplot","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/curvesplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot of fitted curves — curvesplot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/curvesplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot of fitted curves — curvesplot","text":"","code":"# (1) A toy example on a very small subsample of a microarray data set) # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package = \"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.01) #> Removing intercept from test coefficients f <- drcfit(s_quad, progressbar = TRUE) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |==== | 6% | |======== | 12% | |============ | 18% | |================ | 24% | |===================== | 29% | |========================= | 35% | |============================= | 41% | |================================= | 47% | |===================================== | 53% | |========================================= | 59% | |============================================= | 65% | |================================================= | 71% | |====================================================== | 76% | |========================================================== | 82% | |============================================================== | 88% | |================================================================== | 94% | |======================================================================| 100% r <- bmdcalc(f) # (1.a) # Default plot of all the curves with BMD values added as points on the curve # curvesplot(r$res, xmax = max(f$omicdata$dose)) # \\donttest{ # use of line size, point size, transparency curvesplot(r$res, xmax = max(f$omicdata$dose), line.alpha = 0.2, line.size = 1, point.alpha = 0.3, point.size = 1.8) # the same plot with dose not in log scale # fixing xmin and xmax curvesplot(r$res, xmin = 0.1, xmax = max(f$omicdata$dose), dose_log_transfo = FALSE, addBMD = TRUE) # or not curvesplot(r$res, dose_log_transfo = FALSE, addBMD = TRUE) # plot of curves colored by models curvesplot(r$res, xmax = max(f$omicdata$dose), colorby = \"model\") # plot of curves facetted by item curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"id\") # plot of curves facetted by trends curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"trend\") # the same plot with free y scales curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"trend\", free.y.scales = TRUE) # (1.b) # Plot of all the curves without shifting y0 values to 0 # and without scaling curvesplot(r$res, xmax = max(f$omicdata$dose), scaling = FALSE, y0shift = FALSE) # (1.c) # Plot of all the curves colored by model, with one facet per trend # curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"trend\", colorby = \"model\") # changing the number of columns curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"trend\", colorby = \"model\", ncol4faceting = 4) # playing with size and transparency of lines curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"trend\", colorby = \"model\", line.size = 0.5, line.alpha = 0.8) curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"trend\", colorby = \"model\", line.size = 0.8, line.alpha = 0.2) curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = \"trend\", line.size = 1, line.alpha = 0.2) # (2) an example on a microarray data set (a subsample of a greater data set) # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 1000 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" #> [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" #> Data were normalized between arrays using the following method: cyclicloess (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.001)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.001: 78 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |= | 1% | |== | 3% | |=== | 4% | |==== | 5% | |==== | 6% | |===== | 8% | |====== | 9% | |======= | 10% | |======== | 12% | |========= | 13% 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|================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |===================================================================== | 99% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 11 dose-response curves out of 78 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 67 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 0 11 30 23 #> log-Gauss-probit #> 3 #> Distribution of the trends (curve shapes) among the 67 fitted dose-response curves: #> #> U bell dec inc #> 6 20 22 19 (r <- bmdcalc(f)) #> 1 BMD-xfold values and 0 BMD-zSD values are not defined (coded NaN as #> the BMR stands outside the range of response values defined by the model). #> 28 BMD-xfold values and 0 BMD-zSD values could not be calculated (coded #> NA as the BMR stands within the range of response values defined by the #> model but outside the range of tested doses). # plot split by trend and model with BMR-BMD points added on curves # adding transparency curvesplot(r$res, xmax = max(f$omicdata$dose), line.alpha = 0.2, line.size = 0.8, addBMD = TRUE, point.alpha = 0.2, point.size = 1.5, facetby = \"trend\", facetby2 = \"model\") # same plot without scaling and not in log dose scale curvesplot(r$res, xmax = max(f$omicdata$dose), line.alpha = 0.2, line.size = 0.8, dose_log_transfo = FALSE, addBMD = TRUE, point.alpha = 0.2, point.size = 1.5, scaling = FALSE, facetby = \"trend\", facetby2 = \"model\") # (3) An example from data published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # a dataframe with metabolomic results (output $res of bmdcalc() or bmdboot() functions) resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # a dataframe with annotation of each item identified in the previous file # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe # bootstrap results and annotation extendedres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(extendedres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport # Plot of the dose-response curves by pathway colored by trend # with BMR-BMD points added on curves curvesplot(extendedres, facetby = \"path_class\", npoints = 100, line.size = 0.5, colorby = \"trend\", xmax = 7, addBMD = TRUE) # The same plot not in log scale curvesplot(extendedres, facetby = \"path_class\", npoints = 100, line.size = 0.5, dose_log_transfo = FALSE, colorby = \"trend\", xmin = 0, xmax = 7) # The same plot in log scale without scaling curvesplot(extendedres, facetby = \"path_class\", npoints = 100, line.size = 0.5, colorby = \"trend\", scaling = FALSE, xmax = 7) # Plot of the dose-response curves split by pathway and by trend # for a selection pathway chosen_path_class <- c(\"Membrane transport\", \"Lipid metabolism\") ischosen <- is.element(extendedres$path_class, chosen_path_class) curvesplot(extendedres[ischosen, ], facetby = \"trend\", facetby2 = \"path_class\", npoints = 100, line.size = 0.5, xmax = 7) # Plot of the dose-response curves for a specific pathway # in this example the \"lipid metabolism\" pathclass LMres <- extendedres[extendedres$path_class == \"Lipid metabolism\", ] curvesplot(LMres, facetby = \"id\", npoints = 100, line.size = 0.8, point.size = 2, colorby = \"trend\", xmax = 7) # }"},{"path":"/reference/drcfit.html","id":null,"dir":"Reference","previous_headings":"","what":"Dose response modelling for responsive items — drcfit","title":"Dose response modelling for responsive items — drcfit","text":"Fits dose reponse models responsive items.","code":""},{"path":"/reference/drcfit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dose response modelling for responsive items — drcfit","text":"","code":"drcfit(itemselect, information.criterion = c(\"AICc\", \"BIC\", \"AIC\"), deltaAICminfromnullmodel = 2, postfitfilter = TRUE, preventsfitsoutofrange = TRUE, enablesfequal0inGP = TRUE, enablesfequal0inLGP = TRUE, progressbar = TRUE, parallel = c(\"no\", \"snow\", \"multicore\"), ncpus) # S3 method for class 'drcfit' print(x, ...) # S3 method for class 'drcfit' plot(x, items, plot.type = c(\"dose_fitted\", \"dose_residuals\",\"fitted_residuals\"), dose_log_transfo = TRUE, BMDoutput, BMDtype = c(\"zSD\", \"xfold\"), ...) plotfit2pdf(x, items, plot.type = c(\"dose_fitted\", \"dose_residuals\", \"fitted_residuals\"), dose_log_transfo = TRUE, BMDoutput, BMDtype = c(\"zSD\", \"xfold\"), nrowperpage = 6, ncolperpage = 4, path2figs = getwd())"},{"path":"/reference/drcfit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dose response modelling for responsive items — drcfit","text":"itemselect object class \"itemselect\" returned function itemselect. information.criterion information criterion used select best fit model, \"AICc\" recommended default choice (corrected version AIC recommended small samples (see Burnham Anderson 2004), \"BIC\" \"AIC\". deltaAICminfromnullmodel minimal difference chosen information criterion (AICc, AIC BIC) null model best fit model, requested accept fit bestfit model. default fixed 2 keep models fit data clearly better null model, can fixed 0 less stringent. postfitfilter TRUE fits significant trends residuals (showing global significant quadratic trend residuals function dose (rank-scale)) considered failures eliminated. strongly recommended let TRUE, default value. preventsfitsoutofrange TRUE fits Gaussian log-Gaussian models give extremum value outside range observed signal item eliminated candidate models item, choice best. strongly recommended let TRUE, default value. enablesfequal0inGP TRUE fit Gauss-probit model 5 parameters successful, simplified version f = 0 also fitted included candidate models. submodel log-Gauss-probit model corresponds probit model. recommend let argument TRUE, default value, order prevent overfitting, prefer description monotonic curve parameter f necessary model data according information criterion. enablesfequal0inLGP TRUE fit log-Gauss-probit model 5 parameters successful, simplified version f = 0 also fitted included candidate models. submodel log-Gauss-probit model corresponds log-probit model. recommend let argument TRUE, default value, order prevent overfitting prefer description monotonic curve parameter f necessary model data according information criterion. progressbar TRUE progress bar used follow fitting process. parallel type parallel operation used, \"snow\" \"multicore\" (second one available Windows), \"\" parallel operation. ncpus Number processes used parallel operation : typically one fix number available CPUs. x object class \"drcfit\". items Argument plot.drcfit function : number first fits plot (20 items max) character vector specifying identifiers items plot (20 items max). plot.type type plot, default \"dose_fitted\" plot fitted curves observed points added plot observed means dose added black plain circles, \"dose_residuals\" plot residuals function dose, \"fitted_residuals\" plot residuals function fitted value. dose_log_transfo default TRUE use log transformation dose axis (used dose x-axis, plot.type \"fitted_residuals\"). BMDoutput Argument can used add BMD values optionally confidence intervals plot type \"dose_fitted\". must previously apply bmdcalc optionally bmdboot x class drcfit give argument output bmdcalc bmdboot. BMDtype type BMD add plot, \"zSD\" (default choice) \"xfold\" (used BMDoutput missing). nrowperpage Number rows plots plots saved pdf file using plotfit2pdf() (passed facet_wrap()). ncolperpage Number columns plots plots saved pdf file using plotfit2pdf() (passed facet_wrap()). path2figs File path plots saved pdf file using plotfit2pdf() ... arguments passed graphical print functions.","code":""},{"path":"/reference/drcfit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dose response modelling for responsive items — drcfit","text":"selected item, five dose-response models (linear, Hill, exponential, Gauss-probit log-Gauss-probit, see Larras et al. 2018 definition) fitted non linear regression, using nls function. fit biphasic model gives extremum value range observed signal, eliminated (may happen rare cases, especially observational data number samples high dose uncontrolled, doses distributed along dose range). best fit chosen one giving lowest AICc (BIC AIC) value. use AICc (second-order Akaike criterion) instead AIC strongly recommended prevent overfitting may occur dose-response designs small number data points (Hurvich Tsai, 1989; Burnham Anderson DR, 2004). Note extremely rare cases number data points great, AIC converge AICc procedures equivalent. Items best AICc value lower AICc value null model (constant model) minus 2 eliminated. Items best fit showing global significant quadratic trend residuals function dose (rank-scale) also eliminated (best fit considered reliable cases). retained item classified four classes global trend, can used roughly describe shape dose-response curve: inc increasing curves, dec decreasing curves , U U-shape curves, bell bell-shape curves. curves fitted Gauss-probit model can classified increasing decreasing dose value extremum reached zero simplified version f = 0 retained (corresponding probit model). curves fitted log-Gauss-probit model can classified increasing decreasing simplified version f = 0 retained (corresponding log-probit model). retained item thus classified 16 class typology depending chosen model parameter values : H.inc increasing Hill curves, H.dec decreasing Hill curves, L.inc increasing linear curves, L.dec decreasing linear curves, E.inc.convex increasing convex exponential curves, E.dec.concave decreasing concave exponential curves, E.inc.concave increasing concave exponential curves, E.dec.convex decreasing convex exponential curves, GP.U U-shape Gauss-probit curves, GP.bell bell-shape Gauss-probit curves, GP.inc increasing Gauss-probit curves, GP.dec decreasing Gauss-probit curves, lGP.U U-shape log-Gauss-probit curves, lGP.bell bell-shape log-Gauss-probit curves. lGP.inc increasing log-Gauss-probit curves, lGP.dec decreasing log-Gauss-probit curves,","code":""},{"path":"/reference/drcfit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dose response modelling for responsive items — drcfit","text":"drcfit returns object class \"drcfit\", list 4 components: fitres data frame reporting results fit selected item successful fit reached (one line per item) sorted ascending order adjusted p-values returned function itemselect. different columns correspond identifier item (id), row number item initial data set (irow), adjusted p-value selection step (adjpvalue), name best fit model (model), number fitted parameters (nbpar), values parameters b, c, d, e f, (NA non used parameters), residual standard deviation (SDres), typology curve (typology), rough trend curve (trend) defined four classes (U, bell, increasing decreasing shape), theoretical y value control y0), theoretical y value maximal dose yatdosemax), theoretical y range x within range tested doses (yrange), maximal absolute y change () control(maxychange) biphasic curves x value extremum reached (xextrem) corresponding y value (yextrem). omicdata object containing data, given input itemselect() also component output itemselect(). information.criterion information criterion used select best fit model given input. information.criterion.val data frame reporting IC values (AICc, BIC AIC) values selected item (one line per item) fitted model (one colum per model IC value fixed Inf fit failed). n.failure number previously selected items workflow failed fit acceptable model. unfitres data frame reporting results selected item successful fit reached (one line per item) sorted ascending order adjusted p-values returned function itemselect. different columns correspond identifier item (id), row number item initial data set (irow), adjusted p-value selection step (adjpvalue), code reason fitting failure (cause, equal \"constant.model\" best fit model constant model \"trend..residuals\" best fit model rejected due quadratic trend residuals.) residualtests data frame P-values tests performed residuals, mean trend (resimeantrendP ) variance trend (resivartrendP). first one tests global significant quadratic trend residuals function dose rank-scale (used eliminate unreliable fits) second one global significant quadratic trend residuals absolute value function dose rank-scale (used alert case heteroscedasticity).","code":""},{"path":[]},{"path":"/reference/drcfit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Dose response modelling for responsive items — drcfit","text":"Burnham, KP, Anderson DR (2004). Multimodel inference: understanding AIC BIC model selection. Sociological methods & research, 33(2), 261-304. Hurvich, CM, Tsai, CL (1989). Regression time series model selection small samples. Biometrika, 76(2), 297-307. Larras F, Billoir E, Baillard V, Siberchicot , Scholz S, Wubet T, Tarkka M, Schmitt-Jansen M Delignette-Muller ML (2018). DRomics: turnkey tool support use dose-response framework omics data ecological risk assessment. Environmental science & technology.doi:10.1021/acs.est.8b04752","code":""},{"path":"/reference/drcfit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dose response modelling for responsive items — drcfit","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/drcfit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dose response modelling for responsive items — drcfit","text":"","code":"# (1) a toy example (a very small subsample of a microarray data set) # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package = \"DRomics\") # to test the package on a small (for a quick calculation) but not very small data set # use the following commented line # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package = \"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.05) #> Removing intercept from test coefficients (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 1 dose-response curves out of 21 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 20 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 0 6 6 7 #> log-Gauss-probit #> 1 #> Distribution of the trends (curve shapes) among the 20 fitted dose-response curves: #> #> U bell dec inc #> 4 4 6 6 # Default plot plot(f) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # \\donttest{ # The same plot without log transformation of the doses # (in raw scale of doses) plot(f, dose_log_transfo = FALSE) # The same plot in x log scale choosing x limits for plot if (require(ggplot2)) plot(f, dose_log_transfo = TRUE) + scale_x_log10(limits = c(0.1, 10)) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Scale for x is already present. #> Adding another scale for x, which will replace the existing scale. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # Plot of residuals as function of the dose plot(f, plot.type = \"dose_residuals\") #> Warning: log-10 transformation introduced infinite values. # Same plot of residuals without log transformation of the doses plot(f, plot.type = \"dose_residuals\", dose_log_transfo = FALSE) # plot of residuals as function of the fitted value plot(f, plot.type = \"fitted_residuals\") # (2) an example on a microarray data set (a subsample of a greater data set) # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package = \"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 1000 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" #> [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" #> Data were normalized between arrays using the following method: cyclicloess (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.05)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.05: 318 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | 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|==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 60 dose-response curves out of 318 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 258 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 2 85 64 89 #> log-Gauss-probit #> 18 #> Distribution of the trends (curve shapes) among the 258 fitted dose-response curves: #> #> U bell dec inc #> 55 52 58 93 # Default plot plot(f) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # save all plots to pdf using plotfit2pdf() plotfit2pdf(f, path2figs = tempdir()) #> #> Figures are stored in /tmp/Rtmp2PXivj. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> pdf #> 2 plotfit2pdf(f, plot.type = \"fitted_residuals\", nrowperpage = 9, ncolperpage = 6, path2figs = tempdir()) #> #> Figures are stored in /tmp/Rtmp2PXivj. #> pdf #> 2 # Plot of the fit of the first 12 most responsive items plot(f, items = 12) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # Plot of the chosen items in the chosen order plot(f, items = c(\"301.2\", \"363.1\", \"383.1\")) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # Look at the table of results for successful fits head(f$fitres) #> id irow adjpvalue model nbpar b c d #> 1 384.2 727 2.519524e-07 Gauss-probit 4 8.39021007 6.160174 6.160174 #> 2 383.1 724 6.558388e-07 Gauss-probit 4 3.81611448 10.480252 10.480252 #> 3 383.2 725 8.234946e-07 Gauss-probit 4 6.27817663 8.505693 8.505693 #> 4 384.1 726 2.804671e-06 Gauss-probit 4 8.59581518 5.684089 5.684089 #> 5 301.1 569 6.932747e-06 exponential 3 2.02444957 NA 12.846110 #> 6 363.1 686 7.084800e-06 exponential 3 -0.06029708 NA 9.026630 #> e f SDres typology trend y0 yatdosemax #> 1 1.538640 6.077561 0.1126233 GP.bell bell 12.13639 11.215361 #> 2 1.833579 1.861385 0.1411563 GP.bell bell 12.13871 11.324854 #> 3 1.751051 3.683404 0.1335847 GP.bell bell 12.04858 11.228734 #> 4 1.874867 6.568752 0.1379656 GP.bell bell 12.09844 11.320509 #> 5 -1.404111 NA 0.4905313 E.dec.convex dec 12.84611 10.839662 #> 6 2.064800 NA 0.2526946 E.dec.concave dec 9.02663 7.590655 #> yrange maxychange xextrem yextrem #> 1 1.0223735 0.9210335 1.538640 12.23774 #> 2 1.0167830 0.8138568 1.833579 12.34164 #> 3 0.9603629 0.8198451 1.751051 12.18910 #> 4 0.9323327 0.7779265 1.874867 12.25284 #> 5 2.0064474 2.0064474 NA NA #> 6 1.4359752 1.4359752 NA NA # Look at the table of results for unsuccessful fits head(f$unfitres) #> id irow adjpvalue cause #> 25 368.1 696 5.865601e-05 trend.in.residuals #> 38 367.1 694 2.748644e-04 trend.in.residuals #> 51 360.2 681 3.964071e-04 trend.in.residuals #> 57 162.2 305 4.818750e-04 trend.in.residuals #> 59 161.1 302 5.387711e-04 trend.in.residuals #> 60 275.1 519 5.387711e-04 trend.in.residuals # count the number of unsuccessful fits for each cause table(f$unfitres$cause) #> #> constant.model trend.in.residuals #> 30 30 # (3) Comparison of parallel and non paralell implementations on a larger selection of items # if(!requireNamespace(\"parallel\", quietly = TRUE)) { if(parallel::detectCores() > 1) { s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.05) system.time(f1 <- drcfit(s_quad, progressbar = TRUE)) system.time(f2 <- drcfit(s_quad, progressbar = FALSE, parallel = \"snow\", ncpus = 2)) }} # }"},{"path":"/reference/ecdfplotwithCI.html","id":null,"dir":"Reference","previous_headings":"","what":"ECDF plot of a variable with given confidence intervals on this variable — ecdfplotwithCI","title":"ECDF plot of a variable with given confidence intervals on this variable — ecdfplotwithCI","text":"Provides ECDF plot variable, x-error bars given confidence intervals variable, possibly partitioned groups. context package function intended used BMD variable groups defined user functional annotation.","code":""},{"path":"/reference/ecdfplotwithCI.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ECDF plot of a variable with given confidence intervals on this variable — ecdfplotwithCI","text":"","code":"ecdfplotwithCI(variable, CI.lower, CI.upper, by, CI.col = \"blue\", CI.alpha = 1, add.point = TRUE, point.size = 1, point.type = 16)"},{"path":"/reference/ecdfplotwithCI.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ECDF plot of a variable with given confidence intervals on this variable — ecdfplotwithCI","text":"variable numeric vector variable plot. context package variable may BMD. CI.lower corresponding numeric vector (length) lower bounds confidence intervals. CI.upper corresponding numeric vector (length) upper bounds confidence intervals. factor length split plot factor (split omitted). context package factor may code groups defined user functional annotation. CI.col color draw confidence intervals (unique color) factor coding color. CI.alpha Optional transparency lines used draw confidence intervals. add.point TRUE points added confidence intervals. point.size Size added points case add.point TRUE. point.type Shape added points case add.point TRUE defined integer coding unique common shape factor coding shape.","code":""},{"path":"/reference/ecdfplotwithCI.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ECDF plot of a variable with given confidence intervals on this variable — ecdfplotwithCI","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/ecdfplotwithCI.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"ECDF plot of a variable with given confidence intervals on this variable — ecdfplotwithCI","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/ecdfplotwithCI.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ECDF plot of a variable with given confidence intervals on this variable — ecdfplotwithCI","text":"","code":"# (1) a toy example (a very small subsample of a microarray data set) # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package=\"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.001) #> Removing intercept from test coefficients f <- drcfit(s_quad, progressbar = TRUE) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |======= | 10% | |============== | 20% | |===================== | 30% | |============================ | 40% | |=================================== | 50% | |========================================== | 60% | |================================================= | 70% | |======================================================== | 80% | |=============================================================== | 90% | |======================================================================| 100% r <- bmdcalc(f) set.seed(1) # to get reproducible results with a so small number of iterations b <- bmdboot(r, niter = 5) # with a non reasonable value for niter #> Warning: #> A small number of iterations (less than 1000) may not be sufficient to #> ensure a good quality of bootstrap confidence intervals. #> The bootstrap may be long if the number of items and the number of #> bootstrap iterations is high. #> | | | 0% | |======= | 10% | |============== | 20% | |===================== | 30% | |============================ | 40% | |========================================== | 60% | |================================================= | 70% | |======================================================== | 80% | |=============================================================== | 90% | |======================================================================| 100% # !!!! TO GET CORRECT RESULTS # !!!! niter SHOULD BE FIXED FAR LARGER , e.g. to 1000 # !!!! but the run will be longer # manual ecdf plot of the bootstrap results as an ecdf distribution # on BMD, plot that could also be obtained with plot(b) # in this simple case # a <- b$res[is.finite(b$res$BMD.zSD.upper), ] ecdfplotwithCI(variable = a$BMD.zSD, CI.lower = a$BMD.zSD.lower, CI.upper = a$BMD.zSD.upper, CI.col = \"red\") # \\donttest{ # (2) An example from data published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # This function can also be used to go deeper in the exploration of the biological # meaning of the responses. Here is an example linking the DRomics outputs # with the functional annotation of the responding metabolites of the microalgae # Scenedesmus vacuolatus to the biocide triclosan. # This extra step uses a dataframe previously built by the user which links the items # to the biological information of interest (e.g. KEGG pathways). # importation of a dataframe with metabolomic results # (output $res of bmdcalc() or bmdboot() functions) resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # importation of a dataframe with annotation of each item # identified in the previous file (this dataframe must be previously built by the user) # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe # bootstrap results and annotation annotres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(annotres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport ### an ECDFplot with confidence intervals by pathway # with color coding for dose-response trend ecdfplotwithCI(variable = annotres$BMD.zSD, CI.lower = annotres$BMD.zSD.lower, CI.upper = annotres$BMD.zSD.upper, by = annotres$path_class, CI.col = annotres$trend) # (3) an example on a microarray data set (a subsample of a greater data set) # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 1000 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" #> [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" #> Data were normalized between arrays using the following method: cyclicloess (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.001)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.001: 78 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" (f <- drcfit(s_quad, progressbar = TRUE)) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |= | 1% | |== | 3% | |=== | 4% | |==== | 5% | |==== | 6% | |===== | 8% | |====== | 9% | |======= | 10% | |======== | 12% | |========= | 13% | |========== | 14% | |=========== | 15% | |============ | 17% | |============= | 18% | |============= | 19% | |============== | 21% | |=============== | 22% | |================ | 23% | |================= | 24% | |================== | 26% | |=================== | 27% | |==================== | 28% | |===================== | 29% | |====================== | 31% | |====================== | 32% | |======================= | 33% | |======================== | 35% | |========================= | 36% | |========================== | 37% | |=========================== | 38% | 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|=============================================== | 67% | |================================================ | 68% | |================================================ | 69% | |================================================= | 71% | |================================================== | 72% | |=================================================== | 73% | |==================================================== | 74% | |===================================================== | 76% | |====================================================== | 77% | |======================================================= | 78% | |======================================================== | 79% | |========================================================= | 81% | |========================================================= | 82% | |========================================================== | 83% | |=========================================================== | 85% | |============================================================ | 86% | |============================================================= | 87% | |============================================================== | 88% | |=============================================================== | 90% | |================================================================ | 91% | |================================================================= | 92% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |===================================================================== | 99% | |======================================================================| 100% #> Results of the fitting using the AICc to select the best fit model #> 11 dose-response curves out of 78 previously selected were removed #> because no model could be fitted reliably. #> Distribution of the chosen models among the 67 fitted dose-response curves: #> #> Hill linear exponential Gauss-probit #> 0 11 30 23 #> log-Gauss-probit #> 3 #> Distribution of the trends (curve shapes) among the 67 fitted dose-response curves: #> #> U bell dec inc #> 6 20 22 19 (r <- bmdcalc(f)) #> 1 BMD-xfold values and 0 BMD-zSD values are not defined (coded NaN as #> the BMR stands outside the range of response values defined by the model). #> 28 BMD-xfold values and 0 BMD-zSD values could not be calculated (coded #> NA as the BMR stands within the range of response values defined by the #> model but outside the range of tested doses). (b <- bmdboot(r, niter = 100)) # niter to put at 1000 for a better precision #> Warning: #> A small number of iterations (less than 1000) may not be sufficient to #> ensure a good quality of bootstrap confidence intervals. #> The bootstrap may be long if the number of items and the number of #> bootstrap iterations is high. #> | | | 0% | |= | 1% | |== | 3% | |=== | 4% | |==== | 6% | |===== | 7% | |====== | 9% | |======= | 10% | |======== | 12% | |========= | 13% | |========== | 15% | |=========== | 16% | |============= | 18% | |============== | 19% | |=============== | 21% | |================ | 22% | |================= | 24% | |================== | 25% | |=================== | 27% | |==================== | 28% | |===================== | 30% | |====================== | 31% | |======================= | 33% | |======================== | 34% | |========================= | 36% | |========================== | 37% | |=========================== | 39% | |============================ | 40% | |============================= | 42% | |============================== | 43% | |=============================== | 45% | |================================ | 46% | |================================= | 48% | |================================== | 49% | |==================================== | 51% | |===================================== | 52% | |====================================== | 54% | |======================================= | 55% | |======================================== | 57% | |========================================= | 58% | |=========================================== | 61% | |============================================ | 63% | |============================================= | 64% | |============================================== | 66% | |=============================================== | 67% | |================================================ | 69% | |================================================= | 70% | |================================================== | 72% | |=================================================== | 73% | |==================================================== | 75% | |===================================================== | 76% | |====================================================== | 78% | |========================================================= | 82% | |=========================================================== | 84% | |============================================================ | 85% | |============================================================= | 87% | |============================================================== | 88% | |=============================================================== | 90% | |================================================================ | 91% | |================================================================= | 93% | |=================================================================== | 96% | |==================================================================== | 97% | |===================================================================== | 99% | |======================================================================| 100% #> Bootstrap confidence interval computation failed on 4 items among 67 #> due to lack of convergence of the model fit for a fraction of the #> bootstrapped samples greater than 0.5. #> For 0 BMD.zSD values and 35 BMD.xfold values among 67 at least one #> bound of the 95 percent confidence interval could not be computed due #> to some bootstrapped BMD values not reachable due to model asymptotes #> or reached outside the range of tested doses (bounds coded Inf)). # (3.a) # manual ecdf plot of the bootstrap results as an ecdf distribution # on BMD for each trend # plot that could also be obtained with plot(b, by = \"trend\") # in this simple case # a <- b$res[is.finite(b$res$BMD.zSD.upper), ] ecdfplotwithCI(variable = a$BMD.zSD, CI.lower = a$BMD.zSD.lower, CI.upper = a$BMD.zSD.upper, by = a$trend, CI.col = \"red\") # (3.b) # ecdf plot of the bootstrap results as an ecdf distribution # on BMD for each model # with the color of the confidence intervals coding for the trend # ecdfplotwithCI(variable = a$BMD.zSD, CI.lower = a$BMD.zSD.lower, CI.upper = a$BMD.zSD.upper, by = a$model, CI.col = a$trend) # changing the size of the points and the transparency of CI lines ecdfplotwithCI(variable = a$BMD.zSD, CI.lower = a$BMD.zSD.lower, CI.upper = a$BMD.zSD.upper, by = a$model, CI.col = a$trend, CI.alpha = 0.5, point.size = 0.5) # with the model coding for the type of points ecdfplotwithCI(variable = a$BMD.zSD, CI.lower = a$BMD.zSD.lower, CI.upper = a$BMD.zSD.upper, CI.col = a$trend, CI.alpha = 0.5, point.size = 0.5, point.type = a$model) # (3.c) # ecdf plot of the bootstrap results as an ecdf distribution on # on BMD_L (lower value of the confidence interval) for each trend # ecdfplotwithCI(variable = a$BMD.zSD.lower, CI.lower = a$BMD.zSD.lower, CI.upper = a$BMD.zSD.upper, by = a$model, CI.col = a$trend, add.point = FALSE) # }"},{"path":"/reference/ecdfquantileplot.html","id":null,"dir":"Reference","previous_headings":"","what":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","title":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","text":"Plots given quantile variable calculated group ECDF plot points sized numbers items per group. context package function intended used BMD variable groups defined user functional annotation.","code":""},{"path":"/reference/ecdfquantileplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","text":"","code":"ecdfquantileplot(variable, by, quantile.prob = 0.5, title)"},{"path":"/reference/ecdfquantileplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","text":"variable numeric vector corresponding variable want calculate given quantile group. context package variable may BMD. factor length defining groups. context package factor may code groups defined user functional annotation. quantile.prob probability (]0, 1[) defining quantile calculate group. title optional title plot.","code":""},{"path":"/reference/ecdfquantileplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","text":"given quantile calculated group (e.g.items metabolic pathway) using function quantile plotted ECDF plot. ECDF plot quantiles point sized according number items corresponding group (e.g. metabolic pathway). recommend use new function sensitivityplot may convenient offers options.","code":""},{"path":"/reference/ecdfquantileplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/ecdfquantileplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/ecdfquantileplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ECDF plot of a given quantile of a variable calculated by group — ecdfquantileplot","text":"","code":"# (1) An example from data published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # a dataframe with metabolomic results (output $res of bmdcalc() or bmdboot() functions) resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # a dataframe with annotation of each item identified in the previous file # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe # bootstrap results and annotation annotres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(annotres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport ### an ECDFplot of quantiles of BMD-zSD calculated by pathway ecdfquantileplot(variable = annotres$BMD.zSD, by = annotres$path_class, quantile.prob = 0.25) # same plot in log10 dose scale (not interesting on this example # but could be on another one) if (require(ggplot2)) ecdfquantileplot(variable = annotres$BMD.zSD, by = annotres$path_class, quantile.prob = 0.25) + scale_y_log10()"},{"path":"/reference/formatdata4DRomics.html","id":null,"dir":"Reference","previous_headings":"","what":"Build an R object that can be used as data input in DRomics — formatdata4DRomics","title":"Build an R object that can be used as data input in DRomics — formatdata4DRomics","text":"Build R object can used data input data importation function two inputs: nitems x nsamples matrix coding signal nsamples vector doses","code":""},{"path":"/reference/formatdata4DRomics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build an R object that can be used as data input in DRomics — formatdata4DRomics","text":"","code":"formatdata4DRomics(signalmatrix, dose, samplenames)"},{"path":"/reference/formatdata4DRomics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build an R object that can be used as data input in DRomics — formatdata4DRomics","text":"signalmatrix matrix data one row item one column sample. row names matrix taken identify items. Depending type measured signal, look help corresponding importation function especially check use good scale data RNAseqdata, microarraydata, continuousomicdata continuousanchoringdata. dose numeric vector giving dose sample. samplenames character vector giving names samples (optional argument - given, col names signalmatrix taken sample names).","code":""},{"path":"/reference/formatdata4DRomics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build an R object that can be used as data input in DRomics — formatdata4DRomics","text":"R object corresponds dataframe can passed input first argument data importation functions RNAseqdata, microarraydata, continuousomicdata continuousanchoringdata.","code":""},{"path":[]},{"path":"/reference/formatdata4DRomics.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Build an R object that can be used as data input in DRomics — formatdata4DRomics","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/formatdata4DRomics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Build an R object that can be used as data input in DRomics — formatdata4DRomics","text":"","code":"# (1) load of data # data(zebraf) str(zebraf) #> List of 3 #> $ counts: int [1:1000, 1:16] 453 331 897 12 326 533 1948 904 583 154 ... #> ..- attr(*, \"dimnames\")=List of 2 #> .. ..$ : chr [1:1000] \"ENSDARG00000102141\" \"ENSDARG00000102123\" \"ENSDARG00000114503\" \"ENSDARG00000115971\" ... #> .. ..$ : chr [1:16] \"I10_05mG_E5\" \"I10_05mG_E6\" \"I10_05mG_E7\" \"I10_C5\" ... #> $ dose : num [1:16] 500 500 500 0 0 0 0 50000 50000 50000 ... #> $ batch : Factor w/ 2 levels \"I10\",\"I17\": 1 1 1 1 1 1 1 2 2 2 ... # (2) formating of data for use in DRomics # data4DRomics <- formatdata4DRomics(signalmatrix = zebraf$counts, dose = zebraf$dose) # \\donttest{ # (3) Normalization and transformation of data # o <- RNAseqdata(data4DRomics) #> Just wait, the transformation using regularized logarithm (rlog) may #> take a few minutes. #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. plot(o) # }"},{"path":"/reference/itemselect.html","id":null,"dir":"Reference","previous_headings":"","what":"Selection of significantly responsive items — itemselect","title":"Selection of significantly responsive items — itemselect","text":"Significantly responsive items selected using one three proposed methods: quadratic trend test, linear trend test ANOVA-based test.","code":""},{"path":"/reference/itemselect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selection of significantly responsive items — itemselect","text":"","code":"itemselect(omicdata, select.method = c(\"quadratic\", \"linear\", \"ANOVA\"), FDR = 0.05, max.ties.prop = 0.2) # S3 method for class 'itemselect' print(x, nfirstitems = 20, ...)"},{"path":"/reference/itemselect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Selection of significantly responsive items — itemselect","text":"omicdata object class \"microarraydata\", \"RNAseqdata\", \"metabolomicdata\" \"continuousanchoringdata\" respectively returned functions microarraydata, RNAseqdata, metabolomicdata continuousanchoringdata. select.method \"quadratic\" quadratic trend test dose ranks, \"linear\" linear trend test dose ranks \"ANOVA\" ANOVA-type test (see details explaination). FDR threshold term FDR (False Discovery Rate) selecting responsive items. max.ties.prop maximal tolerated proportion tied values item, item selected (must ]0, 0.5], default fixed 0.2 - see details description filtering step). x object class \"itemselect\". nfirstitems maximum number selected items print. ... arguments passed print function.","code":""},{"path":"/reference/itemselect.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Selection of significantly responsive items — itemselect","text":"selection responsive items performed using limma package microarray continuous omics data (metabolomics), DESeq2 package RNAseq data lm function continuous anchoring data. Three methods proposed (described ). Within limma methods implemented using functions lmFit, eBayes topTable p-values ajusted multiple testing using Benjamini-Hochberg method (also called q-values), false discovery rate given input (argument FDR). Within DESeq2 methods implemented using functions DESeqDataSetFromMatrix, DESeq results p-values ajusted multiple testing using Benjamini-Hochberg method (also called q-values), false discovery rate given input (argument FDR). continuous anchoring data, lm anova functions used fit model compare null model, pvalues corrected using function p.adjust Benjamini-Hochberg method. ANOVA_based test (\"ANOVA\") classically used selection omics data general case requires many replicates per dose efficient, thus really suited dose-response design. linear trend test (\"linear\") aims detecting monotonic trends dose-response designs, whatever number replicates per dose. proposed Tukey (1985), tests global significance linear model describing response function dose rank-scale. quadratic trend test (\"quadratic\") tests global significance quadratic model describing response function dose rank-scale. variant linear trend method aims detecting monotonic non monotonic trends dose-response designs, whatever number replicates per dose (default chosen method). use one previously described tests, filter based proportion tied values also performed whatever type data, assuming tied values correspond minimal common value non detections imputed. items proportion tied minimal values input argument max.ties.prop eliminated selection.","code":""},{"path":"/reference/itemselect.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Selection of significantly responsive items — itemselect","text":"itemselect returns object class \"itemselect\", list 5 components: adjpvalue vector p-values adjusted Benjamini-Hochberg method (also called q-values) selected items (adjpvalue inferior FDR) sorted ascending order selectindex corresponding vector row indices selected items object omicdata omicdata corresponding object class \"microarraydata\", \"RNAseqdata\", \"continuousomicdata\" \"continuousanchoringdata\" given input. select.method selection method given input. FDR threshold term FDR given input. print \"itemselect\" object gives number selected items identifiers 20 responsive items.","code":""},{"path":[]},{"path":"/reference/itemselect.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Selection of significantly responsive items — itemselect","text":"Tukey JW, Ciminera JL Heyse JF (1985), Testing statistical certainty response increasing doses drug. Biometrics, 295-301. Ritchie , Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth, GK (2015), limma powers differential expression analyses RNA-sequencing microarray studies. Nucleic Acids Research 43, e47. Love MI, Huber W, Anders S (2014), Moderated estimation fold change dispersion RNA-seq data DESeq2. Genome biology, 15(12), 550.","code":""},{"path":"/reference/itemselect.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Selection of significantly responsive items — itemselect","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/itemselect.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Selection of significantly responsive items — itemselect","text":"","code":"# (1) an example on a microarray data set (a subsample of a greater data set) # datafilename <- system.file(\"extdata\", \"transcripto_sample.txt\", package=\"DRomics\") (o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\")) #> Just wait, the normalization using cyclicloess may take a few minutes. #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 1000 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"5.2\" \"6.1\" \"6.2\" \"7.1\" \"7.2\" #> [11] \"8.1\" \"8.2\" \"9.1\" \"9.2\" \"10.1\" \"10.2\" \"11.1\" \"11.2\" \"12.1\" \"12.2\" #> Data were normalized between arrays using the following method: cyclicloess # 1.a using the quadratic trend test # (s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.05)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.05: 318 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" print(s_quad, nfirstitems = 30) #> Number of selected items using a quadratic trend test with an FDR of 0.05: 318 #> Identifiers of the first 30 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" \"351.2\" \"12.1\" \"7.2\" \"239.2\" \"368.1\" \"263.2\" \"264.1\" #> [28] \"138.1\" \"233.2\" \"334.1\" # to get the names of all the selected items (selecteditems <- s_quad$omicdata$item[s_quad$selectindex]) #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" \"351.2\" \"12.1\" \"7.2\" \"239.2\" \"368.1\" \"263.2\" \"264.1\" #> [28] \"138.1\" \"233.2\" \"334.1\" \"353.1\" \"359\" \"136.1\" \"167.1\" \"138.2\" \"27.2\" #> [37] \"312.1\" \"367.1\" \"264.2\" \"334.2\" \"168.2\" \"247.2\" \"336\" \"168.1\" \"25.2\" #> [46] \"7.1\" \"136.2\" \"167.2\" \"233.1\" \"353.2\" \"360.2\" \"13.2\" \"12.2\" \"88.1\" #> [55] \"263.1\" \"352.2\" \"162.2\" \"70\" \"161.1\" \"275.1\" \"358.2\" \"88.2\" \"320.1\" #> [64] \"352.1\" \"311.1\" \"27.1\" \"4\" \"358.1\" \"42.2\" \"55.2\" \"92\" \"249.2\" #> [73] \"360.1\" \"103\" \"162.1\" \"320.2\" \"198.1\" \"229.2\" \"371.2\" \"225\" \"321.2\" #> [82] \"329.1\" \"438.2\" \"229.1\" \"25.1\" \"348\" \"228.1\" \"247.1\" \"371.1\" \"512.1\" #> [91] \"81\" \"13.1\" \"148\" \"311.2\" \"113.2\" \"83.2\" \"268.2\" \"268.1\" \"84.1\" #> [100] \"321.1\" \"467.1\" \"228.2\" \"113.1\" \"489.1\" \"330.2\" \"249.1\" \"83.1\" \"439.2\" #> [109] \"369.2\" \"330.1\" \"295.1\" \"294.1\" \"295.2\" \"490.2\" \"117.2\" \"337\" \"267.2\" #> [118] \"274.1\" \"367.2\" \"468.1\" \"116.1\" \"368.2\" \"401.2\" \"116.2\" \"137\" \"341.1\" #> [127] \"195.2\" \"490.1\" \"467.2\" \"404.2\" \"446.1\" \"329.2\" \"85.2\" \"118.1\" \"118.2\" #> [136] \"126\" \"267.1\" \"115\" \"132.1\" \"401.1\" \"402.1\" \"449.1\" \"404.1\" \"199.2\" #> [145] \"312.2\" \"84.2\" \"74.1\" \"275.2\" \"446.2\" \"373.1\" \"449.2\" \"465.2\" \"117.1\" #> [154] \"294.2\" \"74.2\" \"194.2\" \"195.1\" \"40.2\" \"497.2\" \"169.2\" \"5.2\" \"194.1\" #> [163] \"372.2\" \"274.2\" \"410.1\" \"169.1\" \"170\" \"171.1\" \"202.2\" \"207.1\" \"373.2\" #> [172] \"402.2\" \"496.1\" \"372.1\" \"298.2\" \"67.1\" \"144.1\" \"154.1\" \"199.1\" \"369.1\" #> [181] \"396.2\" \"232.1\" \"177.2\" \"41.2\" \"85.1\" \"159\" \"171.2\" \"245.2\" \"396.1\" #> [190] \"87.2\" \"299.1\" \"496.2\" \"161.2\" \"266.1\" \"512.2\" \"464.1\" \"356.1\" \"397.1\" #> [199] \"8.1\" \"39.2\" \"464.2\" \"61.2\" \"385.1\" \"131.2\" \"176.1\" \"497.1\" \"245.1\" #> [208] \"528.1\" \"131.1\" \"465.1\" \"54.1\" \"204.2\" \"451.1\" \"54.2\" \"441.1\" \"260.1\" #> [217] \"299.2\" \"397.2\" \"400.2\" \"436\" \"177.1\" \"232.2\" \"386.2\" \"260.2\" \"256.1\" #> [226] \"87.1\" \"265.1\" \"204.1\" \"258.2\" \"67.2\" \"198.2\" \"439.1\" \"40.1\" \"206.2\" #> [235] \"66.1\" \"385.2\" \"356.2\" \"293.2\" \"527.1\" \"258.1\" \"129.1\" \"68.1\" \"191.1\" #> [244] \"511.1\" \"286.1\" \"75.2\" \"128.2\" \"428.1\" \"122.1\" \"144.2\" \"282.1\" \"523.2\" #> [253] \"377.2\" \"386.1\" \"128.1\" \"440.1\" \"523.1\" \"461.2\" \"293.1\" \"342.2\" \"433.1\" #> [262] \"68.2\" \"86.2\" \"451.2\" \"298.1\" \"291.1\" \"342.1\" \"43.1\" \"499.1\" \"333.2\" #> [271] \"227.1\" \"121.1\" \"208.2\" \"412.2\" \"438.1\" \"282.2\" \"376.2\" \"75.1\" \"155.1\" #> [280] \"526.2\" \"291.2\" \"482.2\" \"129.2\" \"526.1\" \"121.2\" \"440.2\" \"515.2\" \"65.2\" #> [289] \"341.2\" \"122.2\" \"461.1\" \"224.1\" \"419.1\" \"482.1\" \"466.1\" \"524.2\" \"524.1\" #> [298] \"112.1\" \"41.1\" \"284.1\" \"187.1\" \"513.2\" \"110.1\" \"101.2\" \"419.2\" \"210.1\" #> [307] \"205.1\" \"498.2\" \"382.1\" \"133.2\" \"390.1\" \"243.1\" \"452.1\" \"110.2\" \"513.1\" #> [316] \"468.2\" \"511.2\" \"65.1\" # \\donttest{ # 1.b using the linear trend test # (s_lin <- itemselect(o, select.method = \"linear\", FDR = 0.05)) #> Removing intercept from test coefficients #> Number of selected items using a linear trend test with an FDR of 0.05: 90 #> Identifiers of the first 20 most responsive items: #> [1] \"300.2\" \"301.1\" \"239.1\" \"300.1\" \"240.1\" \"301.2\" \"240.2\" \"239.2\" \"364.2\" #> [10] \"363.1\" \"364.1\" \"136.1\" \"363.2\" \"27.2\" \"138.1\" \"336\" \"233.2\" \"25.2\" #> [19] \"27.1\" \"136.2\" # 1.c using the ANOVA-based test # (s_ANOVA <- itemselect(o, select.method = \"ANOVA\", FDR = 0.05)) #> Removing intercept from test coefficients #> Number of selected items using an ANOVA type test with an FDR of 0.05: 203 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"363.2\" \"367.1\" \"383.1\" \"383.2\" \"363.1\" \"364.1\" \"364.2\" \"384.1\" #> [10] \"368.1\" \"300.2\" \"351.1\" \"301.1\" \"320.1\" \"350.2\" \"300.1\" \"351.2\" \"353.1\" #> [19] \"353.2\" \"350.1\" # 1.d using the quadratic trend test with a smaller false discovery rate # (s_quad.2 <- itemselect(o, select.method = \"quadratic\", FDR = 0.001)) #> Removing intercept from test coefficients #> Number of selected items using a quadratic trend test with an FDR of 0.001: 78 #> Identifiers of the first 20 most responsive items: #> [1] \"384.2\" \"383.1\" \"383.2\" \"384.1\" \"301.1\" \"363.1\" \"300.2\" \"364.2\" \"364.1\" #> [10] \"363.2\" \"301.2\" \"300.1\" \"351.1\" \"350.2\" \"239.1\" \"240.1\" \"240.2\" \"370\" #> [19] \"15\" \"350.1\" # }"},{"path":"/reference/metabolomicdata.html","id":null,"dir":"Reference","previous_headings":"","what":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","title":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","text":"Metabolomic continuous omics data imported .txt file (internally imported using function read.table) checked R object class data.frame (see description argument file required format data). normalization transformation provided function. pretreatment continuous omic data data must done importation data, data must imported log scale needed (imperative example metabolomic data), can directly modelled using normal error model. strong hypothesis required selection items dose-reponse modelling. example, basic procedure pre-treatment metabolomic data follow three steps described thereafter: ) removing metabolites proportion missing data (non detections) across samples high (20 50 percents according tolerance level); ii) retrieving missing values data using half minimum method (.e. half minimum value found metabolite across samples); iii) log-transformation values. scaling total intensity (normalization sum signals sample) another normalization necessary pertinent, recommend three previously decribed steps.","code":""},{"path":"/reference/metabolomicdata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","text":"","code":"continuousomicdata(file, backgrounddose, check = TRUE) metabolomicdata(file, backgrounddose, check = TRUE) # S3 method for class 'continuousomicdata' print(x, ...) # S3 method for class 'continuousomicdata' plot(x, range4boxplot = 0, ...)"},{"path":"/reference/metabolomicdata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","text":"file name .txt file (e.g. \"mydata.txt\") containing one row per item, first column corresponding identifier item, columns giving responses item replicate dose concentration. first line, name identifier column, must tested doses concentrations numeric format corresponding replicate (example, triplicates treatment, first line \"item\", 0, 0, 0, 0.1, 0.1, 0.1, etc.). file imported within function using function read.table default field separator (sep argument)default decimal separator (dec argument \".\"). Alternatively R object class data.frame can directly given input, corresponding output read.table(file, header = FALSE) file described . two alternatives illustrated examples. backgrounddose argument must used dose zero data, prevent calculation BMD extrapolation. doses equal value given backgrounddose fixed 0, considered background level exposition. check TRUE format input file checked. x object class \"continuousomicdata\". range4boxplot argument passed boxplot(), fixed default 0 prevent producing large plot files due many outliers. Can put 1.5 obtain classical boxplots. ... arguments passed print plot functions.","code":""},{"path":"/reference/metabolomicdata.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","text":"function imports data, checks format (see description argument file required format data) gives print information help user check coding data correct : tested doses (concentrations), number replicates dose, number items identifiers first 20 items. metabolomicdata() first name gave function. renamed continuousomicdata (keeping first name available) offer use continuous omic data proteomics data RT-QPCR data. Nevertheless one take care scale data imported DRomics. transformation may needed enable use normal error model step DRomics workflow (selection items modelling BMD calculation)","code":""},{"path":"/reference/metabolomicdata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","text":"continuousomicdata() returns object class \"continuousomicdata\", list 7 components: data numeric matrix responses item replicate (one line per item, one column per replicate) dose numeric vector tested doses concentrations corresponding column data item character vector identifiers items, corresponding line data design table experimental design (tested doses number replicates dose) control user data.mean numeric matrix mean responses item per dose (mean corresponding replicates) (one line per item, one column per unique value dose data.sd numeric matrix standard deviations response item per dose (sd corresponding replicates, NA replicate) (one line per item, one column per unique value dose) containsNA TRUE data set contains NA values print continuousomicdata object gives tested doses (concentrations) number replicates dose, number items, identifiers first 20 items (check good coding data) normalization method. plot continuousomicdata object shows data distribution dose concentration replicate.","code":""},{"path":[]},{"path":"/reference/metabolomicdata.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/metabolomicdata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import and check of continuous omic data (e.g. metabolomic data) — continuousomicdata","text":"","code":"# (1) import and check of metabolomic data # (an example on a subsample of a greater data set given in the package (see ?Scenedesmus)) # datafilename <- system.file(\"extdata\", \"metabolo_sample.txt\", package = \"DRomics\") o <- continuousomicdata(datafilename) #> Warning: #> We recommend you to check that your omic data were correctly pretreated #> before importation. In particular data (e.g. metabolomic signal) should #> have been log-transformed, without replacing 0 values by NA values #> (consider using the half minimum method instead for example). print(o) #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.1 1.79 2.92 4.78 7.76 #> 10 6 2 2 2 6 2 #> Number of items: 109 #> Identifiers of the first 20 items: #> #> [1] \"P_2\" \"P_4\" \"P_5\" \"P_6\" \"P_7\" \"P_10\" \"P_11\" \"P_12\" \"P_14\" \"P_16\" #> [11] \"P_19\" \"P_21\" \"P_22\" \"P_26\" \"P_32\" \"P_34\" \"P_35\" \"P_36\" \"P_37\" \"P_38\" plot(o) PCAdataplot(o) # if you want to skip the check of data o <- continuousomicdata(datafilename, check = FALSE) # If you want to use your own data set just replace datafilename, # the first argument of metabolomicdata(), # by the name of your data file (e.g. \"mydata.txt\") # # You should take care that the field separator of this data file is one # of the default field separators recognised by the read.table() function # when it is used with its default field separator (sep argument) # Tabs are recommended. # Use of an R object of class data.frame # An example using the complete data set # Scenedesmus_metab (see ?Scenedesmus for details) data(Scenedesmus_metab) (o <- continuousomicdata(Scenedesmus_metab)) #> Warning: #> We recommend you to check that your omic data were correctly pretreated #> before importation. In particular data (e.g. metabolomic signal) should #> have been log-transformed, without replacing 0 values by NA values #> (consider using the half minimum method instead for example). #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.1 1.79 2.92 4.78 7.76 #> 6 3 3 3 3 3 3 #> Number of items: 224 #> Identifiers of the first 20 items: #> #> [1] \"NAP_1\" \"NAP_2\" \"NAP_3\" \"NAP_4\" \"NAP_5\" \"NAP_6\" \"NAP_7\" \"NAP_8\" #> [9] \"NAP_9\" \"NAP_11\" \"NAP_13\" \"NAP_14\" \"NAP_15\" \"NAP_16\" \"NAP_17\" \"NAP_18\" #> [17] \"NAP_19\" \"NAP_20\" \"NAP_21\" \"NAP_22\" plot(o)"},{"path":"/reference/microarraydata.html","id":null,"dir":"Reference","previous_headings":"","what":"Import, check and normalization of single-channel microarray data — microarraydata","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"Single-channel microarray data log2 imported .txt file (internally imported using function read.table), checked R object class data.frame (see description argument file required format data)normalized (arrays normalization). omicdata deprecated version microarraydata.","code":""},{"path":"/reference/microarraydata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"","code":"microarraydata(file, backgrounddose, check = TRUE, norm.method = c(\"cyclicloess\", \"quantile\", \"scale\", \"none\")) omicdata(file, backgrounddose, check = TRUE, norm.method = c(\"cyclicloess\", \"quantile\", \"scale\", \"none\")) # S3 method for class 'microarraydata' print(x, ...) # S3 method for class 'microarraydata' plot(x, range4boxplot = 0, ...)"},{"path":"/reference/microarraydata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"file name .txt file (e.g. \"mydata.txt\") containing one row per item, first column corresponding identifier item, columns giving responses item replicate dose concentration. first line, name identifier column, must tested doses concentrations numeric format corresponding replicate (example, triplicates treatment, first line \"item\", 0, 0, 0, 0.1, 0.1, 0.1, etc.). file imported within function using function read.table default field separator (sep argument) default decimal separator (dec argument \".\"). Alternatively R object class data.frame can directly given input, corresponding output read.table(file, header = FALSE) file described . backgrounddose argument must used dose zero data, prevent calculation BMD extrapolation. doses equal value given backgrounddose fixed 0, considered background level exposition. check TRUE format input file checked. norm.method \"none\" normalization performed, else normalization performed using function normalizeBetweenArrays limma package using specified method. x object class \"microarraydata\". range4boxplot argument passed boxplot(), fixed default 0 prevent producing large plot files due many outliers. Can put 1.5 obtain classical boxplots. ... arguments passed print plot functions.","code":""},{"path":"/reference/microarraydata.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"function imports data, checks format (see description argument file required format data) gives print information help user check coding data correct : tested doses (concentrations) number replicates dose, number items, identifiers first 20 items. argument norm.method \"none\", data normalized using function normalizeBetweenArrays limma package using specified method : \"cyclicloess\" (default choice), \"quantile\" \"scale\".","code":""},{"path":"/reference/microarraydata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"microarraydata returns object class \"microarraydata\", list 9 components: data numeric matrix normalized responses item replicate (one line per item, one column per replicate) dose numeric vector tested doses concentrations corresponding column data item character vector identifiers items, corresponding line data design table experimental design (tested doses number replicates dose) control user data.mean numeric matrix mean responses item per dose (mean corresponding replicates) (one line per item, one column per unique value dose data.sd numeric matrix standard deviations response item per dose (sd corresponding replicates, NA replicate) (one line per item, one column per unique value dose) norm.method normalization method specified input data.beforenorm numeric matrix responses item replicate (one line per item, one column per replicate) normalization containsNA always FALSE microarray data allowed contain NA values print microarraydata object gives tested doses (concentrations) number replicates dose, number items, identifiers first 20 items (check good coding data) normalization method. plot microarraydata object shows data distribution dose concentration replicate normalization.","code":""},{"path":[]},{"path":"/reference/microarraydata.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"Ritchie , Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth, GK (2015), limma powers differential expression analyses RNA-sequencing microarray studies. Nucleic Acids Research 43, e47.","code":""},{"path":"/reference/microarraydata.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/microarraydata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import, check and normalization of single-channel microarray data — microarraydata","text":"","code":"# (1) import, check and normalization of microarray data # (an example on a subsample of a greater data set published in Larras et al. 2018 # Transcriptomic effect of triclosan in the chlorophyte Scenedesmus vacuolatus) datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package=\"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. print(o) #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 100 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"6.1\" \"7.1\" \"8.1\" \"9.1\" \"10.1\" #> [11] \"11.1\" \"12.1\" \"13.1\" \"14.1\" \"15\" \"16.1\" \"17.1\" \"18.1\" \"19.1\" \"20.1\" #> Data were normalized between arrays using the following method: cyclicloess plot(o) PCAdataplot(o) PCAdataplot(o, label = TRUE) # If you want to use your own data set just replace datafilename, # the first argument of microarraydata(), # by the name of your data file (e.g. \"mydata.txt\") # # You should take care that the field separator of this data file is one # of the default field separators recognised by the read.table() function # when it is used with its default field separator (sep argument) # Tabs are recommended. # \\donttest{ # (2) normalization with other methods (o.2 <- microarraydata(datafilename, check = TRUE, norm.method = \"quantile\")) #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 100 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"6.1\" \"7.1\" \"8.1\" \"9.1\" \"10.1\" #> [11] \"11.1\" \"12.1\" \"13.1\" \"14.1\" \"15\" \"16.1\" \"17.1\" \"18.1\" \"19.1\" \"20.1\" #> Data were normalized between arrays using the following method: quantile plot(o.2) (o.3 <- microarraydata(datafilename, check = TRUE, norm.method = \"scale\")) #> Elements of the experimental design in order to check the coding of the data: #> Tested doses and number of replicates for each dose: #> #> 0 0.69 1.223 2.148 3.774 6.631 #> 5 5 5 5 5 5 #> Number of items: 100 #> Identifiers of the first 20 items: #> [1] \"1\" \"2\" \"3\" \"4\" \"5.1\" \"6.1\" \"7.1\" \"8.1\" \"9.1\" \"10.1\" #> [11] \"11.1\" \"12.1\" \"13.1\" \"14.1\" \"15\" \"16.1\" \"17.1\" \"18.1\" \"19.1\" \"20.1\" #> Data were normalized between arrays using the following method: scale plot(o.3) # }"},{"path":"/reference/selectgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Selection of groups on which to focus — selectgroups","title":"Selection of groups on which to focus — selectgroups","text":"Selection groups (e.g. corresponding different biological annotations) focus, based sensitivity (BMD summary value) representativeness (number items group).","code":""},{"path":"/reference/selectgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selection of groups on which to focus — selectgroups","text":"","code":"selectgroups(extendedres, group, explev, BMDmax, BMDtype = c(\"zSD\", \"xfold\"), BMDsummary = c(\"first.quartile\", \"median\" ), nitemsmin = 3, keepallexplev = FALSE)"},{"path":"/reference/selectgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Selection of groups on which to focus — selectgroups","text":"extendedres dataframe results provided drcfit (fitres) bmdcalc (res) subset data frame (selected lines). dataframe extended additional columns coming group (example biological annotation items) optionnally another experimental level (example molecular level), lines can replicated corresponding item one annotation. group name column extendedres coding groups. explev optional argument naming column extendedres coding experimental level. BMDmax maximum BMD summary value used limit groups sensitive (optional input : missing selection based BMD). BMDtype type BMD used selection BMD, \"zSD\" (default choice) \"xfold\". BMDsummary type summary used selection based BMD, \"first.quartile\" (default choice first quartile BMD values per group) \"median\" (choice median BMD values per group). nitemsmin minimum number items per group limit groups represented (can put 1 want select number: recommended. keepallexplev TRUE (default value FALSE), group selected least one experimental level, kept selection experimental levels.","code":""},{"path":"/reference/selectgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Selection of groups on which to focus — selectgroups","text":"function provide subset input extendedres corresponding groups number items representing group greater equal nitemsmin BMDmax secified, BMD summary value less equal BMDmax. one experimental level (explev specified), selection groups made separately experimental level: group may selected one experimental level removed another one. function eliminates rows NA values chosen BMD (BMDtype) performing selection.","code":""},{"path":"/reference/selectgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Selection of groups on which to focus — selectgroups","text":"dataframe corresponding subset extendedres given input, can used exploration using example bmdplot, bmdplotwithgradient, trendplot sensitivityplot.","code":""},{"path":[]},{"path":"/reference/selectgroups.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Selection of groups on which to focus — selectgroups","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/selectgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Selection of groups on which to focus — selectgroups","text":"","code":"# (1) # An example from the paper published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # the dataframe with metabolomic results resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # the dataframe with annotation of each item identified in the previous file # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe extendedres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(extendedres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport # (1) Sensitivity by pathway # (1.a) before selection sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\") # (1.b) after selection on representativeness extendedres.b <- selectgroups(extendedres, group = \"path_class\", nitemsmin = 10) sensitivityplot(extendedres.b, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\") # \\donttest{ # (1.c) after selection on sensitivity extendedres.c <- selectgroups(extendedres, group = \"path_class\", BMDmax = 1.25, BMDtype = \"zSD\", BMDsummary = \"first.quartile\", nitemsmin = 1) sensitivityplot(extendedres.c, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\") # (1.d) after selection on representativeness and sensitivity extendedres.d <- selectgroups(extendedres, group = \"path_class\", BMDmax = 1.25, BMDtype = \"zSD\", BMDsummary = \"first.quartile\", nitemsmin = 10) sensitivityplot(extendedres.d, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\") # (2) # An example with two molecular levels # ### Rename metabolomic results metabextendedres <- extendedres # Import the dataframe with transcriptomic results contigresfilename <- system.file(\"extdata\", \"triclosanSVcontigres.txt\", package = \"DRomics\") contigres <- read.table(contigresfilename, header = TRUE, stringsAsFactors = TRUE) str(contigres) #> 'data.frame':\t447 obs. of 27 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... # Import the dataframe with functional annotation (or any other descriptor/category # you want to use, here KEGG pathway classes) contigannotfilename <- system.file(\"extdata\", \"triclosanSVcontigannot.txt\", package = \"DRomics\") contigannot <- read.table(contigannotfilename, header = TRUE, stringsAsFactors = TRUE) str(contigannot) #> 'data.frame':\t562 obs. of 2 variables: #> $ contig : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ path_class: Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... # Merging of both previous dataframes contigextendedres <- merge(x = contigres, y = contigannot, by.x = \"id\", by.y = \"contig\") # to see the structure of this dataframe str(contigextendedres) #> 'data.frame':\t562 obs. of 28 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... #> $ path_class : Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... ### Merge metabolomic and transcriptomic results extendedres <- rbind(metabextendedres, contigextendedres) extendedres$molecular.level <- factor(c(rep(\"metabolites\", nrow(metabextendedres)), rep(\"contigs\", nrow(contigextendedres)))) str(extendedres) #> 'data.frame':\t646 obs. of 29 variables: #> $ id : Factor w/ 478 levels \"NAP47_51\",\"NAP_2\",..: 1 2 3 4 4 4 4 5 6 7 ... #> $ irow : int 46 2 21 28 28 28 28 34 38 47 ... #> $ adjpvalue : num 7.16e-04 6.23e-05 1.11e-05 1.03e-05 1.03e-05 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 3 3 3 3 3 2 2 4 ... #> $ nbpar : int 2 3 2 2 2 2 2 3 3 5 ... #> $ b : num -0.056 0.4598 -0.0595 -0.0451 -0.0451 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 7.34 5.94 5.39 7.86 7.86 ... #> $ e : num NA -1.65 NA NA NA ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.1245 0.126 0.0793 0.052 0.052 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 7 7 7 7 7 2 2 9 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 3 3 3 3 1 ... #> $ y0 : num 7.34 5.94 5.39 7.86 7.86 ... #> $ yrange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ maxychange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 2.224 0.528 1.333 1.154 1.154 ... #> $ BMR.zSD : num 7.22 5.82 5.31 7.81 7.81 ... #> $ BMD.xfold : num NA NA NA NA NA ... #> $ BMR.xfold : num 6.61 5.35 4.85 7.07 7.07 ... #> $ BMD.zSD.lower : num 0.979 0.2 0.853 0.752 0.752 ... #> $ BMD.zSD.upper : num 4.07 1.11 1.75 1.46 1.46 ... #> $ BMD.xfold.lower : num Inf Inf 7.61 Inf Inf ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 1000 957 1000 1000 1000 1000 1000 648 620 872 ... #> $ path_class : Factor w/ 18 levels \"Amino acid metabolism\",..: 5 5 3 3 2 6 8 5 5 5 ... #> $ molecular.level : Factor w/ 2 levels \"contigs\",\"metabolites\": 2 2 2 2 2 2 2 2 2 2 ... # optional inverse alphabetic ordering of groups for the plot extendedres$path_class <- factor(extendedres$path_class, levels = sort(levels(extendedres$path_class), decreasing = TRUE)) ### (2.1) sensitivity plot of both molecular levels before and after selection of # most sensitive groups sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", colorby = \"molecular.level\", BMDsummary = \"first.quartile\") extendedres.2 <- selectgroups(extendedres, group = \"path_class\", explev = \"molecular.level\", BMDmax = 1, BMDtype = \"zSD\", BMDsummary = \"first.quartile\", nitemsmin = 1) sensitivityplot(extendedres.2, BMDtype = \"zSD\", group = \"path_class\", , colorby = \"molecular.level\", BMDsummary = \"first.quartile\") ### (2.2) same selection but keeping all the experimental as soon # as the selection criterion is met for at least one experimental level extendedres.3 <- selectgroups(extendedres, group = \"path_class\", explev = \"molecular.level\", BMDmax = 1, BMDtype = \"zSD\", BMDsummary = \"first.quartile\", nitemsmin = 1, keepallexplev = TRUE) extendedres.2 #> id irow adjpvalue model nbpar b c #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.0560055901 NA #> 2 NAP_2 2 6.232579e-05 exponential 3 0.4598124176 NA #> 5 NAP_30 28 1.028343e-05 linear 2 -0.0450783225 NA #> 6 NAP_30 28 1.028343e-05 linear 2 -0.0450783225 NA #> 7 NAP_30 28 1.028343e-05 linear 2 -0.0450783225 NA #> 8 NAP_38 34 1.885047e-03 exponential 3 0.6010677009 NA #> 9 NAP_42 38 4.160193e-03 exponential 3 0.6721022679 NA #> 10 NAP_52 47 3.920169e-02 log-Gauss-probit 5 0.4500858981 7.202003 #> 11 NAP_54 49 3.767103e-04 exponential 3 0.4520654041 NA #> 12 NAP_56 51 1.489919e-03 exponential 3 0.4392508278 NA #> 13 NAP_58 53 2.834198e-02 linear 2 -0.0193812708 NA #> 14 NAP_73 67 3.767103e-04 linear 2 -0.0578784221 NA #> 16 NP_121 197 9.889460e-03 linear 2 0.0614947494 NA #> 17 NP_121 197 9.889460e-03 linear 2 0.0614947494 NA #> 18 NP_129 204 7.286216e-03 exponential 3 0.0077860286 NA #> 19 NP_129 204 7.286216e-03 exponential 3 0.0077860286 NA #> 20 NP_129 204 7.286216e-03 exponential 3 0.0077860286 NA #> 22 NP_140 214 8.550044e-03 Gauss-probit 4 0.6955280524 4.835774 #> 23 NP_140 214 8.550044e-03 Gauss-probit 4 0.6955280524 4.835774 #> 25 NP_147 221 1.061967e-03 exponential 3 -0.0319538831 NA #> 26 NP_33 113 5.599308e-02 log-Gauss-probit 4 0.6150169995 4.917345 #> 27 NP_33 113 5.599308e-02 log-Gauss-probit 4 0.6150169995 4.917345 #> 28 NP_33 113 5.599308e-02 log-Gauss-probit 4 0.6150169995 4.917345 #> 29 NP_33 113 5.599308e-02 log-Gauss-probit 4 0.6150169995 4.917345 #> 30 NP_33 113 5.599308e-02 log-Gauss-probit 4 0.6150169995 4.917345 #> 31 NP_35 115 3.238559e-03 Gauss-probit 4 1.5075787740 5.975143 #> 32 NP_35 115 3.238559e-03 Gauss-probit 4 1.5075787740 5.975143 #> 33 NP_35 115 3.238559e-03 Gauss-probit 4 1.5075787740 5.975143 #> 34 NP_35 115 3.238559e-03 Gauss-probit 4 1.5075787740 5.975143 #> 35 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 36 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 37 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 39 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 40 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 41 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 42 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 43 NP_43 123 1.055895e-02 exponential 3 -0.3967359528 NA #> 44 NP_55 135 1.119380e-06 Gauss-probit 4 2.3252734893 4.469882 #> 46 NP_56 136 5.289310e-03 exponential 3 -0.0043605275 NA #> 47 NP_56 136 5.289310e-03 exponential 3 -0.0043605275 NA #> 48 NP_59 139 1.293440e-02 exponential 3 -0.3667815344 NA #> 49 NP_59 139 1.293440e-02 exponential 3 -0.3667815344 NA #> 50 NP_59 139 1.293440e-02 exponential 3 -0.3667815344 NA #> 51 NP_59 139 1.293440e-02 exponential 3 -0.3667815344 NA #> 52 NP_60 140 6.939560e-03 exponential 3 -0.5150005401 NA #> 53 NP_60 140 6.939560e-03 exponential 3 -0.5150005401 NA #> 54 NP_60 140 6.939560e-03 exponential 3 -0.5150005401 NA #> 55 NP_60 140 6.939560e-03 exponential 3 -0.5150005401 NA #> 56 NP_60 140 6.939560e-03 exponential 3 -0.5150005401 NA #> 57 NP_60 140 6.939560e-03 exponential 3 -0.5150005401 NA #> 58 NP_68 147 3.449660e-04 Gauss-probit 4 2.4449502970 5.055577 #> 59 NP_69 148 1.156302e-03 linear 2 -0.0507632397 NA #> 60 NP_69 148 1.156302e-03 linear 2 -0.0507632397 NA #> 61 NP_74 153 7.029527e-02 log-Gauss-probit 5 0.2715410382 4.847403 #> 62 NP_74 153 7.029527e-02 log-Gauss-probit 5 0.2715410382 4.847403 #> 63 NP_74 153 7.029527e-02 log-Gauss-probit 5 0.2715410382 4.847403 #> 64 NP_74 153 7.029527e-02 log-Gauss-probit 5 0.2715410382 4.847403 #> 65 NP_74 153 7.029527e-02 log-Gauss-probit 5 0.2715410382 4.847403 #> 67 NP_90 168 3.072572e-02 exponential 3 -0.2686241513 NA #> 68 NP_90 168 3.072572e-02 exponential 3 -0.2686241513 NA #> 69 NP_90 168 3.072572e-02 exponential 3 -0.2686241513 NA #> 70 NP_90 168 3.072572e-02 exponential 3 -0.2686241513 NA #> 71 NP_92 170 2.278810e-03 exponential 3 -0.0045956887 NA #> 72 NP_92 170 2.278810e-03 exponential 3 -0.0045956887 NA #> 73 NP_92 170 2.278810e-03 exponential 3 -0.0045956887 NA #> 75 NP_94 172 1.798752e-05 Gauss-probit 4 3.0165756601 4.862842 #> 76 NP_94 172 1.798752e-05 Gauss-probit 4 3.0165756601 4.862842 #> 77 NP_94 172 1.798752e-05 Gauss-probit 4 3.0165756601 4.862842 #> 79 NP_96 174 5.128859e-04 Gauss-probit 4 2.4099064659 5.216540 #> 80 NP_96 174 5.128859e-04 Gauss-probit 4 2.4099064659 5.216540 #> 81 NP_98 176 1.544866e-04 Gauss-probit 4 1.8803246284 5.908317 #> 82 NP_98 176 1.544866e-04 Gauss-probit 4 1.8803246284 5.908317 #> 83 NP_98 176 1.544866e-04 Gauss-probit 4 1.8803246284 5.908317 #> 84 NP_98 176 1.544866e-04 Gauss-probit 4 1.8803246284 5.908317 #> 86 c00276 39331 9.401685e-07 exponential 3 1.4994358863 NA #> 87 c00281 41217 2.894669e-06 exponential 3 1.4081722202 NA #> 103 c00973 8280 6.803372e-03 exponential 3 -0.7727846601 NA #> 116 c01318 18587 9.761656e-03 exponential 3 -0.0254133606 NA #> 121 c01442 22830 8.398538e-07 exponential 3 -0.4594973812 NA #> 123 c01449 23118 3.283441e-05 Gauss-probit 4 4.4613014130 3.681397 #> 125 c01613 28185 5.354723e-03 linear 2 0.0799120939 NA #> 131 c01645 29447 4.923790e-08 Gauss-probit 4 2.7016585216 3.159101 #> 134 c01924 38335 2.843216e-04 Gauss-probit 4 2.4465374667 11.894336 #> 150 c02837 53881 7.971049e-04 linear 2 0.1575210301 NA #> 157 c02964 54187 3.684145e-04 exponential 3 -1.6864714649 NA #> 161 c03088 54664 3.084821e-03 Gauss-probit 4 2.1488658006 11.596818 #> 169 c03232 55298 2.548714e-06 linear 2 0.2143540812 NA #> 172 c03284 55434 1.365366e-03 linear 2 -0.3672796068 NA #> 178 c03358 55610 1.361311e-03 Gauss-probit 4 2.7322361908 10.205586 #> 180 c03440 55810 2.679169e-03 linear 2 -0.1138782478 NA #> 181 c03440 55810 2.679169e-03 linear 2 -0.1138782478 NA #> 183 c03526 56019 2.223268e-04 exponential 3 -1.2492729382 NA #> 184 c03540 56053 3.430925e-03 linear 2 0.1965028128 NA #> 186 c03544 56063 6.386500e-03 exponential 3 -0.0014175944 NA #> 188 c03571 56127 9.413418e-03 log-Gauss-probit 4 0.5983299493 4.168006 #> 193 c03661 56344 7.587641e-03 log-Gauss-probit 4 0.6923539914 3.041593 #> 196 c03724 56499 4.297972e-05 log-Gauss-probit 4 0.5840913112 12.278610 #> 198 c03760 56585 9.777594e-03 linear 2 -0.1351427617 NA #> 202 c03784 56645 5.006652e-03 log-Gauss-probit 4 0.6898227977 9.654095 #> 228 c04342 58826 1.308910e-04 Gauss-probit 4 7.1974259415 7.723356 #> 233 c04434 59217 7.874407e-03 Gauss-probit 5 3.9082976255 2.896934 #> 235 c04513 59551 9.612303e-03 log-Gauss-probit 4 0.7506662658 11.849601 #> 238 c04553 59682 2.052935e-04 exponential 3 -0.0065668916 NA #> 243 c04613 59817 2.581335e-03 Gauss-probit 4 3.0320225840 11.908364 #> 245 c04619 59830 8.483282e-03 exponential 3 0.0232807480 NA #> 246 c04619 59830 8.483282e-03 exponential 3 0.0232807480 NA #> 252 c04647 59892 1.704160e-03 linear 2 0.0934490851 NA #> 253 c04655 59910 4.758216e-05 log-Gauss-probit 4 0.7297683681 11.552853 #> 256 c04683 59973 5.823021e-04 exponential 3 0.8026136129 NA #> 266 c04883 60416 7.479393e-03 exponential 3 1.7011616772 NA #> 275 c05081 60856 1.998628e-04 exponential 3 0.0440643594 NA #> 276 c05100 60898 4.685588e-05 exponential 3 1.6262517332 NA #> 291 c05326 247 4.812259e-03 linear 2 -0.1628213963 NA #> 293 c05358 382 7.443184e-03 exponential 3 0.0209061611 NA #> 301 c05581 1323 4.187799e-03 linear 2 0.1169616348 NA #> 305 c05641 1567 2.764636e-03 exponential 3 -0.0008701043 NA #> 306 c05645 1576 7.113588e-04 linear 2 -0.1348525089 NA #> 312 c05903 2148 4.374165e-03 log-Gauss-probit 4 0.7532079225 12.168583 #> 327 c06059 2495 1.623735e-03 exponential 3 0.9866188485 NA #> 338 c06208 2970 8.742721e-04 linear 2 0.1440100625 NA #> 341 c06313 3413 6.508671e-04 Gauss-probit 4 2.5239792304 6.359865 #> 344 c06518 3887 4.434225e-04 exponential 3 1.2826794141 NA #> 346 c06548 3976 1.118557e-04 linear 2 0.2267279513 NA #> 354 c06762 4630 1.129124e-03 exponential 3 0.0257922735 NA #> 361 c06881 4900 9.898720e-04 exponential 3 0.0003893310 NA #> 364 c06943 5037 4.856986e-04 linear 2 0.1205146652 NA #> 370 c07072 5328 9.285630e-04 exponential 3 1.7509076084 NA #> 372 c07118 5428 1.069214e-06 Gauss-probit 4 3.2010176397 13.816221 #> 375 c07206 5768 1.331320e-04 exponential 3 0.0115070482 NA #> 389 c07529 7138 7.515694e-04 exponential 3 0.0281112604 NA #> 399 c07859 8092 1.007072e-03 Gauss-probit 4 2.1459634733 7.693951 #> 405 c08131 8701 1.673911e-03 linear 2 0.1826551851 NA #> 408 c08241 8948 1.777483e-04 Gauss-probit 4 4.3060858426 12.750947 #> 419 c08466 9451 1.593456e-04 exponential 3 0.0008887870 NA #> 430 c08762 10368 1.641885e-03 exponential 3 0.0030933767 NA #> 445 c09125 11677 1.569172e-03 exponential 3 0.8284745591 NA #> 458 c09562 12942 4.373374e-03 exponential 3 -0.0021611489 NA #> 460 c09598 13030 2.362131e-03 linear 2 -0.2586441199 NA #> 475 c10057 14495 3.390469e-03 exponential 3 0.0241537891 NA #> 476 c10066 14533 1.347692e-03 exponential 3 -0.9389331704 NA #> 488 c10238 15302 1.228501e-03 exponential 3 -1.7937748737 NA #> 490 c10269 15440 7.296658e-05 exponential 3 1.5924749054 NA #> 492 c10302 15585 4.225116e-03 exponential 3 0.0066174702 NA #> 494 c10311 15626 1.016928e-03 log-Gauss-probit 4 0.7845650593 3.521660 #> 499 c10413 16075 3.498590e-04 linear 2 0.1135548342 NA #> 501 c10499 16461 1.256216e-07 Gauss-probit 4 2.0625479766 11.831280 #> 513 c10934 17597 5.753046e-03 exponential 3 0.0078914200 NA #> 519 c11233 18523 4.606893e-03 exponential 3 0.0193438159 NA #> 534 c11906 20969 9.654346e-04 Gauss-probit 4 2.2924767928 11.794843 #> 540 c12260 22081 8.245828e-06 exponential 3 0.1353700658 NA #> 549 c12576 23487 9.267247e-03 log-Gauss-probit 4 1.0213517059 4.443206 #> 551 c12705 23819 9.933717e-03 exponential 3 -1.7515673870 NA #> 552 c12781 24007 1.047802e-03 linear 2 0.4825734514 NA #> 553 c12927 24362 7.217499e-04 linear 2 0.3109087577 NA #> 554 c13186 25095 5.598806e-03 exponential 3 -0.0003355018 NA #> 567 c13542 26650 7.852988e-04 exponential 3 0.0026006455 NA #> 571 c13596 26891 2.014638e-03 Gauss-probit 4 2.0943452876 2.661827 #> 577 c13825 27530 7.036682e-03 Gauss-probit 4 2.3751722909 6.820887 #> 579 c14005 27954 6.155794e-03 linear 2 0.4216192438 NA #> 586 c14431 29501 2.619248e-03 log-Gauss-probit 4 0.7955319244 5.947486 #> 587 c14431 29501 2.619248e-03 log-Gauss-probit 4 0.7955319244 5.947486 #> 593 c15572 33143 7.075275e-03 exponential 3 0.0464367552 NA #> 597 c15942 34598 4.718990e-05 exponential 3 -0.0445067510 NA #> 604 c16973 37787 2.825599e-04 Gauss-probit 4 8.3191178064 -4.668448 #> 608 c17497 39284 3.028115e-04 exponential 3 -0.0023128307 NA #> 610 c17517 39327 2.461490e-03 exponential 3 0.0005997141 NA #> 611 c17694 39843 2.019227e-05 exponential 3 -1.8263643423 NA #> 614 c17823 40393 6.238172e-04 exponential 3 0.0006779690 NA #> 620 c18315 42046 3.162528e-03 exponential 3 0.0037074842 NA #> 630 c19738 46332 6.573236e-03 exponential 3 -0.0002344367 NA #> 641 c21327 51498 7.255831e-06 exponential 3 1.4896819388 NA #> 645 c21452 51752 7.810285e-07 exponential 3 1.4272338376 NA #> d e f SDres typology trend y0 #> 1 7.343571 NA NA 0.12454183 L.dec dec 7.343571 #> 2 5.941896 -1.6479584 NA 0.12604568 E.dec.convex dec 5.941896 #> 5 7.859109 NA NA 0.05203245 L.dec dec 7.859109 #> 6 7.859109 NA NA 0.05203245 L.dec dec 7.859109 #> 7 7.859109 NA NA 0.05203245 L.dec dec 7.859109 #> 8 6.857909 -0.3213163 NA 0.23376392 E.dec.convex dec 6.857909 #> 9 6.209286 -0.3230281 NA 0.28968463 E.dec.convex dec 6.209286 #> 10 7.288883 1.3087220 -0.1436781 0.07085857 lGP.U U 7.288883 #> 11 6.868523 -0.6254549 NA 0.15031166 E.dec.convex dec 6.868523 #> 12 7.558481 -0.2649798 NA 0.15353807 E.dec.convex dec 7.558481 #> 13 6.467466 NA NA 0.05769085 L.dec dec 6.467466 #> 14 5.738302 NA NA 0.11726837 L.dec dec 5.738302 #> 16 5.330171 NA NA 0.18706049 L.inc inc 5.330171 #> 17 5.330171 NA NA 0.18706049 L.inc inc 5.330171 #> 18 4.968475 2.0243885 NA 0.14303183 E.inc.convex inc 4.968475 #> 19 4.968475 2.0243885 NA 0.14303183 E.inc.convex inc 4.968475 #> 20 4.968475 2.0243885 NA 0.14303183 E.inc.convex inc 4.968475 #> 22 4.835774 1.4069463 0.2455556 0.10615565 GP.bell bell 4.867514 #> 23 4.835774 1.4069463 0.2455556 0.10615565 GP.bell bell 4.867514 #> 25 5.402328 3.3895616 NA 0.06744390 E.dec.concave dec 5.402328 #> 26 4.917345 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 27 4.917345 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 28 4.917345 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 29 4.917345 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 30 4.917345 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 31 5.975143 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 32 5.975143 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 33 5.975143 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 34 5.975143 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 35 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 36 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 37 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 39 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 40 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 41 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 42 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 43 5.607846 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 44 4.469882 1.8678530 0.8450245 0.12256991 GP.bell bell 5.081883 #> 46 7.018764 1.9509846 NA 0.05587260 E.dec.concave dec 7.018764 #> 47 7.018764 1.9509846 NA 0.05587260 E.dec.concave dec 7.018764 #> 48 4.491695 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 49 4.491695 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 50 4.491695 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 51 4.491695 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 52 5.215709 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 53 5.215709 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 54 5.215709 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 55 5.215709 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 56 5.215709 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 57 5.215709 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 58 5.055577 2.6269663 -0.6208164 0.12476292 GP.U U 4.707014 #> 59 5.513855 NA NA 0.11224716 L.dec dec 5.513855 #> 60 5.513855 NA NA 0.11224716 L.dec dec 5.513855 #> 61 4.689787 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 62 4.689787 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 63 4.689787 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 64 4.689787 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 65 4.689787 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 67 6.070523 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 68 6.070523 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 69 6.070523 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 70 6.070523 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 71 6.489151 1.5921459 NA 0.17066885 E.dec.concave dec 6.489151 #> 72 6.489151 1.5921459 NA 0.17066885 E.dec.concave dec 6.489151 #> 73 6.489151 1.5921459 NA 0.17066885 E.dec.concave dec 6.489151 #> 75 4.862842 1.3892912 0.7365937 0.14844069 GP.bell bell 5.525316 #> 76 4.862842 1.3892912 0.7365937 0.14844069 GP.bell bell 5.525316 #> 77 4.862842 1.3892912 0.7365937 0.14844069 GP.bell bell 5.525316 #> 79 5.216540 1.9279453 0.8281597 0.22659347 GP.bell bell 5.817903 #> 80 5.216540 1.9279453 0.8281597 0.22659347 GP.bell bell 5.817903 #> 81 5.908317 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 82 5.908317 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 83 5.908317 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 84 5.908317 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 86 12.428212 -2.1982296 NA 0.28684892 E.dec.convex dec 12.428212 #> 87 12.411870 -2.4052289 NA 0.28115971 E.dec.convex dec 12.411870 #> 103 7.236270 -2.3752014 NA 0.30192346 E.inc.concave inc 7.236270 #> 116 9.300591 1.9823323 NA 0.31949144 E.dec.concave dec 9.300591 #> 121 13.082606 5.0536325 NA 0.21425367 E.dec.concave dec 13.082606 #> 123 3.681397 2.5679456 7.7390108 0.55445078 GP.bell bell 10.238924 #> 125 10.764018 NA NA 0.23146795 L.inc inc 10.764018 #> 131 3.159101 1.7419187 3.3237027 0.41149782 GP.bell bell 5.859022 #> 134 11.894336 2.4191926 -1.3218368 0.24812481 GP.U U 11.083641 #> 150 5.662624 NA NA 0.32569343 L.inc inc 5.662624 #> 157 8.068158 -1.5870424 NA 0.57855035 E.inc.concave inc 8.068158 #> 161 11.596818 1.8275480 -0.8682280 0.29736729 GP.U U 10.992074 #> 169 8.329304 NA NA 0.31094519 L.inc inc 8.329304 #> 172 9.111936 NA NA 0.84138808 L.dec dec 9.111936 #> 178 10.205586 2.8555099 2.3244195 0.41255116 GP.bell bell 11.551851 #> 180 13.959092 NA NA 0.26918435 L.dec dec 13.959092 #> 181 13.959092 NA NA 0.26918435 L.dec dec 13.959092 #> 183 6.736177 -0.6498650 NA 0.38185313 E.inc.concave inc 6.736177 #> 184 6.408554 NA NA 0.52767310 L.inc inc 6.408554 #> 186 8.901037 1.0237053 NA 0.37429011 E.dec.concave dec 8.901037 #> 188 4.168006 2.3167014 1.3407136 0.64489184 lGP.bell bell 4.168006 #> 193 3.041593 1.8809478 1.7942520 0.87754767 lGP.bell bell 3.041593 #> 196 12.278610 1.7372724 -2.0216456 0.53171016 lGP.U U 12.278610 #> 198 10.350443 NA NA 0.39026969 L.dec dec 10.350443 #> 202 9.654095 1.9560222 -2.1370159 1.14939830 lGP.U U 9.654095 #> 228 7.723356 2.5397977 5.2069816 0.18596550 GP.bell bell 12.616033 #> 233 -2.630433 0.0000000 3.7693124 0.45467691 GP.bell bell 3.902563 #> 235 11.849601 1.9585039 0.8349853 0.42763624 lGP.bell bell 11.849601 #> 238 15.751380 1.2089428 NA 0.43153316 E.dec.concave dec 15.751380 #> 243 11.908364 2.7394740 1.2475547 0.17108221 GP.bell bell 12.737821 #> 245 8.309831 1.7832021 NA 0.47444618 E.inc.convex inc 8.309831 #> 246 8.309831 1.7832021 NA 0.47444618 E.inc.convex inc 8.309831 #> 252 17.926717 NA NA 0.19130163 L.inc inc 17.926717 #> 253 11.552853 1.7586717 0.9857557 0.27811862 lGP.bell bell 11.552853 #> 256 11.273898 -2.4815554 NA 0.24110024 E.dec.convex dec 11.273898 #> 266 5.055152 -0.5889109 NA 0.72440497 E.dec.convex dec 5.055152 #> 275 11.097343 2.0754574 NA 0.27234802 E.inc.convex inc 11.097343 #> 276 6.963048 -2.0722462 NA 0.44005180 E.dec.convex dec 6.963048 #> 291 4.628540 NA NA 0.44374643 L.dec dec 4.628540 #> 293 9.824353 1.9682607 NA 0.21543421 E.inc.convex inc 9.824353 #> 301 12.582904 NA NA 0.28459726 L.inc inc 12.582904 #> 305 12.978673 0.9062525 NA 0.48389149 E.dec.concave dec 12.978673 #> 306 12.920552 NA NA 0.29030980 L.dec dec 12.920552 #> 312 12.168583 2.0515078 0.7474307 0.31596860 lGP.bell bell 12.168583 #> 327 4.946831 -0.7537935 NA 0.38646039 E.dec.convex dec 4.946831 #> 338 10.397791 NA NA 0.31334720 L.inc inc 10.397791 #> 341 6.359865 2.1273579 -4.1079331 1.11545022 GP.U U 3.480083 #> 344 10.429183 -2.6796044 NA 0.37877170 E.dec.convex dec 10.429183 #> 346 9.672700 NA NA 0.45436857 L.inc inc 9.672700 #> 354 5.609934 1.8216559 NA 0.32521096 E.inc.convex inc 5.609934 #> 361 9.377940 0.8420820 NA 0.38873834 E.inc.convex inc 9.377940 #> 364 10.014326 NA NA 0.27962037 L.inc inc 10.014326 #> 370 6.118400 -2.3714972 NA 0.59801495 E.dec.convex dec 6.118400 #> 372 13.816221 1.9596785 -3.2185534 0.39523662 GP.U U 11.147673 #> 375 11.805578 1.3537714 NA 0.50658959 E.inc.convex inc 11.805578 #> 389 5.114821 1.4084038 NA 1.09855421 E.inc.convex inc 5.114821 #> 399 7.693951 1.5471002 0.6661478 0.19149575 GP.bell bell 8.207650 #> 405 4.872178 NA NA 0.43574679 L.inc inc 4.872178 #> 408 12.750947 2.2781947 -2.6699053 0.24834756 GP.U U 10.429737 #> 419 8.322289 0.8805994 NA 0.48194682 E.inc.convex inc 8.322289 #> 430 7.540803 1.2173378 NA 0.27550558 E.inc.convex inc 7.540803 #> 445 12.928749 -1.3776799 NA 0.29718879 E.dec.convex dec 12.928749 #> 458 9.916022 1.0820381 NA 0.43570777 E.dec.concave dec 9.916022 #> 460 6.195518 NA NA 0.64907510 L.dec dec 6.195518 #> 475 12.738419 1.6671166 NA 0.51466168 E.inc.convex inc 12.738419 #> 476 7.627804 -2.0128076 NA 0.32054437 E.inc.concave inc 7.627804 #> 488 4.475119 -1.3700499 NA 0.65855748 E.inc.concave inc 4.475119 #> 490 8.307995 -2.3133165 NA 0.42499412 E.dec.convex dec 8.307995 #> 492 10.373727 1.3213539 NA 0.46734668 E.inc.convex inc 10.373727 #> 494 3.521660 1.5925756 1.6525193 0.71149572 lGP.bell bell 3.521660 #> 499 8.667552 NA NA 0.23785120 L.inc inc 8.667552 #> 501 11.831280 2.0127634 -2.1716042 0.35429372 GP.U U 10.482349 #> 513 2.856807 1.2403251 NA 0.72920067 E.inc.convex inc 2.856807 #> 519 11.872955 1.6928649 NA 0.36955986 E.inc.convex inc 11.872955 #> 534 11.794843 1.8803822 -1.7229567 0.53837776 GP.U U 10.564068 #> 540 4.828430 2.0830008 NA 0.73289707 E.inc.convex inc 4.828430 #> 549 4.443206 1.5956017 -2.0801672 1.01087060 lGP.U U 4.443206 #> 551 3.922000 -1.4167347 NA 0.80658748 E.inc.concave inc 3.922000 #> 552 2.983728 NA NA 1.23243335 L.inc inc 2.983728 #> 553 3.293896 NA NA 0.78506590 L.inc inc 3.293896 #> 554 4.782034 0.7862608 NA 0.62138156 E.dec.concave dec 4.782034 #> 567 4.100345 0.9665863 NA 0.83033880 E.inc.convex inc 4.100345 #> 571 2.661827 1.8081921 1.5558322 0.59126123 GP.bell bell 3.733593 #> 577 6.820887 2.2045847 -1.9822962 0.65642859 GP.U U 5.532363 #> 579 2.353068 NA NA 1.29727813 L.inc inc 2.353068 #> 586 5.947486 2.1464196 -1.2818324 0.48811443 lGP.U U 5.947486 #> 587 5.947486 2.1464196 -1.2818324 0.48811443 lGP.U U 5.947486 #> 593 2.709461 1.8595387 NA 0.73835019 E.inc.convex inc 2.709461 #> 597 5.266716 1.7742492 NA 0.45397783 E.dec.concave dec 5.266716 #> 604 -4.668448 2.7306565 14.1086676 0.29870979 GP.bell bell 8.700290 #> 608 7.176469 1.0238480 NA 0.42350852 E.dec.concave dec 7.176469 #> 610 2.783684 0.8429907 NA 0.60237813 E.inc.convex inc 2.783684 #> 611 8.797682 -1.8935670 NA 0.47334309 E.inc.concave inc 8.797682 #> 614 9.580443 0.9456406 NA 0.19945603 E.inc.convex inc 9.580443 #> 620 2.604185 1.0531930 NA 0.75747748 E.inc.convex inc 2.604185 #> 630 8.262534 0.7222172 NA 0.96123411 E.dec.concave dec 8.262534 #> 641 13.349535 -1.8926198 NA 0.34219661 E.dec.convex dec 13.349535 #> 645 12.344658 -2.4650317 NA 0.25729560 E.dec.convex dec 12.344658 #> yrange maxychange xextrem yextrem BMD.zSD BMR.zSD BMD.xfold #> 1 0.4346034 0.4346034 NA NA 2.2237393 7.219029 NA #> 2 0.4556672 0.4556672 NA NA 0.5279668 5.815850 NA #> 5 0.3498078 0.3498078 NA NA 1.1542677 7.807077 NA #> 6 0.3498078 0.3498078 NA NA 1.1542677 7.807077 NA #> 7 0.3498078 0.3498078 NA NA 1.1542677 7.807077 NA #> 8 0.6010677 0.6010677 NA NA 0.1582542 6.624146 NA #> 9 0.6721023 0.6721023 NA NA 0.1821546 5.919602 0.8318574 #> 10 0.1912790 0.1912790 1.4588204 7.097604 0.7315304 7.218025 NA #> 11 0.4520636 0.4520636 NA NA 0.2528186 6.718211 NA #> 12 0.4392508 0.4392508 NA NA 0.1139635 7.404943 NA #> 13 0.1503987 0.1503987 NA NA 2.9766289 6.409775 NA #> 14 0.4491366 0.4491366 NA NA 2.0261156 5.621033 NA #> 16 0.4771993 0.4771993 NA NA 3.0418937 5.517232 NA #> 17 0.4771993 0.4771993 NA NA 3.0418937 5.517232 NA #> 18 0.3520280 0.3520280 NA NA 5.9997652 5.111507 NA #> 19 0.3520280 0.3520280 NA NA 5.9997652 5.111507 NA #> 20 0.3520280 0.3520280 NA NA 5.9997652 5.111507 NA #> 22 0.2455556 0.2138158 1.4069463 5.081330 0.6597819 4.973670 NA #> 23 0.2455556 0.2138158 1.4069463 5.081330 0.6597819 4.973670 NA #> 25 0.2833937 0.2833937 NA NA 3.8465968 5.334884 NA #> 26 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 27 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 28 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 29 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 30 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 31 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 32 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 33 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 34 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 35 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 36 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 37 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 39 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 40 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 41 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 42 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 43 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 44 0.8109383 0.5779147 1.8678530 5.314907 0.6370853 5.204453 6.6295891 #> 46 0.2284144 0.2284144 NA NA 5.1225625 6.962891 NA #> 47 0.2284144 0.2284144 NA NA 5.1225625 6.962891 NA #> 48 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 49 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 50 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 51 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 52 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 53 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 54 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 55 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 56 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 57 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 58 0.5522909 0.2800374 2.6269663 4.434761 0.8261462 4.582251 NA #> 59 0.3939227 0.3939227 NA NA 2.2111898 5.401608 NA #> 60 0.3939227 0.3939227 NA NA 2.2111898 5.401608 NA #> 61 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 62 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 63 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 64 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 65 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 67 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 68 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 69 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 70 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 71 0.5966741 0.5966741 NA NA 5.7972864 6.318483 NA #> 72 0.5966741 0.5966741 NA NA 5.7972864 6.318483 NA #> 73 0.5966741 0.5966741 NA NA 5.7972864 6.318483 NA #> 75 0.6573939 0.5832748 1.3892912 5.599435 3.9480418 5.376875 7.2728900 #> 76 0.6573939 0.5832748 1.3892912 5.599435 3.9480418 5.376875 7.2728900 #> 77 0.6573939 0.5832748 1.3892912 5.599435 3.9480418 5.376875 7.2728900 #> 79 0.7838623 0.5570660 1.9279453 6.044699 1.8746151 6.044497 NA #> 80 0.7838623 0.5570660 1.9279453 6.044699 1.8746151 6.044497 NA #> 81 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 82 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 83 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 84 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 86 1.4260064 1.4260064 NA NA 0.4667565 12.141363 3.8804658 #> 87 1.3187707 1.3187707 NA NA 0.5356979 12.130711 5.1282913 #> 103 0.7254029 0.7254029 NA NA 1.1767628 7.538193 6.5436013 #> 116 0.6953599 0.6953599 NA NA 5.1699097 8.981100 NA #> 121 1.2471032 1.2471032 NA NA 1.9341638 12.868353 NA #> 123 2.6271853 1.4457016 2.5679456 11.420407 0.7339797 10.793374 1.6629870 #> 125 0.5298971 0.5298971 NA NA 2.8965322 10.995486 NA #> 131 2.6773137 2.0535315 1.7419187 6.482804 0.7603447 6.270519 1.3328628 #> 134 1.0214920 0.5111417 2.4191926 10.572500 0.7894809 10.835516 NA #> 150 1.0445220 1.0445220 NA NA 2.0676187 5.988317 3.5948366 #> 157 1.6606248 1.6606248 NA NA 0.6668007 8.646709 1.0329475 #> 161 0.7968439 0.5333591 1.8275480 10.728590 4.9242758 11.289442 NA #> 169 1.4213819 1.4213819 NA NA 1.4506148 8.640249 3.8857687 #> 172 2.4354311 2.4354311 NA NA 2.2908652 8.270548 2.4809261 #> 178 1.4297124 0.9781543 2.8555099 12.530005 0.8151722 11.964402 NA #> 180 0.7551267 0.7551267 NA NA 2.3637907 13.689907 NA #> 181 0.7551267 0.7551267 NA NA 2.3637907 13.689907 NA #> 183 1.2492267 1.2492267 NA NA 0.2370668 7.118030 0.5035207 #> 184 1.3030102 1.3030102 NA NA 2.6853208 6.936227 3.2613039 #> 186 0.9204590 0.9204590 NA NA 5.7121222 8.526747 6.5967242 #> 188 1.3407136 1.3407136 2.3167014 5.508719 1.1232833 4.812898 0.9282272 #> 193 1.7942520 1.7942520 1.8809478 4.835845 0.8218053 3.919141 0.5103535 #> 196 2.0216456 2.0216456 1.7372724 10.256965 0.6687799 11.746900 0.9695193 #> 198 0.8961317 0.8961317 NA NA 2.8878327 9.960173 NA #> 202 2.1370159 2.1370159 1.9560222 7.517079 0.9072409 8.504697 0.8197933 #> 228 0.7767741 0.4624698 2.5397977 12.930338 0.9318105 12.801999 NA #> 233 0.9872534 0.6272225 2.2864089 4.529785 1.0599593 4.357240 0.8573274 #> 235 0.8349853 0.8349853 1.9585039 12.684587 0.8218319 12.277238 NA #> 238 1.5763081 1.5763081 NA NA 5.0780515 15.319847 6.6301060 #> 243 0.7000964 0.4180970 2.7394740 13.155918 0.7253125 12.908904 NA #> 245 0.9360331 0.9360331 NA NA 5.4609223 8.784277 6.4241875 #> 246 0.9360331 0.9360331 NA NA 5.4609223 8.784277 6.4241875 #> 252 0.6196609 0.6196609 NA NA 2.0471215 18.118018 NA #> 253 0.9857557 0.9857557 1.7586717 12.538608 0.5508093 11.830971 NA #> 256 0.7471482 0.7471482 NA NA 0.8865055 11.032798 NA #> 266 1.7011398 1.7011398 NA NA 0.3267449 4.330747 0.2076643 #> 275 1.0315095 1.0315095 NA NA 4.0915465 11.369691 NA #> 276 1.5599561 1.5599561 NA NA 0.6538412 6.522996 1.1581900 #> 291 1.0796687 1.0796687 NA NA 2.7253570 4.184793 2.8427097 #> 293 0.5863849 0.5863849 NA NA 4.7734831 10.039788 NA #> 301 0.7755726 0.7755726 NA NA 2.4332531 12.867502 NA #> 305 1.3091568 1.3091568 NA NA 5.7300529 12.494781 6.6231563 #> 306 0.8942070 0.8942070 NA NA 2.1527950 12.630242 NA #> 312 0.7474307 0.7474307 2.0515078 12.916014 0.7635263 12.484552 NA #> 327 0.9864697 0.9864697 NA NA 0.3747033 4.560371 0.5245918 #> 338 0.9549307 0.9549307 NA NA 2.1758702 10.711139 NA #> 341 3.2718424 2.0436913 2.1273579 2.251932 1.5320368 2.364633 0.3746104 #> 344 1.1746841 1.1746841 NA NA 0.9378058 10.050411 4.4938667 #> 346 1.5034330 1.5034330 NA NA 2.0040254 10.127068 4.2662140 #> 354 0.9567759 0.9567759 NA NA 4.7558343 5.935145 5.6919180 #> 361 1.0233370 1.0233370 NA NA 5.8164566 9.766679 6.5575207 #> 364 0.7991327 0.7991327 NA NA 2.3202186 10.293947 NA #> 370 1.6440213 1.6440213 NA NA 0.9909542 5.520385 1.0195641 #> 372 2.1088211 1.5588160 1.9596785 10.597668 0.9547622 10.752436 5.8228192 #> 375 1.5309438 1.5309438 NA NA 5.1540813 12.312167 6.2821526 #> 389 3.0879871 3.0879871 NA NA 5.1982036 6.213375 4.1613342 #> 399 0.6258898 0.4734413 1.5471002 8.360099 4.1335653 8.016154 NA #> 405 1.2111865 1.2111865 NA NA 2.3856251 5.307925 2.6674183 #> 408 1.0680948 0.7193997 2.2781947 10.081042 1.0862987 10.181390 NA #> 419 1.6551785 1.6551785 NA NA 5.5456395 8.804236 6.0260053 #> 430 0.7148534 0.7148534 NA NA 5.4786411 7.816309 NA #> 445 0.8217456 0.8217456 NA NA 0.6120840 12.631560 NA #> 458 0.9890126 0.9890126 NA NA 5.7470070 9.480314 NA #> 460 1.7150692 1.7150692 NA NA 2.5095297 5.546443 2.3953832 #> 475 1.2652916 1.2652916 NA NA 5.1762835 13.253081 NA #> 476 0.9041080 0.9041080 NA NA 0.8406026 7.948348 3.3682176 #> 488 1.7795910 1.7795910 NA NA 0.6267952 5.133676 0.3931901 #> 490 1.5018619 1.5018619 NA NA 0.7181485 7.883001 1.7061269 #> 492 0.9936782 0.9936782 NA NA 5.6440554 10.841074 NA #> 494 1.6525193 1.6525193 1.5925756 5.174180 0.5751369 4.233156 0.4008074 #> 499 0.7529821 0.7529821 NA NA 2.0945933 8.905403 NA #> 501 1.9945506 1.1718772 2.0127634 9.659675 0.5750563 10.128055 6.1142017 #> 513 1.6477686 1.6477686 NA NA 5.6272766 3.586008 4.4854464 #> 519 0.9527003 0.9527003 NA NA 5.0802181 12.242515 NA #> 534 1.5216846 1.0295037 1.8803822 10.071887 4.9758852 11.102445 NA #> 540 3.1308885 3.1308885 NA NA 3.8712302 5.561327 3.1637050 #> 549 2.0801672 2.0801672 1.5956017 2.363039 0.4677758 3.432336 0.2651867 #> 551 1.7353224 1.7353224 NA NA 0.8742707 4.728587 0.3591303 #> 552 3.1999446 3.1999446 NA NA 2.5538772 4.216161 0.6182950 #> 553 2.0616360 2.0616360 NA NA 2.5250685 4.078962 1.0594414 #> 554 1.5426292 1.5426292 NA NA 5.9163079 4.160652 5.7105061 #> 567 2.4773277 2.4773277 NA NA 5.5764311 4.930684 4.8975036 #> 571 1.4460633 0.9619972 1.8081921 4.217660 5.0186649 3.142332 1.0034930 #> 577 1.6331559 0.9393843 2.2045847 4.838591 1.7413609 4.875934 1.2937236 #> 579 2.7957572 2.7957572 NA NA 3.0768950 3.650346 0.5581026 #> 586 1.2818324 1.2818324 2.1464196 4.665653 0.7105856 5.459371 0.8008469 #> 587 1.2818324 1.2818324 2.1464196 4.665653 0.7105856 5.459371 0.8008469 #> 593 1.5961529 1.5961529 NA NA 5.2575129 3.447811 3.5740658 #> 597 1.8241896 1.8241896 NA NA 4.2864651 4.812738 4.5279917 #> 604 1.4684527 0.7399295 2.7306565 9.440219 0.6335929 8.998999 NA #> 608 1.5003885 1.5003885 NA NA 5.3399279 6.752960 5.8776276 #> 610 1.5629901 1.5629901 NA NA 5.8277486 3.386062 5.1779858 #> 611 1.7713152 1.7713152 NA NA 0.5680459 9.271025 1.2444712 #> 614 0.7519149 0.7519149 NA NA 5.3784638 9.779899 NA #> 620 2.0074095 2.0074095 NA NA 5.6077502 3.361662 4.4929978 #> 630 2.2773688 2.2773688 NA NA 6.0081475 7.301299 5.8988929 #> 641 1.4448595 1.4448595 NA NA 0.4939544 13.007338 4.2861144 #> 645 1.3303544 1.3303544 NA NA 0.4900168 12.087363 4.9350083 #> BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper #> 1 6.609214 0.97850954 4.0686985 Inf Inf #> 2 5.347706 0.20008806 1.1095586 Inf Inf #> 5 7.073198 0.75185882 1.4649978 Inf Inf #> 6 7.073198 0.75185882 1.4649978 Inf Inf #> 7 7.073198 0.75185882 1.4649978 Inf Inf #> 8 6.172119 0.05543773 0.6804425 0.56115437 Inf #> 9 5.588358 0.08095270 0.7936032 0.32929317 Inf #> 10 8.017772 0.42468408 1.0520363 Inf Inf #> 11 6.181671 0.07579775 0.7005182 Inf Inf #> 12 6.802633 0.03694799 0.4209217 Inf Inf #> 13 5.820719 1.67433198 5.3037292 Inf Inf #> 14 5.164472 1.25236329 2.8522870 7.56375893 Inf #> 16 5.863188 1.32631865 6.0595553 5.92523484 Inf #> 17 5.863188 1.32631865 6.0595553 5.92523484 Inf #> 18 5.465323 2.76071129 7.1933590 7.67906625 Inf #> 19 5.465323 2.76071129 7.1933590 7.67906625 Inf #> 20 5.465323 2.76071129 7.1933590 7.67906625 Inf #> 22 4.380763 0.36442365 2.2863213 Inf Inf #> 23 4.380763 0.36442365 2.2863213 Inf Inf #> 25 4.862095 1.87728956 5.7928776 Inf Inf #> 26 4.425610 0.63687870 2.6421628 1.60732079 Inf #> 27 4.425610 0.63687870 2.6421628 1.60732079 Inf #> 28 4.425610 0.63687870 2.6421628 1.60732079 Inf #> 29 4.425610 0.63687870 2.6421628 1.60732079 Inf #> 30 4.425610 0.63687870 2.6421628 1.60732079 Inf #> 31 5.860923 0.37773418 3.4329037 2.12091124 Inf #> 32 5.860923 0.37773418 3.4329037 2.12091124 Inf #> 33 5.860923 0.37773418 3.4329037 2.12091124 Inf #> 34 5.860923 0.37773418 3.4329037 2.12091124 Inf #> 35 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 36 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 37 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 39 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 40 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 41 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 42 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 43 6.168631 0.24887531 4.0820798 2.79173759 Inf #> 44 4.573695 0.32686514 3.1332891 5.73891269 Inf #> 46 6.316887 2.43380228 6.3223929 Inf Inf #> 47 6.316887 2.43380228 6.3223929 Inf Inf #> 48 4.940865 0.35799216 4.2882933 2.32048362 Inf #> 49 4.940865 0.35799216 4.2882933 2.32048362 Inf #> 50 4.940865 0.35799216 4.2882933 2.32048362 Inf #> 51 4.940865 0.35799216 4.2882933 2.32048362 Inf #> 52 5.737279 0.30086446 3.1833272 1.30741047 Inf #> 53 5.737279 0.30086446 3.1833272 1.30741047 Inf #> 54 5.737279 0.30086446 3.1833272 1.30741047 Inf #> 55 5.737279 0.30086446 3.1833272 1.30741047 Inf #> 56 5.737279 0.30086446 3.1833272 1.30741047 Inf #> 57 5.737279 0.30086446 3.1833272 1.30741047 Inf #> 58 5.177716 0.46124416 1.4873687 Inf Inf #> 59 4.962470 1.03576992 3.6179409 Inf Inf #> 60 4.962470 1.03576992 3.6179409 Inf Inf #> 61 5.158766 0.48406738 1.2370211 0.80809797 Inf #> 62 5.158766 0.48406738 1.2370211 0.80809797 Inf #> 63 5.158766 0.48406738 1.2370211 0.80809797 Inf #> 64 5.158766 0.48406738 1.2370211 0.80809797 Inf #> 65 5.158766 0.48406738 1.2370211 0.80809797 Inf #> 67 6.677576 0.26644454 4.2871164 Inf Inf #> 68 6.677576 0.26644454 4.2871164 Inf Inf #> 69 6.677576 0.26644454 4.2871164 Inf Inf #> 70 6.677576 0.26644454 4.2871164 Inf Inf #> 71 5.840236 3.09334793 6.6162300 7.32335336 Inf #> 72 5.840236 3.09334793 6.6162300 7.32335336 Inf #> 73 5.840236 3.09334793 6.6162300 7.32335336 Inf #> 75 4.972785 0.63064808 4.7996149 5.94571062 Inf #> 76 4.972785 0.63064808 4.7996149 5.94571062 Inf #> 77 4.972785 0.63064808 4.7996149 5.94571062 Inf #> 79 5.236113 0.52106128 5.9739107 5.91975161 Inf #> 80 5.236113 0.52106128 5.9739107 5.91975161 Inf #> 81 5.980553 0.42901273 4.3080978 5.24829289 Inf #> 82 5.980553 0.42901273 4.3080978 5.24829289 Inf #> 83 5.980553 0.42901273 4.3080978 5.24829289 Inf #> 84 5.980553 0.42901273 4.3080978 5.24829289 Inf #> 86 11.185391 0.24296501 0.8254193 2.32365503 Inf #> 87 11.170683 0.28244583 0.9252443 2.78927718 Inf #> 103 7.959897 0.46760570 2.8942164 1.94401632 Inf #> 116 8.370532 2.62074447 6.1892385 6.39907958 Inf #> 121 11.774346 1.07799896 3.2324908 6.13410907 Inf #> 123 11.262816 0.39650460 1.0210325 0.93588481 6.2063487 #> 125 11.840420 1.64980574 4.7578654 Inf Inf #> 131 6.444924 0.37828212 3.9147635 0.64172080 4.6921648 #> 134 12.192005 0.45119793 1.3369379 Inf Inf #> 150 6.228886 1.30775683 3.0619063 2.63321348 5.1889529 #> 157 8.874974 0.27525816 1.6705374 0.44800424 2.7783783 #> 161 12.091282 0.60384236 5.6978525 Inf Inf #> 169 9.162234 0.96799087 1.9783744 3.14745914 5.1690481 #> 172 8.200742 1.50525257 3.4011695 1.95515289 3.7737373 #> 178 10.396666 0.53046359 1.1464625 1.68379792 Inf #> 180 12.563182 1.54875481 3.5028687 Inf Inf #> 181 12.563182 1.54875481 3.5028687 Inf Inf #> 183 7.409795 0.08183997 0.5980861 0.19125061 1.6257035 #> 184 7.049409 1.75885065 4.3783442 2.22896204 5.6185328 #> 186 8.010933 2.93461899 6.0719877 5.69970451 Inf #> 188 4.584806 0.50278548 1.8172104 0.39883826 1.5901895 #> 193 3.345752 0.35654678 1.4592305 0.16656157 1.1565046 #> 196 11.050749 0.43301683 0.8971572 0.74039512 1.2380754 #> 198 9.315398 1.73688931 5.1197523 5.38884008 Inf #> 202 8.688686 0.35183367 1.7316828 0.36262819 1.4953577 #> 228 11.354430 0.48380870 1.5400128 Inf Inf #> 233 4.292819 0.40044672 6.5720202 0.37251622 5.9756156 #> 235 10.664641 0.31820753 1.5828158 1.30158571 Inf #> 238 14.176242 2.74132244 5.6217506 6.05963970 Inf #> 243 11.464039 0.45259236 0.9852054 Inf Inf #> 245 9.140814 2.60802191 6.3443547 4.77393169 Inf #> 246 9.140814 2.60802191 6.3443547 4.77393169 Inf #> 252 19.719388 1.32526215 3.0171899 Inf Inf #> 253 10.397567 0.27487668 0.8684603 1.20202041 Inf #> 256 10.146508 0.34554032 1.8031346 Inf Inf #> 266 4.549637 0.11229115 1.1562790 0.08920198 0.6082146 #> 275 12.207078 2.02820057 5.2593819 6.17057748 Inf #> 276 6.266743 0.27957255 1.4053942 0.62385724 2.4264491 #> 291 4.165686 1.61304816 4.7440724 2.05493380 4.8155223 #> 293 10.806789 2.28426943 5.7598313 Inf Inf #> 301 13.841195 1.57349911 3.9836203 Inf Inf #> 305 11.680806 2.74748436 5.9480926 5.95909053 Inf #> 306 11.628496 1.31584676 3.3142276 Inf Inf #> 312 10.951725 0.32210163 1.4464539 Inf Inf #> 327 4.452148 0.11967646 1.0401929 0.19534852 1.6577848 #> 338 11.437571 1.50483906 3.0487558 5.43599051 Inf #> 341 3.132075 0.55429742 5.7718558 0.24775913 0.9522847 #> 344 9.386265 0.41050756 2.0533236 2.06051125 Inf #> 346 10.639970 1.12797148 3.0911075 3.10061531 6.0301429 #> 354 6.170927 2.27138538 5.7358531 3.72920786 6.3344473 #> 361 10.315734 2.77164549 6.0800988 5.52360080 Inf #> 364 11.015759 1.35631407 3.9482498 6.11649532 Inf #> 370 5.506560 0.38959091 2.2272543 0.50373257 2.2560210 #> 372 12.262440 0.43168938 4.3245839 5.06418512 Inf #> 375 12.986136 2.72596759 5.7854763 5.14744071 Inf #> 389 5.626303 2.38471036 5.6950050 1.25212089 5.0100759 #> 399 7.386885 0.58678810 4.9024458 Inf Inf #> 405 5.359396 1.48802589 3.7982782 1.85410611 4.4410965 #> 408 11.472711 0.49209232 4.7797975 Inf Inf #> 419 9.154518 3.01643160 5.7582422 4.45466791 6.2076045 #> 430 8.294884 3.11003882 5.9115909 5.95974847 Inf #> 445 11.635874 0.24885181 1.7730190 Inf Inf #> 458 8.924420 2.95397705 6.1224920 5.86209386 Inf #> 460 5.575966 1.51555559 4.1284671 1.76309457 3.8398589 #> 475 14.012261 2.57284026 6.0635216 5.59601377 Inf #> 476 8.390585 0.35181993 1.9120592 1.37283122 Inf #> 488 4.922630 0.20461711 1.6845815 0.13994566 1.2568975 #> 490 7.477196 0.32356830 1.4338426 0.97175675 3.0973018 #> 492 11.411100 2.93033518 6.1810802 5.55823991 Inf #> 494 3.873826 0.18220941 1.0969609 0.09169953 0.8486895 #> 499 9.534307 1.36558810 3.0277098 5.76250310 Inf #> 501 11.530584 0.33731937 0.9197975 1.50805897 Inf #> 513 3.142488 2.82669704 6.1106136 1.28010249 5.2509376 #> 519 13.060251 2.75999567 5.7929246 6.33809779 Inf #> 534 11.620474 0.57587075 5.6502320 1.73266615 Inf #> 540 5.311273 2.09802632 5.1109137 1.45839343 4.7290176 #> 549 3.998886 0.05399315 1.2442471 0.01432065 0.6768693 #> 551 4.314200 0.27996824 2.9680621 0.12373376 1.4896918 #> 552 3.282101 1.41586955 4.2784519 0.34784795 1.0906435 #> 553 3.623286 1.47301680 4.2390445 0.68741986 1.8230891 #> 554 4.303831 3.07905725 6.0763029 2.72472787 5.8528389 #> 567 4.510380 3.04662568 5.8599969 1.79850499 5.3875293 #> 571 4.106953 0.64467381 5.9696696 0.44860443 4.9243516 #> 577 4.979126 0.65520412 6.2161646 0.68872614 5.5777863 #> 579 2.588375 1.71979781 5.8334366 0.29269724 1.3384842 #> 586 5.352737 0.31331655 1.2981207 0.45448294 1.4204213 #> 587 5.352737 0.31331655 1.2981207 0.45448294 1.4204213 #> 593 2.980407 2.37205969 6.2112758 1.06163514 5.0651393 #> 597 4.740045 2.16859102 5.3652514 2.72959715 5.5368126 #> 604 7.830261 0.38533255 0.8633687 1.66508242 Inf #> 608 6.458822 2.75629900 5.7006541 4.24574070 6.1784483 #> 610 3.062052 3.04378438 6.0023852 1.53742008 5.4386620 #> 611 9.677450 0.25304437 1.2327338 0.63096978 2.7434432 #> 614 10.538488 3.21741886 5.6953260 6.60129169 Inf #> 620 2.864603 2.80741209 6.0552893 1.04401193 5.0611838 #> 630 7.436280 2.99251277 6.0940883 2.95949542 5.9721761 #> 641 12.014582 0.23273791 0.8898776 2.22713522 Inf #> 645 11.110192 0.27124408 0.8292255 2.91506706 Inf #> nboot.successful path_class #> 1 1000 Lipid metabolism #> 2 957 Lipid metabolism #> 5 1000 Biosynthesis of other secondary metabolites #> 6 1000 Membrane transport #> 7 1000 Signal transduction #> 8 648 Lipid metabolism #> 9 620 Lipid metabolism #> 10 872 Lipid metabolism #> 11 909 Lipid metabolism #> 12 565 Lipid metabolism #> 13 1000 Lipid metabolism #> 14 1000 Lipid metabolism #> 16 1000 Membrane transport #> 17 1000 Signal transduction #> 18 718 Amino acid metabolism #> 19 718 Biosynthesis of other secondary metabolites #> 20 718 Translation #> 22 975 Membrane transport #> 23 975 Signal transduction #> 25 938 Membrane transport #> 26 962 Amino acid metabolism #> 27 962 Metabolism of other amino acids #> 28 962 Biosynthesis of other secondary metabolites #> 29 962 Translation #> 30 962 Membrane transport #> 31 979 Amino acid metabolism #> 32 979 Biosynthesis of other secondary metabolites #> 33 979 Translation #> 34 979 Membrane transport #> 35 851 Amino acid metabolism #> 36 851 Metabolism of other amino acids #> 37 851 Lipid metabolism #> 39 851 Energy metabolism #> 40 851 Translation #> 41 851 Biosynthesis of other secondary metabolites #> 42 851 Membrane transport #> 43 851 Signal transduction #> 44 1000 Amino acid metabolism #> 46 859 Energy metabolism #> 47 859 Signal transduction #> 48 833 Amino acid metabolism #> 49 833 Metabolism of other amino acids #> 50 833 Biosynthesis of other secondary metabolites #> 51 833 Membrane transport #> 52 890 Amino acid metabolism #> 53 890 Metabolism of other amino acids #> 54 890 Energy metabolism #> 55 890 Translation #> 56 890 Membrane transport #> 57 890 Signal transduction #> 58 940 Amino acid metabolism #> 59 1000 Lipid metabolism #> 60 1000 Amino acid metabolism #> 61 635 Amino acid metabolism #> 62 635 Metabolism of other amino acids #> 63 635 Biosynthesis of other secondary metabolites #> 64 635 Translation #> 65 635 Membrane transport #> 67 820 Amino acid metabolism #> 68 820 Metabolism of other amino acids #> 69 820 Biosynthesis of other secondary metabolites #> 70 820 Membrane transport #> 71 722 Energy metabolism #> 72 722 Membrane transport #> 73 722 Signal transduction #> 75 953 Amino acid metabolism #> 76 953 Lipid metabolism #> 77 953 Energy metabolism #> 79 962 Amino acid metabolism #> 80 962 Signal transduction #> 81 998 Amino acid metabolism #> 82 998 Metabolism of other amino acids #> 83 998 Translation #> 84 998 Membrane transport #> 86 497 Nucleotide metabolism #> 87 495 Nucleotide metabolism #> 103 443 Metabolism of other amino acids #> 116 353 Nucleotide metabolism #> 121 483 Nucleotide metabolism #> 123 304 Membrane transport #> 125 500 Nucleotide metabolism #> 131 483 Metabolism of other amino acids #> 134 405 Metabolism of other amino acids #> 150 500 Metabolism of other amino acids #> 157 487 Membrane transport #> 161 439 Metabolism of terpenoids and polyketides #> 169 500 Nucleotide metabolism #> 172 500 Metabolism of other amino acids #> 178 336 Nucleotide metabolism #> 180 500 Metabolism of other amino acids #> 181 500 Nucleotide metabolism #> 183 478 Nucleotide metabolism #> 184 500 Metabolism of other amino acids #> 186 295 Nucleotide metabolism #> 188 482 Metabolism of terpenoids and polyketides #> 193 483 Membrane transport #> 196 498 Transport and catabolism #> 198 500 Metabolism of terpenoids and polyketides #> 202 476 Metabolism of other amino acids #> 228 261 Nucleotide metabolism #> 233 415 Metabolism of other amino acids #> 235 480 Nucleotide metabolism #> 238 336 Metabolism of other amino acids #> 243 333 Transport and catabolism #> 245 315 Nucleotide metabolism #> 246 315 Metabolism of other amino acids #> 252 500 Metabolism of terpenoids and polyketides #> 253 499 Nucleotide metabolism #> 256 479 Membrane transport #> 266 446 Metabolism of terpenoids and polyketides #> 275 454 Metabolism of terpenoids and polyketides #> 276 491 Metabolism of terpenoids and polyketides #> 291 500 Metabolism of terpenoids and polyketides #> 293 377 Metabolism of terpenoids and polyketides #> 301 500 Metabolism of other amino acids #> 305 275 Metabolism of terpenoids and polyketides #> 306 500 Metabolism of other amino acids #> 312 491 Transport and catabolism #> 327 474 Nucleotide metabolism #> 338 500 Metabolism of terpenoids and polyketides #> 341 403 Transport and catabolism #> 344 469 Nucleotide metabolism #> 346 500 Metabolism of other amino acids #> 354 385 Nucleotide metabolism #> 361 291 Nucleotide metabolism #> 364 500 Nucleotide metabolism #> 370 465 Transport and catabolism #> 372 406 Metabolism of other amino acids #> 375 330 Transport and catabolism #> 389 345 Metabolism of other amino acids #> 399 466 Transport and catabolism #> 405 500 Metabolism of terpenoids and polyketides #> 408 303 Metabolism of other amino acids #> 419 266 Transport and catabolism #> 430 317 Membrane transport #> 445 485 Metabolism of other amino acids #> 458 282 Transport and catabolism #> 460 500 Membrane transport #> 475 366 Transport and catabolism #> 476 473 Metabolism of other amino acids #> 488 484 Metabolism of other amino acids #> 490 490 Membrane transport #> 492 306 Transport and catabolism #> 494 492 Membrane transport #> 499 500 Metabolism of other amino acids #> 501 499 Metabolism of other amino acids #> 513 309 Membrane transport #> 519 360 Membrane transport #> 534 434 Metabolism of other amino acids #> 540 461 Metabolism of other amino acids #> 549 483 Metabolism of terpenoids and polyketides #> 551 464 Membrane transport #> 552 500 Transport and catabolism #> 553 500 Metabolism of terpenoids and polyketides #> 554 266 Nucleotide metabolism #> 567 268 Transport and catabolism #> 571 448 Nucleotide metabolism #> 577 371 Nucleotide metabolism #> 579 500 Membrane transport #> 586 493 Transport and catabolism #> 587 493 Membrane transport #> 593 343 Metabolism of other amino acids #> 597 440 Nucleotide metabolism #> 604 250 Nucleotide metabolism #> 608 301 Nucleotide metabolism #> 610 280 Nucleotide metabolism #> 611 494 Metabolism of terpenoids and polyketides #> 614 283 Metabolism of other amino acids #> 620 312 Nucleotide metabolism #> 630 264 Nucleotide metabolism #> 641 496 Nucleotide metabolism #> 645 497 Nucleotide metabolism #> molecular.level #> 1 metabolites #> 2 metabolites #> 5 metabolites #> 6 metabolites #> 7 metabolites #> 8 metabolites #> 9 metabolites #> 10 metabolites #> 11 metabolites #> 12 metabolites #> 13 metabolites #> 14 metabolites #> 16 metabolites #> 17 metabolites #> 18 metabolites #> 19 metabolites #> 20 metabolites #> 22 metabolites #> 23 metabolites #> 25 metabolites #> 26 metabolites #> 27 metabolites #> 28 metabolites #> 29 metabolites #> 30 metabolites #> 31 metabolites #> 32 metabolites #> 33 metabolites #> 34 metabolites #> 35 metabolites #> 36 metabolites #> 37 metabolites #> 39 metabolites #> 40 metabolites #> 41 metabolites #> 42 metabolites #> 43 metabolites #> 44 metabolites #> 46 metabolites #> 47 metabolites #> 48 metabolites #> 49 metabolites #> 50 metabolites #> 51 metabolites #> 52 metabolites #> 53 metabolites #> 54 metabolites #> 55 metabolites #> 56 metabolites #> 57 metabolites #> 58 metabolites #> 59 metabolites #> 60 metabolites #> 61 metabolites #> 62 metabolites #> 63 metabolites #> 64 metabolites #> 65 metabolites #> 67 metabolites #> 68 metabolites #> 69 metabolites #> 70 metabolites #> 71 metabolites #> 72 metabolites #> 73 metabolites #> 75 metabolites #> 76 metabolites #> 77 metabolites #> 79 metabolites #> 80 metabolites #> 81 metabolites #> 82 metabolites #> 83 metabolites #> 84 metabolites #> 86 contigs #> 87 contigs #> 103 contigs #> 116 contigs #> 121 contigs #> 123 contigs #> 125 contigs #> 131 contigs #> 134 contigs #> 150 contigs #> 157 contigs #> 161 contigs #> 169 contigs #> 172 contigs #> 178 contigs #> 180 contigs #> 181 contigs #> 183 contigs #> 184 contigs #> 186 contigs #> 188 contigs #> 193 contigs #> 196 contigs #> 198 contigs #> 202 contigs #> 228 contigs #> 233 contigs #> 235 contigs #> 238 contigs #> 243 contigs #> 245 contigs #> 246 contigs #> 252 contigs #> 253 contigs #> 256 contigs #> 266 contigs #> 275 contigs #> 276 contigs #> 291 contigs #> 293 contigs #> 301 contigs #> 305 contigs #> 306 contigs #> 312 contigs #> 327 contigs #> 338 contigs #> 341 contigs #> 344 contigs #> 346 contigs #> 354 contigs #> 361 contigs #> 364 contigs #> 370 contigs #> 372 contigs #> 375 contigs #> 389 contigs #> 399 contigs #> 405 contigs #> 408 contigs #> 419 contigs #> 430 contigs #> 445 contigs #> 458 contigs #> 460 contigs #> 475 contigs #> 476 contigs #> 488 contigs #> 490 contigs #> 492 contigs #> 494 contigs #> 499 contigs #> 501 contigs #> 513 contigs #> 519 contigs #> 534 contigs #> 540 contigs #> 549 contigs #> 551 contigs #> 552 contigs #> 553 contigs #> 554 contigs #> 567 contigs #> 571 contigs #> 577 contigs #> 579 contigs #> 586 contigs #> 587 contigs #> 593 contigs #> 597 contigs #> 604 contigs #> 608 contigs #> 610 contigs #> 611 contigs #> 614 contigs #> 620 contigs #> 630 contigs #> 641 contigs #> 645 contigs extendedres.3 #> id irow adjpvalue model nbpar b c #> 1 NAP47_51 46 7.158246e-04 linear 2 -5.600559e-02 NA #> 2 NAP_2 2 6.232579e-05 exponential 3 4.598124e-01 NA #> 5 NAP_30 28 1.028343e-05 linear 2 -4.507832e-02 NA #> 6 NAP_30 28 1.028343e-05 linear 2 -4.507832e-02 NA #> 7 NAP_30 28 1.028343e-05 linear 2 -4.507832e-02 NA #> 8 NAP_38 34 1.885047e-03 exponential 3 6.010677e-01 NA #> 9 NAP_42 38 4.160193e-03 exponential 3 6.721023e-01 NA #> 10 NAP_52 47 3.920169e-02 log-Gauss-probit 5 4.500859e-01 7.2020026 #> 11 NAP_54 49 3.767103e-04 exponential 3 4.520654e-01 NA #> 12 NAP_56 51 1.489919e-03 exponential 3 4.392508e-01 NA #> 13 NAP_58 53 2.834198e-02 linear 2 -1.938127e-02 NA #> 14 NAP_73 67 3.767103e-04 linear 2 -5.787842e-02 NA #> 16 NP_121 197 9.889460e-03 linear 2 6.149475e-02 NA #> 17 NP_121 197 9.889460e-03 linear 2 6.149475e-02 NA #> 18 NP_129 204 7.286216e-03 exponential 3 7.786029e-03 NA #> 19 NP_129 204 7.286216e-03 exponential 3 7.786029e-03 NA #> 20 NP_129 204 7.286216e-03 exponential 3 7.786029e-03 NA #> 22 NP_140 214 8.550044e-03 Gauss-probit 4 6.955281e-01 4.8357744 #> 23 NP_140 214 8.550044e-03 Gauss-probit 4 6.955281e-01 4.8357744 #> 25 NP_147 221 1.061967e-03 exponential 3 -3.195388e-02 NA #> 26 NP_33 113 5.599308e-02 log-Gauss-probit 4 6.150170e-01 4.9173448 #> 27 NP_33 113 5.599308e-02 log-Gauss-probit 4 6.150170e-01 4.9173448 #> 28 NP_33 113 5.599308e-02 log-Gauss-probit 4 6.150170e-01 4.9173448 #> 29 NP_33 113 5.599308e-02 log-Gauss-probit 4 6.150170e-01 4.9173448 #> 30 NP_33 113 5.599308e-02 log-Gauss-probit 4 6.150170e-01 4.9173448 #> 31 NP_35 115 3.238559e-03 Gauss-probit 4 1.507579e+00 5.9751431 #> 32 NP_35 115 3.238559e-03 Gauss-probit 4 1.507579e+00 5.9751431 #> 33 NP_35 115 3.238559e-03 Gauss-probit 4 1.507579e+00 5.9751431 #> 34 NP_35 115 3.238559e-03 Gauss-probit 4 1.507579e+00 5.9751431 #> 35 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 36 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 37 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 39 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 40 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 41 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 42 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 43 NP_43 123 1.055895e-02 exponential 3 -3.967360e-01 NA #> 44 NP_55 135 1.119380e-06 Gauss-probit 4 2.325273e+00 4.4698821 #> 46 NP_56 136 5.289310e-03 exponential 3 -4.360528e-03 NA #> 47 NP_56 136 5.289310e-03 exponential 3 -4.360528e-03 NA #> 48 NP_59 139 1.293440e-02 exponential 3 -3.667815e-01 NA #> 49 NP_59 139 1.293440e-02 exponential 3 -3.667815e-01 NA #> 50 NP_59 139 1.293440e-02 exponential 3 -3.667815e-01 NA #> 51 NP_59 139 1.293440e-02 exponential 3 -3.667815e-01 NA #> 52 NP_60 140 6.939560e-03 exponential 3 -5.150005e-01 NA #> 53 NP_60 140 6.939560e-03 exponential 3 -5.150005e-01 NA #> 54 NP_60 140 6.939560e-03 exponential 3 -5.150005e-01 NA #> 55 NP_60 140 6.939560e-03 exponential 3 -5.150005e-01 NA #> 56 NP_60 140 6.939560e-03 exponential 3 -5.150005e-01 NA #> 57 NP_60 140 6.939560e-03 exponential 3 -5.150005e-01 NA #> 58 NP_68 147 3.449660e-04 Gauss-probit 4 2.444950e+00 5.0555770 #> 59 NP_69 148 1.156302e-03 linear 2 -5.076324e-02 NA #> 60 NP_69 148 1.156302e-03 linear 2 -5.076324e-02 NA #> 61 NP_74 153 7.029527e-02 log-Gauss-probit 5 2.715410e-01 4.8474027 #> 62 NP_74 153 7.029527e-02 log-Gauss-probit 5 2.715410e-01 4.8474027 #> 63 NP_74 153 7.029527e-02 log-Gauss-probit 5 2.715410e-01 4.8474027 #> 64 NP_74 153 7.029527e-02 log-Gauss-probit 5 2.715410e-01 4.8474027 #> 65 NP_74 153 7.029527e-02 log-Gauss-probit 5 2.715410e-01 4.8474027 #> 67 NP_90 168 3.072572e-02 exponential 3 -2.686242e-01 NA #> 68 NP_90 168 3.072572e-02 exponential 3 -2.686242e-01 NA #> 69 NP_90 168 3.072572e-02 exponential 3 -2.686242e-01 NA #> 70 NP_90 168 3.072572e-02 exponential 3 -2.686242e-01 NA #> 71 NP_92 170 2.278810e-03 exponential 3 -4.595689e-03 NA #> 72 NP_92 170 2.278810e-03 exponential 3 -4.595689e-03 NA #> 73 NP_92 170 2.278810e-03 exponential 3 -4.595689e-03 NA #> 75 NP_94 172 1.798752e-05 Gauss-probit 4 3.016576e+00 4.8628416 #> 76 NP_94 172 1.798752e-05 Gauss-probit 4 3.016576e+00 4.8628416 #> 77 NP_94 172 1.798752e-05 Gauss-probit 4 3.016576e+00 4.8628416 #> 79 NP_96 174 5.128859e-04 Gauss-probit 4 2.409906e+00 5.2165398 #> 80 NP_96 174 5.128859e-04 Gauss-probit 4 2.409906e+00 5.2165398 #> 81 NP_98 176 1.544866e-04 Gauss-probit 4 1.880325e+00 5.9083172 #> 82 NP_98 176 1.544866e-04 Gauss-probit 4 1.880325e+00 5.9083172 #> 83 NP_98 176 1.544866e-04 Gauss-probit 4 1.880325e+00 5.9083172 #> 84 NP_98 176 1.544866e-04 Gauss-probit 4 1.880325e+00 5.9083172 #> 85 c00134 2802 2.762369e-04 linear 2 -2.179358e-01 NA #> 86 c00276 39331 9.401685e-07 exponential 3 1.499436e+00 NA #> 87 c00281 41217 2.894669e-06 exponential 3 1.408172e+00 NA #> 88 c00322 52577 1.875371e-03 exponential 3 1.805394e-03 NA #> 91 c00398 54508 1.668205e-05 linear 2 -1.405762e-01 NA #> 95 c00628 61115 5.388575e-07 exponential 3 4.923794e-02 NA #> 99 c00847 5295 9.884514e-03 linear 2 -1.266449e-01 NA #> 102 c00941 7809 4.335219e-03 linear 2 -7.650138e-02 NA #> 103 c00973 8280 6.803372e-03 exponential 3 -7.727847e-01 NA #> 104 c00973 8280 6.803372e-03 exponential 3 -7.727847e-01 NA #> 106 c01041 9614 2.607961e-04 exponential 3 1.528981e+00 NA #> 109 c01117 12251 2.634664e-03 linear 2 1.543520e-01 NA #> 110 c01133 12630 4.922435e-03 Gauss-probit 4 2.075770e+00 9.8438541 #> 111 c01155 13129 6.385399e-03 Gauss-probit 4 1.840019e+00 6.1917399 #> 115 c01250 16689 1.585374e-05 linear 2 4.316514e-01 NA #> 116 c01318 18587 9.761656e-03 exponential 3 -2.541336e-02 NA #> 117 c01370 20546 2.292186e-03 linear 2 9.464515e-02 NA #> 120 c01438 22651 1.939187e-04 Gauss-probit 4 1.764551e+00 10.9277844 #> 121 c01442 22830 8.398538e-07 exponential 3 -4.594974e-01 NA #> 122 c01447 23029 4.281444e-10 exponential 3 -1.254855e+00 NA #> 123 c01449 23118 3.283441e-05 Gauss-probit 4 4.461301e+00 3.6813967 #> 125 c01613 28185 5.354723e-03 linear 2 7.991209e-02 NA #> 127 c01616 28258 1.619046e-06 exponential 3 3.240702e-02 NA #> 128 c01629 28787 2.064119e-03 Gauss-probit 5 4.505403e+00 14.4444824 #> 129 c01643 29361 6.042318e-05 exponential 3 1.083425e-03 NA #> 131 c01645 29447 4.923790e-08 Gauss-probit 4 2.701659e+00 3.1591011 #> 132 c01645 29447 4.923790e-08 Gauss-probit 4 2.701659e+00 3.1591011 #> 133 c01739 32213 2.428740e-04 log-Gauss-probit 4 7.597910e-01 8.6772914 #> 134 c01924 38335 2.843216e-04 Gauss-probit 4 2.446537e+00 11.8943363 #> 137 c01952 39007 6.650930e-03 exponential 3 1.162982e-04 NA #> 140 c02004 40603 1.872409e-03 linear 2 9.583803e-02 NA #> 141 c02010 40836 3.289575e-06 Gauss-probit 4 2.473829e+00 11.5666888 #> 142 c02083 42840 3.959555e-04 linear 2 -1.362122e-01 NA #> 143 c02160 45443 1.197938e-07 exponential 3 4.291368e-01 NA #> 147 c02486 52753 2.171026e-05 exponential 3 -6.980028e-03 NA #> 148 c02572 52958 4.595821e-03 linear 2 1.555437e-01 NA #> 149 c02651 53192 2.080403e-04 exponential 3 4.499442e-02 NA #> 150 c02837 53881 7.971049e-04 linear 2 1.575210e-01 NA #> 152 c02877 53976 2.432349e-03 linear 2 -1.413708e-01 NA #> 156 c02955 54166 8.455883e-03 linear 2 2.166378e-01 NA #> 157 c02964 54187 3.684145e-04 exponential 3 -1.686471e+00 NA #> 158 c03046 54476 2.875687e-05 linear 2 -2.051304e-01 NA #> 161 c03088 54664 3.084821e-03 Gauss-probit 4 2.148866e+00 11.5968176 #> 164 c03134 54867 5.690878e-04 Gauss-probit 4 2.175505e+00 15.6410941 #> 165 c03146 54920 1.245703e-05 linear 2 -4.288489e-01 NA #> 166 c03147 54922 4.186163e-03 log-Gauss-probit 5 7.895259e-01 8.1665963 #> 167 c03150 54936 3.566795e-04 linear 2 1.084285e-01 NA #> 169 c03232 55298 2.548714e-06 linear 2 2.143541e-01 NA #> 170 c03258 55367 4.473786e-04 Gauss-probit 4 1.798037e+00 8.9781782 #> 171 c03284 55434 1.365366e-03 linear 2 -3.672796e-01 NA #> 172 c03284 55434 1.365366e-03 linear 2 -3.672796e-01 NA #> 174 c03319 55519 1.104637e-04 Gauss-probit 4 2.837930e+00 10.6370079 #> 178 c03358 55610 1.361311e-03 Gauss-probit 4 2.732236e+00 10.2055856 #> 179 c03392 55694 7.499082e-05 linear 2 -1.334615e-01 NA #> 180 c03440 55810 2.679169e-03 linear 2 -1.138782e-01 NA #> 181 c03440 55810 2.679169e-03 linear 2 -1.138782e-01 NA #> 183 c03526 56019 2.223268e-04 exponential 3 -1.249273e+00 NA #> 184 c03540 56053 3.430925e-03 linear 2 1.965028e-01 NA #> 185 c03540 56053 3.430925e-03 linear 2 1.965028e-01 NA #> 186 c03544 56063 6.386500e-03 exponential 3 -1.417594e-03 NA #> 188 c03571 56127 9.413418e-03 log-Gauss-probit 4 5.983299e-01 4.1680058 #> 190 c03586 56164 3.854715e-03 Gauss-probit 4 2.552963e+00 11.4923353 #> 193 c03661 56344 7.587641e-03 log-Gauss-probit 4 6.923540e-01 3.0415932 #> 196 c03724 56499 4.297972e-05 log-Gauss-probit 4 5.840913e-01 12.2786104 #> 197 c03745 56548 9.243140e-03 linear 2 -1.172201e-01 NA #> 198 c03760 56585 9.777594e-03 linear 2 -1.351428e-01 NA #> 199 c03761 56586 4.702449e-03 linear 2 1.262182e-01 NA #> 200 c03761 56586 4.702449e-03 linear 2 1.262182e-01 NA #> 201 c03784 56645 5.006652e-03 log-Gauss-probit 4 6.898228e-01 9.6540953 #> 202 c03784 56645 5.006652e-03 log-Gauss-probit 4 6.898228e-01 9.6540953 #> 203 c03784 56645 5.006652e-03 log-Gauss-probit 4 6.898228e-01 9.6540953 #> 205 c03801 56685 2.570986e-03 log-Gauss-probit 4 9.764227e-01 13.7221921 #> 207 c03925 57063 5.381358e-04 Gauss-probit 5 3.210268e+00 2.3821872 #> 208 c03931 57089 2.851070e-03 Gauss-probit 4 6.126334e+00 0.7526332 #> 211 c03948 57165 6.052153e-03 log-Gauss-probit 4 7.690806e-01 11.6144651 #> 214 c03950 57172 5.750220e-03 log-Gauss-probit 4 8.114609e-01 7.7688166 #> 215 c03979 57294 3.113834e-03 exponential 3 -4.177525e-03 NA #> 217 c04048 57585 3.263192e-03 linear 2 -1.211670e-01 NA #> 218 c04049 57589 4.043602e-04 log-Gauss-probit 4 7.902587e-01 13.3308965 #> 220 c04113 57859 1.165972e-04 linear 2 3.130549e-01 NA #> 221 c04117 57876 3.652581e-04 Gauss-probit 5 6.914901e+00 15.3546350 #> 222 c04129 57927 2.456812e-03 linear 2 -1.106194e-01 NA #> 225 c04240 58395 2.561139e-03 exponential 3 -1.404086e-03 NA #> 228 c04342 58826 1.308910e-04 Gauss-probit 4 7.197426e+00 7.7233560 #> 229 c04391 59033 3.624150e-04 linear 2 -1.363145e-01 NA #> 232 c04434 59217 7.874407e-03 Gauss-probit 5 3.908298e+00 2.8969340 #> 233 c04434 59217 7.874407e-03 Gauss-probit 5 3.908298e+00 2.8969340 #> 235 c04513 59551 9.612303e-03 log-Gauss-probit 4 7.506663e-01 11.8496013 #> 236 c04527 59607 1.222252e-03 exponential 3 4.226728e-04 NA #> 237 c04553 59682 2.052935e-04 exponential 3 -6.566892e-03 NA #> 238 c04553 59682 2.052935e-04 exponential 3 -6.566892e-03 NA #> 240 c04553 59682 2.052935e-04 exponential 3 -6.566892e-03 NA #> 242 c04609 59807 6.407706e-03 linear 2 2.392413e-01 NA #> 243 c04613 59817 2.581335e-03 Gauss-probit 4 3.032023e+00 11.9083638 #> 245 c04619 59830 8.483282e-03 exponential 3 2.328075e-02 NA #> 246 c04619 59830 8.483282e-03 exponential 3 2.328075e-02 NA #> 248 c04625 59843 3.759793e-03 linear 2 1.791830e-01 NA #> 251 c04647 59892 1.704160e-03 linear 2 9.344909e-02 NA #> 252 c04647 59892 1.704160e-03 linear 2 9.344909e-02 NA #> 253 c04655 59910 4.758216e-05 log-Gauss-probit 4 7.297684e-01 11.5528527 #> 255 c04667 59936 7.175098e-04 exponential 3 3.727143e-03 NA #> 256 c04683 59973 5.823021e-04 exponential 3 8.026136e-01 NA #> 257 c04702 60014 1.150256e-05 exponential 3 -8.958404e-04 NA #> 259 c04721 60057 1.935071e-03 linear 2 8.621251e-02 NA #> 260 c04785 60199 2.947658e-03 exponential 3 1.554432e+00 NA #> 263 c04803 60240 8.645920e-03 linear 2 1.054475e-01 NA #> 265 c04841 60324 1.209154e-05 linear 2 1.327705e-01 NA #> 266 c04883 60416 7.479393e-03 exponential 3 1.701162e+00 NA #> 270 c04981 60634 1.604178e-03 Gauss-probit 5 6.073747e+00 15.5258805 #> 272 c04990 60654 1.290312e-03 linear 2 2.168492e-01 NA #> 274 c05067 60825 3.429820e-03 exponential 3 4.733132e-01 NA #> 275 c05081 60856 1.998628e-04 exponential 3 4.406436e-02 NA #> 276 c05100 60898 4.685588e-05 exponential 3 1.626252e+00 NA #> 277 c05122 60947 5.831294e-03 linear 2 1.017186e-01 NA #> 279 c05183 61177 7.470683e-05 exponential 3 -6.029337e-03 NA #> 280 c05186 61192 9.977277e-06 exponential 3 -7.710575e-03 NA #> 282 c05207 61277 4.276674e-03 exponential 3 1.045991e+00 NA #> 285 c05269 10 9.270577e-05 Gauss-probit 4 2.656350e+00 9.3830167 #> 286 c05284 73 3.841471e-03 linear 2 2.319088e-01 NA #> 287 c05305 159 1.899100e-03 linear 2 -1.337043e-01 NA #> 288 c05305 159 1.899100e-03 linear 2 -1.337043e-01 NA #> 291 c05326 247 4.812259e-03 linear 2 -1.628214e-01 NA #> 293 c05358 382 7.443184e-03 exponential 3 2.090616e-02 NA #> 295 c05377 463 9.290871e-03 linear 2 -9.062729e-02 NA #> 296 c05385 497 5.701646e-05 exponential 3 -1.697255e-01 NA #> 297 c05401 565 9.242438e-03 linear 2 -8.645475e-02 NA #> 298 c05401 565 9.242438e-03 linear 2 -8.645475e-02 NA #> 299 c05417 633 9.280962e-05 exponential 3 -3.112237e-03 NA #> 301 c05581 1323 4.187799e-03 linear 2 1.169616e-01 NA #> 303 c05589 1360 8.134238e-03 linear 2 -6.289437e-02 NA #> 305 c05641 1567 2.764636e-03 exponential 3 -8.701043e-04 NA #> 306 c05645 1576 7.113588e-04 linear 2 -1.348525e-01 NA #> 307 c05645 1576 7.113588e-04 linear 2 -1.348525e-01 NA #> 308 c05698 1694 3.957047e-04 Gauss-probit 4 1.079324e+00 7.5677415 #> 312 c05903 2148 4.374165e-03 log-Gauss-probit 4 7.532079e-01 12.1685832 #> 314 c05923 2195 1.058132e-05 exponential 3 -2.107493e+00 NA #> 317 c05946 2245 7.079721e-03 Gauss-probit 4 2.368354e+00 11.2206087 #> 320 c05970 2298 2.347871e-03 Gauss-probit 5 3.471728e+00 3.6603885 #> 321 c05970 2298 2.347871e-03 Gauss-probit 5 3.471728e+00 3.6603885 #> 322 c05996 2356 3.511114e-04 exponential 3 7.501887e-01 NA #> 327 c06059 2495 1.623735e-03 exponential 3 9.866188e-01 NA #> 328 c06077 2535 2.710065e-04 exponential 3 3.802400e-02 NA #> 330 c06085 2553 1.680296e-05 exponential 3 -2.430678e-03 NA #> 331 c06133 2659 8.340750e-07 linear 2 1.581037e-01 NA #> 332 c06133 2659 8.340750e-07 linear 2 1.581037e-01 NA #> 333 c06142 2694 3.174858e-04 Gauss-probit 4 1.565882e+00 7.7633888 #> 336 c06164 2784 2.112917e-04 Gauss-probit 5 3.551701e+00 6.6100833 #> 338 c06208 2970 8.742721e-04 linear 2 1.440101e-01 NA #> 339 c06258 3180 3.342031e-03 log-Gauss-probit 4 7.135024e-01 2.8818475 #> 340 c06303 3372 1.986352e-04 Gauss-probit 4 2.664174e+00 5.4338860 #> 341 c06313 3413 6.508671e-04 Gauss-probit 4 2.523979e+00 6.3598648 #> 342 c06429 3686 7.498489e-03 linear 2 2.843827e-01 NA #> 343 c06440 3711 4.047233e-03 Gauss-probit 4 1.930519e+00 11.5544508 #> 344 c06518 3887 4.434225e-04 exponential 3 1.282679e+00 NA #> 346 c06548 3976 1.118557e-04 linear 2 2.267280e-01 NA #> 350 c06637 4352 4.977792e-04 exponential 3 2.940553e-02 NA #> 354 c06762 4630 1.129124e-03 exponential 3 2.579227e-02 NA #> 357 c06876 4888 7.528161e-08 exponential 3 8.665206e-02 NA #> 358 c06880 4897 2.817783e-08 exponential 3 -1.170819e-01 NA #> 360 c06880 4897 2.817783e-08 exponential 3 -1.170819e-01 NA #> 361 c06881 4900 9.898720e-04 exponential 3 3.893310e-04 NA #> 362 c06884 4906 9.612303e-03 log-Gauss-probit 4 6.237164e-01 11.5919778 #> 364 c06943 5037 4.856986e-04 linear 2 1.205147e-01 NA #> 365 c06962 5080 9.268299e-03 linear 2 -7.266508e-02 NA #> 368 c07027 5226 4.204526e-05 exponential 3 -1.818945e-02 NA #> 370 c07072 5328 9.285630e-04 exponential 3 1.750908e+00 NA #> 372 c07118 5428 1.069214e-06 Gauss-probit 4 3.201018e+00 13.8162214 #> 375 c07206 5768 1.331320e-04 exponential 3 1.150705e-02 NA #> 376 c07232 5877 3.268967e-03 linear 2 -1.649789e-01 NA #> 378 c07259 5994 8.634188e-03 exponential 3 -2.238603e-03 NA #> 379 c07261 6000 9.973687e-03 exponential 3 -6.624269e-01 NA #> 380 c07263 6010 6.171276e-03 exponential 3 -3.039276e-04 NA #> 384 c07386 6531 6.587401e-09 exponential 3 1.261410e+00 NA #> 386 c07492 6981 1.040808e-03 linear 2 8.280517e-02 NA #> 387 c07492 6981 1.040808e-03 linear 2 8.280517e-02 NA #> 389 c07529 7138 7.515694e-04 exponential 3 2.811126e-02 NA #> 391 c07550 7226 3.659885e-03 linear 2 -1.065091e-01 NA #> 394 c07703 7740 5.335570e-05 exponential 3 -9.916504e-02 NA #> 395 c07715 7768 1.877882e-09 exponential 3 2.083659e-01 NA #> 397 c07797 7953 9.102023e-03 Gauss-probit 5 5.456328e+00 11.4303586 #> 399 c07859 8092 1.007072e-03 Gauss-probit 4 2.145963e+00 7.6939508 #> 401 c07957 8310 1.752806e-04 Gauss-probit 4 2.462513e+00 8.3205953 #> 404 c08065 8553 3.401488e-04 exponential 3 -1.628308e-02 NA #> 405 c08131 8701 1.673911e-03 linear 2 1.826552e-01 NA #> 408 c08241 8948 1.777483e-04 Gauss-probit 4 4.306086e+00 12.7509474 #> 409 c08251 8970 3.556009e-07 exponential 3 3.107220e-01 NA #> 410 c08284 9045 9.182573e-05 exponential 3 -6.347284e-03 NA #> 411 c08296 9071 6.435369e-04 Gauss-probit 4 1.769636e+00 14.5173033 #> 414 c08408 9323 1.435436e-03 linear 2 -1.328818e-01 NA #> 416 c08437 9388 2.774965e-03 Gauss-probit 4 2.500577e+00 15.2527459 #> 419 c08466 9451 1.593456e-04 exponential 3 8.887870e-04 NA #> 420 c08470 9462 9.083535e-04 exponential 3 2.790157e-03 NA #> 422 c08630 9820 1.427514e-05 linear 2 5.428566e-01 NA #> 427 c08733 10248 3.326616e-05 Gauss-probit 4 3.137538e+00 9.9736456 #> 428 c08733 10248 3.326616e-05 Gauss-probit 4 3.137538e+00 9.9736456 #> 430 c08762 10368 1.641885e-03 exponential 3 3.093377e-03 NA #> 433 c08806 10557 2.319865e-04 exponential 3 -1.396020e-03 NA #> 437 c08946 11149 1.361378e-03 linear 2 -1.268688e-01 NA #> 439 c08979 11300 3.707499e-03 exponential 3 1.744083e-03 NA #> 441 c08979 11300 3.707499e-03 exponential 3 1.744083e-03 NA #> 443 c09104 11625 8.299711e-04 linear 2 2.921985e-01 NA #> 445 c09125 11677 1.569172e-03 exponential 3 8.284746e-01 NA #> 447 c09171 11859 6.956216e-04 exponential 3 4.862071e-03 NA #> 450 c09314 12339 1.109864e-06 linear 2 -1.887393e-01 NA #> 453 c09437 12639 3.571289e-04 Gauss-probit 4 1.928769e+00 11.2188707 #> 454 c09437 12639 3.571289e-04 Gauss-probit 4 1.928769e+00 11.2188707 #> 457 c09562 12942 4.373374e-03 exponential 3 -2.161149e-03 NA #> 458 c09562 12942 4.373374e-03 exponential 3 -2.161149e-03 NA #> 460 c09598 13030 2.362131e-03 linear 2 -2.586441e-01 NA #> 461 c09599 13034 5.092176e-03 linear 2 9.794904e-02 NA #> 464 c09662 13187 7.680486e-03 linear 2 -1.015681e-01 NA #> 465 c09664 13193 4.054480e-04 exponential 3 3.206674e-02 NA #> 466 c09667 13199 4.349398e-03 exponential 3 -3.552147e-02 NA #> 467 c09730 13354 2.336694e-03 exponential 3 -7.259535e-04 NA #> 468 c09850 13648 2.372640e-05 exponential 3 -9.250649e-05 NA #> 469 c09874 13707 9.527637e-03 log-Gauss-probit 4 6.223239e-01 6.1391526 #> 470 c09918 13875 8.329706e-03 linear 2 7.343326e-02 NA #> 471 c09971 14113 5.839468e-03 exponential 3 -3.989293e-02 NA #> 473 c10039 14416 8.764282e-04 linear 2 2.073703e-01 NA #> 475 c10057 14495 3.390469e-03 exponential 3 2.415379e-02 NA #> 476 c10066 14533 1.347692e-03 exponential 3 -9.389332e-01 NA #> 479 c10088 14635 3.780459e-03 linear 2 -8.624646e-02 NA #> 480 c10088 14635 3.780459e-03 linear 2 -8.624646e-02 NA #> 481 c10125 14799 1.608679e-03 Gauss-probit 4 2.867974e+00 11.7480225 #> 482 c10155 14932 5.257232e-06 exponential 3 2.863366e-01 NA #> 483 c10163 14967 4.667371e-03 linear 2 1.195824e-01 NA #> 486 c10229 15259 6.113675e-04 Gauss-probit 4 2.711076e+00 12.5673623 #> 488 c10238 15302 1.228501e-03 exponential 3 -1.793775e+00 NA #> 490 c10269 15440 7.296658e-05 exponential 3 1.592475e+00 NA #> 491 c10269 15440 7.296658e-05 exponential 3 1.592475e+00 NA #> 492 c10302 15585 4.225116e-03 exponential 3 6.617470e-03 NA #> 493 c10304 15596 2.269389e-03 linear 2 -1.056212e-01 NA #> 494 c10311 15626 1.016928e-03 log-Gauss-probit 4 7.845651e-01 3.5216604 #> 495 c10345 15778 3.099644e-03 log-Gauss-probit 4 8.904332e-01 11.1141357 #> 496 c10386 15957 3.625125e-03 linear 2 2.199089e-01 NA #> 498 c10413 16075 3.498590e-04 linear 2 1.135548e-01 NA #> 499 c10413 16075 3.498590e-04 linear 2 1.135548e-01 NA #> 500 c10419 16107 4.121617e-03 Gauss-probit 4 5.137237e+00 8.8683481 #> 501 c10499 16461 1.256216e-07 Gauss-probit 4 2.062548e+00 11.8312797 #> 502 c10511 16512 7.145948e-03 linear 2 1.747655e-01 NA #> 504 c10532 16606 7.887575e-04 exponential 3 8.633562e-02 NA #> 505 c10607 16795 2.129357e-04 Gauss-probit 4 2.821383e+00 14.3133366 #> 509 c10754 17155 5.833637e-04 exponential 3 -2.995883e-04 NA #> 513 c10934 17597 5.753046e-03 exponential 3 7.891420e-03 NA #> 514 c10934 17597 5.753046e-03 exponential 3 7.891420e-03 NA #> 515 c10976 17699 8.637145e-03 linear 2 2.195307e-01 NA #> 517 c11168 18235 1.949306e-03 exponential 3 2.214326e-03 NA #> 518 c11210 18416 7.958846e-04 log-Gauss-probit 4 1.054030e+00 7.7051920 #> 519 c11233 18523 4.606893e-03 exponential 3 1.934382e-02 NA #> 520 c11334 18970 8.791723e-04 exponential 3 1.020052e-02 NA #> 521 c11382 19182 4.750777e-03 Gauss-probit 4 1.103583e+00 3.2368169 #> 522 c11397 19250 3.445348e-03 exponential 3 2.401132e-04 NA #> 523 c11456 19511 3.753562e-09 exponential 3 -1.853928e-01 NA #> 525 c11462 19537 9.770964e-03 log-Gauss-probit 4 7.191494e-01 3.3180250 #> 526 c11480 19620 5.163659e-03 linear 2 1.902326e-01 NA #> 527 c11530 19841 8.558466e-03 log-Gauss-probit 4 1.099263e+00 5.8568962 #> 528 c11558 19962 5.915481e-04 linear 2 1.224014e-01 NA #> 531 c11630 20284 5.955893e-06 exponential 3 -3.666783e-03 NA #> 534 c11906 20969 9.654346e-04 Gauss-probit 4 2.292477e+00 11.7948434 #> 535 c11942 21057 4.470344e-03 exponential 3 1.593425e-03 NA #> 540 c12260 22081 8.245828e-06 exponential 3 1.353701e-01 NA #> 542 c12281 22175 4.161221e-03 log-Gauss-probit 4 6.948815e-01 4.9989594 #> 543 c12403 22716 3.760750e-03 Gauss-probit 4 2.214023e+00 14.5772553 #> 544 c12506 23173 7.671714e-05 exponential 3 -2.509837e-03 NA #> 546 c12544 23346 1.722105e-03 linear 2 1.861575e-01 NA #> 547 c12572 23468 6.773752e-04 exponential 3 1.083771e-02 NA #> 549 c12576 23487 9.267247e-03 log-Gauss-probit 4 1.021352e+00 4.4432065 #> 551 c12705 23819 9.933717e-03 exponential 3 -1.751567e+00 NA #> 552 c12781 24007 1.047802e-03 linear 2 4.825735e-01 NA #> 553 c12927 24362 7.217499e-04 linear 2 3.109088e-01 NA #> 554 c13186 25095 5.598806e-03 exponential 3 -3.355018e-04 NA #> 556 c13243 25350 9.139692e-03 exponential 3 -6.088131e-01 NA #> 558 c13270 25470 5.267827e-03 linear 2 1.432054e-01 NA #> 559 c13277 25500 2.669356e-04 Gauss-probit 4 2.594431e+00 10.6317327 #> 561 c13297 25589 4.131109e-04 exponential 3 -6.832444e-03 NA #> 562 c13297 25589 4.131109e-04 exponential 3 -6.832444e-03 NA #> 565 c13517 26538 8.227406e-04 exponential 3 7.571071e-01 NA #> 566 c13525 26574 3.237246e-03 exponential 3 -1.128907e+00 NA #> 567 c13542 26650 7.852988e-04 exponential 3 2.600645e-03 NA #> 569 c13574 26794 1.921858e-03 linear 2 2.317066e-01 NA #> 571 c13596 26891 2.014638e-03 Gauss-probit 4 2.094345e+00 2.6618273 #> 572 c13598 26896 4.965270e-06 linear 2 2.958897e-01 NA #> 573 c13605 26931 1.210717e-03 linear 2 1.951699e-01 NA #> 574 c13674 27161 2.365368e-03 exponential 3 -6.072924e-01 NA #> 575 c13764 27378 4.475730e-03 exponential 3 5.093825e-03 NA #> 577 c13825 27530 7.036682e-03 Gauss-probit 4 2.375172e+00 6.8208872 #> 579 c14005 27954 6.155794e-03 linear 2 4.216192e-01 NA #> 580 c14005 27954 6.155794e-03 linear 2 4.216192e-01 NA #> 581 c14237 28676 1.538888e-04 log-Gauss-probit 4 7.541007e-01 7.8537519 #> 583 c14363 29213 1.557209e-05 exponential 3 2.253639e-02 NA #> 585 c14423 29467 4.543972e-03 exponential 3 -1.604455e-02 NA #> 586 c14431 29501 2.619248e-03 log-Gauss-probit 4 7.955319e-01 5.9474858 #> 587 c14431 29501 2.619248e-03 log-Gauss-probit 4 7.955319e-01 5.9474858 #> 588 c14618 30291 2.083712e-03 Gauss-probit 5 3.713824e+00 16.5075720 #> 590 c15068 31336 1.093408e-04 linear 2 5.374968e-01 NA #> 591 c15455 32647 2.235788e-06 exponential 3 9.915016e-03 NA #> 593 c15572 33143 7.075275e-03 exponential 3 4.643676e-02 NA #> 595 c15719 33766 3.883724e-04 linear 2 -1.905828e-01 NA #> 596 c15843 34290 1.405103e-05 Gauss-probit 4 3.067016e+00 2.6582772 #> 597 c15942 34598 4.718990e-05 exponential 3 -4.450675e-02 NA #> 598 c15975 34674 3.356863e-03 exponential 3 -8.170672e-01 NA #> 599 c15975 34674 3.356863e-03 exponential 3 -8.170672e-01 NA #> 603 c16742 36804 2.781433e-04 linear 2 3.848784e-01 NA #> 604 c16973 37787 2.825599e-04 Gauss-probit 4 8.319118e+00 -4.6684484 #> 606 c17138 38478 7.921107e-04 linear 2 1.967745e-01 NA #> 608 c17497 39284 3.028115e-04 exponential 3 -2.312831e-03 NA #> 610 c17517 39327 2.461490e-03 exponential 3 5.997141e-04 NA #> 611 c17694 39843 2.019227e-05 exponential 3 -1.826364e+00 NA #> 612 c17823 40393 6.238172e-04 exponential 3 6.779690e-04 NA #> 614 c17823 40393 6.238172e-04 exponential 3 6.779690e-04 NA #> 615 c17823 40393 6.238172e-04 exponential 3 6.779690e-04 NA #> 616 c18178 41738 1.357329e-03 linear 2 1.677462e-01 NA #> 617 c18301 42015 1.848281e-06 exponential 3 1.568251e-01 NA #> 618 c18306 42025 7.861070e-04 Gauss-probit 4 1.686325e+00 6.0977853 #> 619 c18306 42025 7.861070e-04 Gauss-probit 4 1.686325e+00 6.0977853 #> 620 c18315 42046 3.162528e-03 exponential 3 3.707484e-03 NA #> 623 c18540 42550 4.541751e-03 log-Gauss-probit 5 2.314017e-01 3.5945632 #> 624 c18686 42975 4.956775e-03 linear 2 3.126935e-01 NA #> 626 c18794 43434 3.967972e-04 exponential 3 1.595602e-01 NA #> 630 c19738 46332 6.573236e-03 exponential 3 -2.344367e-04 NA #> 631 c20526 48892 1.461304e-06 exponential 3 -3.976049e-02 NA #> 634 c20668 49255 2.423243e-04 linear 2 2.041387e-01 NA #> 641 c21327 51498 7.255831e-06 exponential 3 1.489682e+00 NA #> 642 c21366 51578 5.125046e-03 exponential 3 -6.140642e-01 NA #> 643 c21438 51724 9.104359e-03 linear 2 1.122721e-01 NA #> 644 c21442 51732 7.480590e-05 exponential 3 4.885340e-04 NA #> 645 c21452 51752 7.810285e-07 exponential 3 1.427234e+00 NA #> 646 c21521 51888 5.591900e-05 exponential 3 4.662577e-04 NA #> d e f SDres typology trend y0 #> 1 7.3435706 NA NA 0.12454183 L.dec dec 7.343571 #> 2 5.9418958 -1.6479584 NA 0.12604568 E.dec.convex dec 5.941896 #> 5 7.8591094 NA NA 0.05203245 L.dec dec 7.859109 #> 6 7.8591094 NA NA 0.05203245 L.dec dec 7.859109 #> 7 7.8591094 NA NA 0.05203245 L.dec dec 7.859109 #> 8 6.8579095 -0.3213163 NA 0.23376392 E.dec.convex dec 6.857909 #> 9 6.2092863 -0.3230281 NA 0.28968463 E.dec.convex dec 6.209286 #> 10 7.2888833 1.3087220 -0.1436781 0.07085857 lGP.U U 7.288883 #> 11 6.8685231 -0.6254549 NA 0.15031166 E.dec.convex dec 6.868523 #> 12 7.5584812 -0.2649798 NA 0.15353807 E.dec.convex dec 7.558481 #> 13 6.4674657 NA NA 0.05769085 L.dec dec 6.467466 #> 14 5.7383018 NA NA 0.11726837 L.dec dec 5.738302 #> 16 5.3301711 NA NA 0.18706049 L.inc inc 5.330171 #> 17 5.3301711 NA NA 0.18706049 L.inc inc 5.330171 #> 18 4.9684754 2.0243885 NA 0.14303183 E.inc.convex inc 4.968475 #> 19 4.9684754 2.0243885 NA 0.14303183 E.inc.convex inc 4.968475 #> 20 4.9684754 2.0243885 NA 0.14303183 E.inc.convex inc 4.968475 #> 22 4.8357744 1.4069463 0.2455556 0.10615565 GP.bell bell 4.867514 #> 23 4.8357744 1.4069463 0.2455556 0.10615565 GP.bell bell 4.867514 #> 25 5.4023281 3.3895616 NA 0.06744390 E.dec.concave dec 5.402328 #> 26 4.9173448 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 27 4.9173448 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 28 4.9173448 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 29 4.9173448 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 30 4.9173448 3.2236437 0.4530573 0.21991569 lGP.bell bell 4.917345 #> 31 5.9751431 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 32 5.9751431 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 33 5.9751431 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 34 5.9751431 0.5875851 -0.6980909 0.26535797 GP.U U 5.328111 #> 35 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 36 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 37 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 39 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 40 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 41 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 42 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 43 5.6078463 -1.2203359 NA 0.20417365 E.inc.concave inc 5.607846 #> 44 4.4698821 1.8678530 0.8450245 0.12256991 GP.bell bell 5.081883 #> 46 7.0187638 1.9509846 NA 0.05587260 E.dec.concave dec 7.018764 #> 47 7.0187638 1.9509846 NA 0.05587260 E.dec.concave dec 7.018764 #> 48 4.4916953 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 49 4.4916953 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 50 4.4916953 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 51 4.4916953 -1.7031497 NA 0.18517809 E.inc.concave inc 4.491695 #> 52 5.2157086 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 53 5.2157086 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 54 5.2157086 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 55 5.2157086 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 56 5.2157086 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 57 5.2157086 -1.5647018 NA 0.23592637 E.inc.concave inc 5.215709 #> 58 5.0555770 2.6269663 -0.6208164 0.12476292 GP.U U 4.707014 #> 59 5.5138551 NA NA 0.11224716 L.dec dec 5.513855 #> 60 5.5138551 NA NA 0.11224716 L.dec dec 5.513855 #> 61 4.6897871 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 62 4.6897871 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 63 4.6897871 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 64 4.6897871 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 65 4.6897871 1.0923496 -0.4146272 0.17573194 lGP.U U 4.689787 #> 67 6.0705233 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 68 6.0705233 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 69 6.0705233 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 70 6.0705233 -1.1113399 NA 0.14811681 E.inc.concave inc 6.070523 #> 71 6.4891514 1.5921459 NA 0.17066885 E.dec.concave dec 6.489151 #> 72 6.4891514 1.5921459 NA 0.17066885 E.dec.concave dec 6.489151 #> 73 6.4891514 1.5921459 NA 0.17066885 E.dec.concave dec 6.489151 #> 75 4.8628416 1.3892912 0.7365937 0.14844069 GP.bell bell 5.525316 #> 76 4.8628416 1.3892912 0.7365937 0.14844069 GP.bell bell 5.525316 #> 77 4.8628416 1.3892912 0.7365937 0.14844069 GP.bell bell 5.525316 #> 79 5.2165398 1.9279453 0.8281597 0.22659347 GP.bell bell 5.817903 #> 80 5.2165398 1.9279453 0.8281597 0.22659347 GP.bell bell 5.817903 #> 81 5.9083172 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 82 5.9083172 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 83 5.9083172 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 84 5.9083172 1.3702809 -0.6148327 0.16888946 GP.U U 5.436867 #> 85 10.8510453 NA NA 0.41695413 L.dec dec 10.851045 #> 86 12.4282123 -2.1982296 NA 0.28684892 E.dec.convex dec 12.428212 #> 87 12.4118704 -2.4052289 NA 0.28115971 E.dec.convex dec 12.411870 #> 88 16.4105357 1.1527615 NA 0.14530179 E.inc.convex inc 16.410536 #> 91 12.9488316 NA NA 0.20667778 L.dec dec 12.948832 #> 95 15.7725900 1.8964701 NA 0.31543564 E.inc.convex inc 15.772590 #> 99 14.6496706 NA NA 0.37973174 L.dec dec 14.649671 #> 102 9.3913476 NA NA 0.18766848 L.dec dec 9.391348 #> 103 7.2362696 -2.3752014 NA 0.30192346 E.inc.concave inc 7.236270 #> 104 7.2362696 -2.3752014 NA 0.30192346 E.inc.concave inc 7.236270 #> 106 7.3442837 -1.3800379 NA 0.50872456 E.dec.convex dec 7.344284 #> 109 15.1021881 NA NA 0.37952774 L.inc inc 15.102188 #> 110 9.8438541 3.1688140 1.4586502 0.27599070 GP.bell bell 10.298744 #> 111 6.1917399 2.6438900 -2.1169700 0.72076613 GP.U U 5.437714 #> 115 12.3018744 NA NA 0.78665664 L.inc inc 12.301874 #> 116 9.3005915 1.9823323 NA 0.31949144 E.dec.concave dec 9.300591 #> 117 17.3249844 NA NA 0.20275495 L.inc inc 17.324984 #> 120 10.9277844 1.5194196 -2.4264132 0.80589796 GP.U U 9.253000 #> 121 13.0826064 5.0536325 NA 0.21425367 E.dec.concave dec 13.082606 #> 122 14.4649621 6.7164464 NA 0.21109707 E.dec.concave dec 14.464962 #> 123 3.6813967 2.5679456 7.7390108 0.55445078 GP.bell bell 10.238924 #> 125 10.7640181 NA NA 0.23146795 L.inc inc 10.764018 #> 127 15.1467650 1.8613086 NA 0.20825290 E.inc.convex inc 15.146765 #> 128 11.2906700 4.5827956 -3.3708994 0.30754041 GP.U U 9.768606 #> 129 11.9235370 0.9067940 NA 0.44761261 E.inc.convex inc 11.923537 #> 131 3.1591011 1.7419187 3.3237027 0.41149782 GP.bell bell 5.859022 #> 132 3.1591011 1.7419187 3.3237027 0.41149782 GP.bell bell 5.859022 #> 133 8.6772914 1.7108205 -1.8696263 0.67001839 lGP.U U 8.677291 #> 134 11.8943363 2.4191926 -1.3218368 0.24812481 GP.U U 11.083641 #> 137 12.4524972 0.7590944 NA 0.28188470 E.inc.convex inc 12.452497 #> 140 11.2948498 NA NA 0.20469339 L.inc inc 11.294850 #> 141 11.5666888 1.5731637 -1.1211951 0.19687033 GP.U U 10.650748 #> 142 10.6115156 NA NA 0.27493541 L.dec dec 10.611516 #> 143 7.5281155 3.3691745 NA 0.40223575 E.inc.convex inc 7.528115 #> 147 17.8604532 1.2635774 NA 0.29022358 E.dec.concave dec 17.860453 #> 148 14.7000111 NA NA 0.44007932 L.inc inc 14.700011 #> 149 7.8301186 1.8307946 NA 0.52494332 E.inc.convex inc 7.830119 #> 150 5.6626237 NA NA 0.32569343 L.inc inc 5.662624 #> 152 12.5354341 NA NA 0.38516458 L.dec dec 12.535434 #> 156 4.2547569 NA NA 0.61055166 L.inc inc 4.254757 #> 157 8.0681583 -1.5870424 NA 0.57855035 E.inc.concave inc 8.068158 #> 158 12.3485193 NA NA 0.33573498 L.dec dec 12.348519 #> 161 11.5968176 1.8275480 -0.8682280 0.29736729 GP.U U 10.992074 #> 164 15.6410941 2.4622535 -1.0424723 0.22568934 GP.U U 15.091678 #> 165 10.5084377 NA NA 0.64924704 L.dec dec 10.508438 #> 166 11.7426152 5.4773692 1.4580135 0.29410154 lGP.bell bell 11.742615 #> 167 10.9445611 NA NA 0.20700446 L.inc inc 10.944561 #> 169 8.3293039 NA NA 0.31094519 L.inc inc 8.329304 #> 170 8.9781782 2.1869740 1.3093175 0.39364195 GP.bell bell 9.603054 #> 171 9.1119357 NA NA 0.84138808 L.dec dec 9.111936 #> 172 9.1119357 NA NA 0.84138808 L.dec dec 9.111936 #> 174 10.6370079 2.4022793 -4.0922462 0.73206856 GP.U U 7.777009 #> 178 10.2055856 2.8555099 2.3244195 0.41255116 GP.bell bell 11.551851 #> 179 13.2867883 NA NA 0.23525867 L.dec dec 13.286788 #> 180 13.9590916 NA NA 0.26918435 L.dec dec 13.959092 #> 181 13.9590916 NA NA 0.26918435 L.dec dec 13.959092 #> 183 6.7361771 -0.6498650 NA 0.38185313 E.inc.concave inc 6.736177 #> 184 6.4085538 NA NA 0.52767310 L.inc inc 6.408554 #> 185 6.4085538 NA NA 0.52767310 L.inc inc 6.408554 #> 186 8.9010369 1.0237053 NA 0.37429011 E.dec.concave dec 8.901037 #> 188 4.1680058 2.3167014 1.3407136 0.64489184 lGP.bell bell 4.168006 #> 190 11.4923353 2.5819255 1.4610375 0.34283417 GP.bell bell 12.368447 #> 193 3.0415932 1.8809478 1.7942520 0.87754767 lGP.bell bell 3.041593 #> 196 12.2786104 1.7372724 -2.0216456 0.53171016 lGP.U U 12.278610 #> 197 8.8178134 NA NA 0.36412409 L.dec dec 8.817813 #> 198 10.3504426 NA NA 0.39026969 L.dec dec 10.350443 #> 199 10.2773638 NA NA 0.38834605 L.inc inc 10.277364 #> 200 10.2773638 NA NA 0.38834605 L.inc inc 10.277364 #> 201 9.6540953 1.9560222 -2.1370159 1.14939830 lGP.U U 9.654095 #> 202 9.6540953 1.9560222 -2.1370159 1.14939830 lGP.U U 9.654095 #> 203 9.6540953 1.9560222 -2.1370159 1.14939830 lGP.U U 9.654095 #> 205 13.7221921 2.8275360 -0.5902918 0.20207860 lGP.U U 13.722192 #> 207 -5.1882517 0.0000000 4.6027866 0.51206355 GP.bell bell 3.199754 #> 208 0.7526332 2.5547012 8.4153150 0.55821268 GP.bell bell 8.467177 #> 211 11.6144651 2.6085834 -0.6474606 0.24250502 lGP.U U 11.614465 #> 214 7.7688166 2.5959623 -1.7181058 0.70975203 lGP.U U 7.768817 #> 215 12.6184168 1.0550836 NA 0.85708282 E.dec.concave dec 12.618417 #> 217 10.6857590 NA NA 0.31873117 L.dec dec 10.685759 #> 218 13.3308965 2.0807712 -2.0437685 0.67930675 lGP.U U 13.330896 #> 220 4.3278401 NA NA 0.64244947 L.inc inc 4.327840 #> 221 23.8696589 0.0000000 -8.8575210 0.31200738 GP.U U 10.754626 #> 222 11.3869012 NA NA 0.25848359 L.dec dec 11.386901 #> 225 11.7554052 1.1188963 NA 0.13641264 E.dec.concave dec 11.755405 #> 228 7.7233560 2.5397977 5.2069816 0.18596550 GP.bell bell 12.616033 #> 229 10.8961840 NA NA 0.27093544 L.dec dec 10.896184 #> 232 -2.6304333 0.0000000 3.7693124 0.45467691 GP.bell bell 3.902563 #> 233 -2.6304333 0.0000000 3.7693124 0.45467691 GP.bell bell 3.902563 #> 235 11.8496013 1.9585039 0.8349853 0.42763624 lGP.bell bell 11.849601 #> 236 10.9187587 0.8633870 NA 0.30698381 E.inc.convex inc 10.918759 #> 237 15.7513805 1.2089428 NA 0.43153316 E.dec.concave dec 15.751380 #> 238 15.7513805 1.2089428 NA 0.43153316 E.dec.concave dec 15.751380 #> 240 15.7513805 1.2089428 NA 0.43153316 E.dec.concave dec 15.751380 #> 242 5.6176186 NA NA 0.71745130 L.inc inc 5.617619 #> 243 11.9083638 2.7394740 1.2475547 0.17108221 GP.bell bell 12.737821 #> 245 8.3098306 1.7832021 NA 0.47444618 E.inc.convex inc 8.309831 #> 246 8.3098306 1.7832021 NA 0.47444618 E.inc.convex inc 8.309831 #> 248 6.9999200 NA NA 0.48572253 L.inc inc 6.999920 #> 251 17.9267166 NA NA 0.19130163 L.inc inc 17.926717 #> 252 17.9267166 NA NA 0.19130163 L.inc inc 17.926717 #> 253 11.5528527 1.7586717 0.9857557 0.27811862 lGP.bell bell 11.552853 #> 255 4.1240469 0.9672252 NA 1.22904641 E.inc.convex inc 4.124047 #> 256 11.2738979 -2.4815554 NA 0.24110024 E.dec.convex dec 11.273898 #> 257 11.1766179 0.9162819 NA 0.22747339 E.dec.concave dec 11.176618 #> 259 10.9147353 NA NA 0.16490391 L.inc inc 10.914735 #> 260 6.4933089 -1.5366759 NA 0.65031263 E.dec.convex dec 6.493309 #> 263 10.5032644 NA NA 0.29248928 L.inc inc 10.503264 #> 265 9.5564865 NA NA 0.18002511 L.inc inc 9.556486 #> 266 5.0551521 -0.5889109 NA 0.72440497 E.dec.convex dec 5.055152 #> 270 29.7823352 0.0000000 -13.8372731 0.77132816 GP.U U 8.816835 #> 272 5.1502968 NA NA 0.49677570 L.inc inc 5.150297 #> 274 13.0468298 -2.5200214 NA 0.13144425 E.dec.convex dec 13.046830 #> 275 11.0973433 2.0754574 NA 0.27234802 E.inc.convex inc 11.097343 #> 276 6.9630481 -2.0722462 NA 0.44005180 E.dec.convex dec 6.963048 #> 277 9.0129913 NA NA 0.28336282 L.inc inc 9.012991 #> 279 6.6904034 1.2899867 NA 0.25309746 E.dec.concave dec 6.690403 #> 280 9.2723423 1.2974934 NA 0.27951792 E.dec.concave dec 9.272342 #> 282 13.4155278 -2.2148393 NA 0.40243907 E.dec.convex dec 13.415528 #> 285 9.3830167 1.9563733 -1.1542378 0.23896903 GP.U U 8.502959 #> 286 3.6770965 NA NA 0.64907732 L.inc inc 3.677097 #> 287 8.2045728 NA NA 0.33406887 L.dec dec 8.204573 #> 288 8.2045728 NA NA 0.33406887 L.dec dec 8.204573 #> 291 4.6285396 NA NA 0.44374643 L.dec dec 4.628540 #> 293 9.8243533 1.9682607 NA 0.21543421 E.inc.convex inc 9.824353 #> 295 8.0476185 NA NA 0.25867705 L.dec dec 8.047618 #> 296 12.4697768 3.3599518 NA 0.26868859 E.dec.concave dec 12.469777 #> 297 11.1166216 NA NA 0.24109408 L.dec dec 11.116622 #> 298 11.1166216 NA NA 0.24109408 L.dec dec 11.116622 #> 299 8.8001787 1.0569847 NA 0.44895951 E.dec.concave dec 8.800179 #> 301 12.5829044 NA NA 0.28459726 L.inc inc 12.582904 #> 303 9.8420421 NA NA 0.14724997 L.dec dec 9.842042 #> 305 12.9786729 0.9062525 NA 0.48389149 E.dec.concave dec 12.978673 #> 306 12.9205516 NA NA 0.29030980 L.dec dec 12.920552 #> 307 12.9205516 NA NA 0.29030980 L.dec dec 12.920552 #> 308 7.5677415 0.9950738 -0.7564927 0.23626721 GP.U U 7.073164 #> 312 12.1685832 2.0515078 0.7474307 0.31596860 lGP.bell bell 12.168583 #> 314 8.8258814 -2.6181633 NA 0.49728328 E.inc.concave inc 8.825881 #> 317 11.2206087 2.3761712 -1.7174779 0.56239213 GP.U U 10.182344 #> 320 6.0844002 3.8714820 2.6450452 0.50826185 GP.bell bell 7.183840 #> 321 6.0844002 3.8714820 2.6450452 0.50826185 GP.bell bell 7.183840 #> 322 9.8322505 -1.0676294 NA 0.23732470 E.dec.convex dec 9.832250 #> 327 4.9468313 -0.7537935 NA 0.38646039 E.dec.convex dec 4.946831 #> 328 7.4334490 1.8152385 NA 0.44755458 E.inc.convex inc 7.433449 #> 330 10.2727203 1.0777063 NA 0.24051502 E.dec.concave dec 10.272720 #> 331 9.5975067 NA NA 0.18071219 L.inc inc 9.597507 #> 332 9.5975067 NA NA 0.18071219 L.inc inc 9.597507 #> 333 7.7633888 0.2414632 0.9158559 0.31711928 GP.bell bell 8.668420 #> 336 25.8775428 0.0000000 -11.8133493 1.05863155 GP.U U 4.430464 #> 338 10.3977914 NA NA 0.31334720 L.inc inc 10.397791 #> 339 2.8818475 1.4831086 0.9310508 0.41140105 lGP.bell bell 2.881847 #> 340 5.4338860 2.2028885 1.3974511 0.27541245 GP.bell bell 6.426716 #> 341 6.3598648 2.1273579 -4.1079331 1.11545022 GP.U U 3.480083 #> 342 2.5445734 NA NA 0.88166215 L.inc inc 2.544573 #> 343 11.5544508 2.3238072 -0.7635036 0.23894341 GP.U U 11.184472 #> 344 10.4291831 -2.6796044 NA 0.37877170 E.dec.convex dec 10.429183 #> 346 9.6726997 NA NA 0.45436857 L.inc inc 9.672700 #> 350 5.5944775 1.7426689 NA 0.51295133 E.inc.convex inc 5.594478 #> 354 5.6099340 1.8216559 NA 0.32521096 E.inc.convex inc 5.609934 #> 357 7.4650252 1.7793179 NA 0.55020692 E.inc.convex inc 7.465025 #> 358 7.1131979 2.2776491 NA 0.30554731 E.dec.concave dec 7.113198 #> 360 7.1131979 2.2776491 NA 0.30554731 E.dec.concave dec 7.113198 #> 361 9.3779405 0.8420820 NA 0.38873834 E.inc.convex inc 9.377940 #> 362 11.5919778 2.1014401 -1.3549521 0.53260300 lGP.U U 11.591978 #> 364 10.0143263 NA NA 0.27962037 L.inc inc 10.014326 #> 365 17.9024313 NA NA 0.17878483 L.dec dec 17.902431 #> 368 13.9040590 1.3987102 NA 0.44692567 E.dec.concave dec 13.904059 #> 370 6.1183998 -2.3714972 NA 0.59801495 E.dec.convex dec 6.118400 #> 372 13.8162214 1.9596785 -3.2185534 0.39523662 GP.U U 11.147673 #> 375 11.8055777 1.3537714 NA 0.50658959 E.inc.convex inc 11.805578 #> 376 7.3479036 NA NA 0.43229217 L.dec dec 7.347904 #> 378 13.3150381 1.2295545 NA 0.21279936 E.dec.concave dec 13.315038 #> 379 9.1311118 -2.2535099 NA 0.26268812 E.inc.concave inc 9.131112 #> 380 10.5378650 0.8143290 NA 0.45401830 E.dec.concave dec 10.537865 #> 384 6.3162507 5.0891995 NA 0.46725727 E.inc.convex inc 6.316251 #> 386 10.0567043 NA NA 0.16441109 L.inc inc 10.056704 #> 387 10.0567043 NA NA 0.16441109 L.inc inc 10.056704 #> 389 5.1148212 1.4084038 NA 1.09855421 E.inc.convex inc 5.114821 #> 391 8.3714801 NA NA 0.25820328 L.dec dec 8.371480 #> 394 9.4154096 3.0079390 NA 0.20173024 E.dec.concave dec 9.415410 #> 395 10.3811233 3.1733712 NA 0.13779241 E.inc.convex inc 10.381123 #> 397 23.7778473 0.0000000 -10.9292695 0.88531156 GP.U U 6.674833 #> 399 7.6939508 1.5471002 0.6661478 0.19149575 GP.bell bell 8.207650 #> 401 8.3205953 2.3566612 1.6122935 0.35325412 GP.bell bell 9.340510 #> 404 13.0053551 1.6480345 NA 0.24599239 E.dec.concave dec 13.005355 #> 405 4.8721779 NA NA 0.43574679 L.inc inc 4.872178 #> 408 12.7509474 2.2781947 -2.6699053 0.24834756 GP.U U 10.429737 #> 409 8.9310280 3.3853270 NA 0.34811954 E.inc.convex inc 8.931028 #> 410 10.8334321 1.3088554 NA 0.28990436 E.dec.concave dec 10.833432 #> 411 14.5173033 1.8299680 -2.9355534 1.00890423 GP.U U 12.797480 #> 414 8.2742116 NA NA 0.31619541 L.dec dec 8.274212 #> 416 15.2527459 2.3845901 -1.0454945 0.25908843 GP.U U 14.589230 #> 419 8.3222889 0.8805994 NA 0.48194682 E.inc.convex inc 8.322289 #> 420 8.4387577 1.1156241 NA 0.37889392 E.inc.convex inc 8.438758 #> 422 3.9580970 NA NA 0.87902801 L.inc inc 3.958097 #> 427 9.9736456 2.3940384 -4.7016243 0.59130862 GP.U U 6.459487 #> 428 9.9736456 2.3940384 -4.7016243 0.59130862 GP.U U 6.459487 #> 430 7.5408033 1.2173378 NA 0.27550558 E.inc.convex inc 7.540803 #> 433 11.7688844 1.0415715 NA 0.21113446 E.dec.concave dec 11.768884 #> 437 7.6097461 NA NA 0.30468219 L.dec dec 7.609746 #> 439 3.4938885 0.8961284 NA 0.99563693 E.inc.convex inc 3.493888 #> 441 3.4938885 0.8961284 NA 0.99563693 E.inc.convex inc 3.493888 #> 443 7.8034297 NA NA 0.65170810 L.inc inc 7.803430 #> 445 12.9287491 -1.3776799 NA 0.29718879 E.dec.convex dec 12.928749 #> 447 9.8736895 1.1503631 NA 0.61355815 E.inc.convex inc 9.873690 #> 450 11.4665408 NA NA 0.24072663 L.dec dec 11.466541 #> 453 11.2188707 1.9158332 0.6101410 0.15859610 GP.bell bell 11.591422 #> 454 11.2188707 1.9158332 0.6101410 0.15859610 GP.bell bell 11.591422 #> 457 9.9160221 1.0820381 NA 0.43570777 E.dec.concave dec 9.916022 #> 458 9.9160221 1.0820381 NA 0.43570777 E.dec.concave dec 9.916022 #> 460 6.1955178 NA NA 0.64907510 L.dec dec 6.195518 #> 461 11.5983183 NA NA 0.24519679 L.inc inc 11.598318 #> 464 11.5369318 NA NA 0.28696334 L.dec dec 11.536932 #> 465 7.6420694 1.9968840 NA 0.29198344 E.inc.convex inc 7.642069 #> 466 13.6080352 2.3215767 NA 0.23573761 E.dec.concave dec 13.608035 #> 467 11.5233287 0.8734262 NA 0.54421378 E.dec.concave dec 11.523329 #> 468 8.8190315 0.7225067 NA 0.17256242 E.dec.concave dec 8.819032 #> 469 6.1391526 3.1550982 -1.2353559 0.46567636 lGP.U U 6.139153 #> 470 15.5969330 NA NA 0.18311723 L.inc inc 15.596933 #> 471 7.1664552 1.8676771 NA 0.53449306 E.dec.concave dec 7.166455 #> 473 14.2054782 NA NA 0.46399131 L.inc inc 14.205478 #> 475 12.7384191 1.6671166 NA 0.51466168 E.inc.convex inc 12.738419 #> 476 7.6278041 -2.0128076 NA 0.32054437 E.inc.concave inc 7.627804 #> 479 18.4632294 NA NA 0.20247498 L.dec dec 18.463229 #> 480 18.4632294 NA NA 0.20247498 L.dec dec 18.463229 #> 481 11.7480225 2.3988301 -1.6451186 0.34020978 GP.U U 10.588494 #> 482 5.8879508 3.3358199 NA 0.44022250 E.inc.convex inc 5.887951 #> 483 9.3907657 NA NA 0.35251384 L.inc inc 9.390766 #> 486 12.5673623 2.0444975 -2.6933806 0.70145770 GP.U U 10.540594 #> 488 4.4751185 -1.3700499 NA 0.65855748 E.inc.concave inc 4.475119 #> 490 8.3079954 -2.3133165 NA 0.42499412 E.dec.convex dec 8.307995 #> 491 8.3079954 -2.3133165 NA 0.42499412 E.dec.convex dec 8.307995 #> 492 10.3737271 1.3213539 NA 0.46734668 E.inc.convex inc 10.373727 #> 493 16.8203565 NA NA 0.24465590 L.dec dec 16.820356 #> 494 3.5216604 1.5925756 1.6525193 0.71149572 lGP.bell bell 3.521660 #> 495 11.1141357 3.0740721 -0.5950246 0.20511033 lGP.U U 11.114136 #> 496 6.9911665 NA NA 0.68942828 L.inc inc 6.991166 #> 498 8.6675517 NA NA 0.23785120 L.inc inc 8.667552 #> 499 8.6675517 NA NA 0.23785120 L.inc inc 8.667552 #> 500 8.8683481 2.2554363 -6.3717984 0.76503106 GP.U U 3.081979 #> 501 11.8312797 2.0127634 -2.1716042 0.35429372 GP.U U 10.482349 #> 502 6.9247347 NA NA 0.53935448 L.inc inc 6.924735 #> 504 5.1107369 2.3273524 NA 0.49190939 E.inc.convex inc 5.110737 #> 505 14.3133366 2.0401909 -2.2874180 0.47711867 GP.U U 12.552174 #> 509 11.6564745 0.8059689 NA 0.34163752 E.dec.concave dec 11.656474 #> 513 2.8568072 1.2403251 NA 0.72920067 E.inc.convex inc 2.856807 #> 514 2.8568072 1.2403251 NA 0.72920067 E.inc.convex inc 2.856807 #> 515 3.7375623 NA NA 0.66526211 L.inc inc 3.737562 #> 517 4.4669724 0.9312615 NA 1.17808168 E.inc.convex inc 4.466972 #> 518 7.7051920 2.7907400 -1.3259250 0.46062229 lGP.U U 7.705192 #> 519 11.8729554 1.6928649 NA 0.36955986 E.inc.convex inc 11.872955 #> 520 5.0903345 1.5288013 NA 0.24985468 E.inc.convex inc 5.090334 #> 521 3.2368169 1.7099772 1.2535246 0.47283855 GP.bell bell 3.614205 #> 522 11.2851985 0.7767753 NA 0.58488759 E.inc.convex inc 11.285199 #> 523 11.1769600 2.5412298 NA 0.24873284 E.dec.concave dec 11.176960 #> 525 3.3180250 1.2611850 0.8999648 0.48348498 lGP.bell bell 3.318025 #> 526 2.9577864 NA NA 0.54268515 L.inc inc 2.957786 #> 527 5.8568962 1.4500622 -1.9460898 0.98274605 lGP.U U 5.856896 #> 528 10.1316456 NA NA 0.28742947 L.inc inc 10.131646 #> 531 6.8771861 0.9719432 NA 0.65133666 E.dec.concave dec 6.877186 #> 534 11.7948434 1.8803822 -1.7229567 0.53837776 GP.U U 10.564068 #> 535 6.6915012 0.9153870 NA 0.99255522 E.inc.convex inc 6.691501 #> 540 4.8284296 2.0830008 NA 0.73289707 E.inc.convex inc 4.828430 #> 542 4.9989594 1.5821319 -1.2090070 0.51207828 lGP.U U 4.998959 #> 543 14.5772553 2.2981412 -1.5915164 0.53119435 GP.U U 13.648609 #> 544 12.2812084 1.0480454 NA 0.31467644 E.dec.concave dec 12.281208 #> 546 2.4794339 NA NA 0.52220615 L.inc inc 2.479434 #> 547 2.6824742 1.2062259 NA 0.82653462 E.inc.convex inc 2.682474 #> 549 4.4432065 1.5956017 -2.0801672 1.01087060 lGP.U U 4.443206 #> 551 3.9219997 -1.4167347 NA 0.80658748 E.inc.concave inc 3.922000 #> 552 2.9837278 NA NA 1.23243335 L.inc inc 2.983728 #> 553 3.2938960 NA NA 0.78506590 L.inc inc 3.293896 #> 554 4.7820339 0.7862608 NA 0.62138156 E.dec.concave dec 4.782034 #> 556 8.9741606 -0.8263232 NA 0.28582133 E.inc.concave inc 8.974161 #> 558 6.9317875 NA NA 0.37680979 L.inc inc 6.931787 #> 559 10.6317327 1.8611164 -1.6585672 0.40393199 GP.U U 9.349429 #> 561 6.2030766 1.3022662 NA 0.31243642 E.dec.concave dec 6.203077 #> 562 6.2030766 1.3022662 NA 0.31243642 E.dec.concave dec 6.203077 #> 565 8.8032367 -2.4187724 NA 0.22248510 E.dec.convex dec 8.803237 #> 566 6.2678143 -1.4799430 NA 0.46551188 E.inc.concave inc 6.267814 #> 567 4.1003451 0.9665863 NA 0.83033880 E.inc.convex inc 4.100345 #> 569 5.2877014 NA NA 0.65093913 L.inc inc 5.287701 #> 571 2.6618273 1.8081921 1.5558322 0.59126123 GP.bell bell 3.733593 #> 572 4.9407625 NA NA 0.47588238 L.inc inc 4.940763 #> 573 11.7181620 NA NA 0.49107177 L.inc inc 11.718162 #> 574 6.8551980 -0.3848874 NA 0.24733881 E.inc.concave inc 6.855198 #> 575 6.0290060 1.1910100 NA 0.60886000 E.inc.convex inc 6.029006 #> 577 6.8208872 2.2045847 -1.9822962 0.65642859 GP.U U 5.532363 #> 579 2.3530681 NA NA 1.29727813 L.inc inc 2.353068 #> 580 2.3530681 NA NA 1.29727813 L.inc inc 2.353068 #> 581 7.8537519 3.5301455 -1.0908882 0.28620983 lGP.U U 7.853752 #> 583 16.1210861 1.6875592 NA 0.25581662 E.inc.convex inc 16.121086 #> 585 6.9329387 1.5675702 NA 0.50617193 E.dec.concave dec 6.932939 #> 586 5.9474858 2.1464196 -1.2818324 0.48811443 lGP.U U 5.947486 #> 587 5.9474858 2.1464196 -1.2818324 0.48811443 lGP.U U 5.947486 #> 588 12.9839302 5.0595658 -2.4186880 0.32894425 GP.U U 12.332673 #> 590 11.8293086 NA NA 1.20927918 L.inc inc 11.829309 #> 591 8.0048304 1.2968103 NA 0.33827142 E.inc.convex inc 8.004830 #> 593 2.7094606 1.8595387 NA 0.73835019 E.inc.convex inc 2.709461 #> 595 7.5037249 NA NA 0.40049288 L.dec dec 7.503725 #> 596 2.6582772 2.6722696 4.3931116 0.45650946 GP.bell bell 5.663831 #> 597 5.2667162 1.7742492 NA 0.45397783 E.dec.concave dec 5.266716 #> 598 8.2084258 -2.0754915 NA 0.30806244 E.inc.concave inc 8.208426 #> 599 8.2084258 -2.0754915 NA 0.30806244 E.inc.concave inc 8.208426 #> 603 3.3517045 NA NA 0.78185445 L.inc inc 3.351704 #> 604 -4.6684484 2.7306565 14.1086676 0.29870979 GP.bell bell 8.700290 #> 606 15.0002172 NA NA 0.53837112 L.inc inc 15.000217 #> 608 7.1764685 1.0238480 NA 0.42350852 E.dec.concave dec 7.176469 #> 610 2.7836841 0.8429907 NA 0.60237813 E.inc.convex inc 2.783684 #> 611 8.7976822 -1.8935670 NA 0.47334309 E.inc.concave inc 8.797682 #> 612 9.5804433 0.9456406 NA 0.19945603 E.inc.convex inc 9.580443 #> 614 9.5804433 0.9456406 NA 0.19945603 E.inc.convex inc 9.580443 #> 615 9.5804433 0.9456406 NA 0.19945603 E.inc.convex inc 9.580443 #> 616 5.1115295 NA NA 0.40444003 L.inc inc 5.111529 #> 617 8.3996060 3.2698204 NA 0.21379500 E.inc.convex inc 8.399606 #> 618 6.0977853 2.1816572 -3.1142421 1.03760938 GP.U U 4.749125 #> 619 6.0977853 2.1816572 -3.1142421 1.03760938 GP.U U 4.749125 #> 620 2.6041848 1.0531930 NA 0.75747748 E.inc.convex inc 2.604185 #> 623 2.7776193 1.5555118 1.7762050 0.50391975 lGP.bell bell 2.777619 #> 624 5.3838933 NA NA 0.89586768 L.inc inc 5.383893 #> 626 5.0070495 2.6958540 NA 0.52415863 E.inc.convex inc 5.007050 #> 630 8.2625336 0.7222172 NA 0.96123411 E.dec.concave dec 8.262534 #> 631 11.8824074 1.7807252 NA 0.31764055 E.dec.concave dec 11.882407 #> 634 6.5549589 NA NA 0.44384894 L.inc inc 6.554959 #> 641 13.3495350 -1.8926198 NA 0.34219661 E.dec.convex dec 13.349535 #> 642 15.6634034 -2.0276704 NA 0.22497205 E.inc.concave inc 15.663403 #> 643 17.0456861 NA NA 0.30817653 L.inc inc 17.045686 #> 644 15.5325419 0.9010627 NA 0.14782250 E.inc.convex inc 15.532542 #> 645 12.3446582 -2.4650317 NA 0.25729560 E.dec.convex dec 12.344658 #> 646 15.7385354 0.8902771 NA 0.15980885 E.inc.convex inc 15.738535 #> yrange maxychange xextrem yextrem BMD.zSD BMR.zSD BMD.xfold #> 1 0.4346034 0.4346034 NA NA 2.2237393 7.219029 NA #> 2 0.4556672 0.4556672 NA NA 0.5279668 5.815850 NA #> 5 0.3498078 0.3498078 NA NA 1.1542677 7.807077 NA #> 6 0.3498078 0.3498078 NA NA 1.1542677 7.807077 NA #> 7 0.3498078 0.3498078 NA NA 1.1542677 7.807077 NA #> 8 0.6010677 0.6010677 NA NA 0.1582542 6.624146 NA #> 9 0.6721023 0.6721023 NA NA 0.1821546 5.919602 0.8318574 #> 10 0.1912790 0.1912790 1.4588204 7.097604 0.7315304 7.218025 NA #> 11 0.4520636 0.4520636 NA NA 0.2528186 6.718211 NA #> 12 0.4392508 0.4392508 NA NA 0.1139635 7.404943 NA #> 13 0.1503987 0.1503987 NA NA 2.9766289 6.409775 NA #> 14 0.4491366 0.4491366 NA NA 2.0261156 5.621033 NA #> 16 0.4771993 0.4771993 NA NA 3.0418937 5.517232 NA #> 17 0.4771993 0.4771993 NA NA 3.0418937 5.517232 NA #> 18 0.3520280 0.3520280 NA NA 5.9997652 5.111507 NA #> 19 0.3520280 0.3520280 NA NA 5.9997652 5.111507 NA #> 20 0.3520280 0.3520280 NA NA 5.9997652 5.111507 NA #> 22 0.2455556 0.2138158 1.4069463 5.081330 0.6597819 4.973670 NA #> 23 0.2455556 0.2138158 1.4069463 5.081330 0.6597819 4.973670 NA #> 25 0.2833937 0.2833937 NA NA 3.8465968 5.334884 NA #> 26 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 27 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 28 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 29 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 30 0.4530573 0.4530573 3.2236437 5.370402 1.5389057 5.137260 NA #> 31 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 32 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 33 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 34 0.6980825 0.6470232 0.5875851 5.277052 2.2442671 5.593469 3.4561250 #> 35 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 36 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 37 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 39 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 40 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 41 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 42 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 43 0.3960491 0.3960491 NA NA 0.8821213 5.812020 NA #> 44 0.8109383 0.5779147 1.8678530 5.314907 0.6370853 5.204453 6.6295891 #> 46 0.2284144 0.2284144 NA NA 5.1225625 6.962891 NA #> 47 0.2284144 0.2284144 NA NA 5.1225625 6.962891 NA #> 48 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 49 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 50 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 51 0.3629299 0.3629299 NA NA 1.1972137 4.676873 NA #> 52 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 53 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 54 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 55 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 56 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 57 0.5113868 0.5113868 NA NA 0.9586777 5.451635 NA #> 58 0.5522909 0.2800374 2.6269663 4.434761 0.8261462 4.582251 NA #> 59 0.3939227 0.3939227 NA NA 2.2111898 5.401608 NA #> 60 0.3939227 0.3939227 NA NA 2.2111898 5.401608 NA #> 61 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 62 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 63 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 64 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 65 0.4981938 0.3405782 1.0482802 4.349209 0.7805309 4.514055 NA #> 67 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 68 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 69 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 70 0.2683749 0.2683749 NA NA 0.8908528 6.218640 NA #> 71 0.5966741 0.5966741 NA NA 5.7972864 6.318483 NA #> 72 0.5966741 0.5966741 NA NA 5.7972864 6.318483 NA #> 73 0.5966741 0.5966741 NA NA 5.7972864 6.318483 NA #> 75 0.6573939 0.5832748 1.3892912 5.599435 3.9480418 5.376875 7.2728900 #> 76 0.6573939 0.5832748 1.3892912 5.599435 3.9480418 5.376875 7.2728900 #> 77 0.6573939 0.5832748 1.3892912 5.599435 3.9480418 5.376875 7.2728900 #> 79 0.7838623 0.5570660 1.9279453 6.044699 1.8746151 6.044497 NA #> 80 0.7838623 0.5570660 1.9279453 6.044699 1.8746151 6.044497 NA #> 81 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 82 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 83 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 84 0.6129220 0.4695398 1.3702809 5.293484 3.6094654 5.605756 NA #> 85 1.4451322 1.4451322 NA NA 1.9131972 10.434091 4.9790105 #> 86 1.4260064 1.4260064 NA NA 0.4667565 12.141363 3.8804658 #> 87 1.3187707 1.3187707 NA NA 0.5356979 12.130711 5.1282913 #> 88 0.5667240 0.5667240 NA NA 5.0725915 16.555837 NA #> 91 0.9321607 0.9321607 NA NA 1.4702189 12.742154 NA #> 95 1.5755952 1.5755952 NA NA 3.7973745 16.088026 NA #> 99 0.8397821 0.8397821 NA NA 2.9983980 14.269939 NA #> 102 0.5072807 0.5072807 NA NA 2.4531384 9.203679 NA #> 103 0.7254029 0.7254029 NA NA 1.1767628 7.538193 6.5436013 #> 104 0.7254029 0.7254029 NA NA 1.1767628 7.538193 6.5436013 #> 106 1.5164596 1.5164596 NA NA 0.5582910 6.835559 0.9033421 #> 109 1.0235083 1.0235083 NA NA 2.4588452 15.481716 NA #> 110 1.0956824 1.0037607 3.1688140 11.302504 0.7285396 10.574734 NA #> 111 1.9146107 1.3629446 2.6438900 4.074770 1.0793925 4.716948 0.8236293 #> 115 2.8622806 2.8622806 NA NA 1.8224349 13.088531 2.8499557 #> 116 0.6953599 0.6953599 NA NA 5.1699097 8.981100 NA #> 117 0.6275920 0.6275920 NA NA 2.1422646 17.527739 NA #> 120 2.3898736 1.6382451 1.5194196 8.501371 4.0482830 10.058898 4.2241830 #> 121 1.2471032 1.2471032 NA NA 1.9341638 12.868353 NA #> 122 2.1130749 2.1130749 NA NA 1.0443056 14.253865 5.1497136 #> 123 2.6271853 1.4457016 2.5679456 11.420407 0.7339797 10.793374 1.6629870 #> 125 0.5298971 0.5298971 NA NA 2.8965322 10.995486 NA #> 127 1.1100339 1.1100339 NA NA 3.7319420 15.355018 NA #> 128 1.1159414 0.6118908 2.9011529 9.264556 1.2030629 9.461066 NA #> 129 1.6230068 1.6230068 NA NA 5.4645388 12.371150 6.3516074 #> 131 2.6773137 2.0535315 1.7419187 6.482804 0.7603447 6.270519 1.3328628 #> 132 2.6773137 2.0535315 1.7419187 6.482804 0.7603447 6.270519 1.3328628 #> 133 1.8696263 1.8696263 1.7108205 6.807665 0.5760685 8.007273 0.6673274 #> 134 1.0214920 0.5111417 2.4191926 10.572500 0.7894809 10.835516 NA #> 137 0.7231739 0.7231739 NA NA 5.9160090 12.734382 NA #> 140 0.6355020 0.6355020 NA NA 2.1358264 11.499543 NA #> 141 0.9825269 0.7772726 1.5731637 10.445494 1.2700689 10.453878 NA #> 142 0.9032233 0.9032233 NA NA 2.0184340 10.336580 NA #> 143 2.6423399 2.6423399 NA NA 2.2280425 7.930351 3.4134579 #> 147 1.3202534 1.3202534 NA NA 4.7401393 17.570230 NA #> 148 1.0314101 1.0314101 NA NA 2.8292974 15.140090 NA #> 149 1.6382243 1.6382243 NA NA 4.6483666 8.355062 5.3321567 #> 150 1.0445220 1.0445220 NA NA 2.0676187 5.988317 3.5948366 #> 152 0.9374300 0.9374300 NA NA 2.7244982 12.150270 NA #> 156 1.4365255 1.4365255 NA NA 2.8183056 4.865309 1.9639952 #> 157 1.6606248 1.6606248 NA NA 0.6668007 8.646709 1.0329475 #> 158 1.3602197 1.3602197 NA NA 1.6366905 12.012784 6.0198387 #> 161 0.7968439 0.5333591 1.8275480 10.728590 4.9242758 11.289442 NA #> 164 0.8762376 0.4930563 2.4622535 14.598622 0.7873949 14.865989 NA #> 165 2.8436971 2.8436971 NA NA 1.5139296 9.859191 2.4503823 #> 166 1.0312987 0.7141166 2.5296882 12.059797 2.0821730 12.036717 NA #> 167 0.7189896 0.7189896 NA NA 1.9091328 11.151566 NA #> 169 1.4213819 1.4213819 NA NA 1.4506148 8.640249 3.8857687 #> 170 1.2475819 0.6844412 2.1869740 10.287496 0.9126300 9.996696 NA #> 171 2.4354311 2.4354311 NA NA 2.2908652 8.270548 2.4809261 #> 172 2.4354311 2.4354311 NA NA 2.2908652 8.270548 2.4809261 #> 174 2.7438223 1.5115754 2.4022793 6.544762 0.9531756 7.044940 1.0251183 #> 178 1.4297124 0.9781543 2.8555099 12.530005 0.8151722 11.964402 NA #> 179 0.8849830 0.8849830 NA NA 1.7627459 13.051530 NA #> 180 0.7551267 0.7551267 NA NA 2.3637907 13.689907 NA #> 181 0.7551267 0.7551267 NA NA 2.3637907 13.689907 NA #> 183 1.2492267 1.2492267 NA NA 0.2370668 7.118030 0.5035207 #> 184 1.3030102 1.3030102 NA NA 2.6853208 6.936227 3.2613039 #> 185 1.3030102 1.3030102 NA NA 2.6853208 6.936227 3.2613039 #> 186 0.9204590 0.9204590 NA NA 5.7121222 8.526747 6.5967242 #> 188 1.3407136 1.3407136 2.3167014 5.508719 1.1232833 4.812898 0.9282272 #> 190 1.0456724 0.5849261 2.5819255 12.953373 1.0452152 12.711281 NA #> 193 1.7942520 1.7942520 1.8809478 4.835845 0.8218053 3.919141 0.5103535 #> 196 2.0216456 2.0216456 1.7372724 10.256965 0.6687799 11.746900 0.9695193 #> 197 0.7772864 0.7772864 NA NA 3.1063283 8.453689 NA #> 198 0.8961317 0.8961317 NA NA 2.8878327 9.960173 NA #> 199 0.8369527 0.8369527 NA NA 3.0767840 10.665710 NA #> 200 0.8369527 0.8369527 NA NA 3.0767840 10.665710 NA #> 201 2.1370159 2.1370159 1.9560222 7.517079 0.9072409 8.504697 0.8197933 #> 202 2.1370159 2.1370159 1.9560222 7.517079 0.9072409 8.504697 0.8197933 #> 203 2.1370159 2.1370159 1.9560222 7.517079 0.9072409 8.504697 0.8197933 #> 205 0.5902918 0.5902918 2.8275360 13.131900 0.6768404 13.520114 NA #> 207 1.3762816 0.9567808 2.1064523 4.156535 0.6470138 3.711818 0.3740702 #> 208 1.6710504 0.9702795 2.5547012 9.167948 1.4222300 9.025390 6.4602636 #> 211 0.6474606 0.6474606 2.6085834 10.967005 0.8877879 11.371960 NA #> 214 1.7181058 1.7181058 2.5959623 6.050711 0.8824251 7.059065 0.9338753 #> 215 2.2364894 2.2364894 NA NA 5.6222008 11.761334 6.0286569 #> 217 0.8034587 0.8034587 NA NA 2.6305103 10.367028 NA #> 218 2.0437685 2.0437685 2.0807712 11.287128 0.6438946 12.651590 1.0021942 #> 220 2.0758668 2.0758668 NA NA 2.0521944 4.970290 1.3824542 #> 221 1.0880222 0.6435345 2.6519769 10.111091 0.7392714 10.442619 NA #> 222 0.7335175 0.7335175 NA NA 2.3366923 11.128418 NA #> 225 0.5248384 0.5248384 NA NA 5.1318609 11.618993 NA #> 228 0.7767741 0.4624698 2.5397977 12.930338 0.9318105 12.801999 NA #> 229 0.9039016 0.9039016 NA NA 1.9875757 10.625249 NA #> 232 0.9872534 0.6272225 2.2864089 4.529785 1.0599593 4.357240 0.8573274 #> 233 0.9872534 0.6272225 2.2864089 4.529785 1.0599593 4.357240 0.8573274 #> 235 0.8349853 0.8349853 1.9585039 12.684587 0.8218319 12.277238 NA #> 236 0.9147026 0.9147026 NA NA 5.6891398 11.225742 NA #> 237 1.5763081 1.5763081 NA NA 5.0780515 15.319847 6.6301060 #> 238 1.5763081 1.5763081 NA NA 5.0780515 15.319847 6.6301060 #> 240 1.5763081 1.5763081 NA NA 5.0780515 15.319847 6.6301060 #> 242 1.5864088 1.5864088 NA NA 2.9988610 6.335070 2.3480977 #> 243 0.7000964 0.4180970 2.7394740 13.155918 0.7253125 12.908904 NA #> 245 0.9360331 0.9360331 NA NA 5.4609223 8.784277 6.4241875 #> 246 0.9360331 0.9360331 NA NA 5.4609223 8.784277 6.4241875 #> 248 1.1881624 1.1881624 NA NA 2.7107625 7.485642 3.9065761 #> 251 0.6196609 0.6196609 NA NA 2.0471215 18.118018 NA #> 252 0.6196609 0.6196609 NA NA 2.0471215 18.118018 NA #> 253 0.9857557 0.9857557 1.7586717 12.538608 0.5508093 11.830971 NA #> 255 3.5343401 3.5343401 NA NA 5.6112409 5.353093 4.5608152 #> 256 0.7471482 0.7471482 NA NA 0.8865055 11.032798 NA #> 257 1.2440698 1.2440698 NA NA 5.0770781 10.949145 6.5328956 #> 259 0.5716752 0.5716752 NA NA 1.9127607 11.079639 NA #> 260 1.5336580 1.5336580 NA NA 0.8327309 5.842996 0.8310632 #> 263 0.6992226 0.6992226 NA NA 2.7737899 10.795754 NA #> 265 0.8804012 0.8804012 NA NA 1.3559120 9.736512 NA #> 266 1.7011398 1.7011398 NA NA 0.3267449 4.330747 0.2076643 #> 270 2.1967634 1.1526784 2.4964781 7.664156 1.0466404 8.045507 1.2705901 #> 272 1.4379270 1.4379270 NA NA 2.2908810 5.647073 2.3750592 #> 274 0.4392427 0.4392427 NA NA 0.8198382 12.915386 NA #> 275 1.0315095 1.0315095 NA NA 4.0915465 11.369691 NA #> 276 1.5599561 1.5599561 NA NA 0.6538412 6.522996 1.1581900 #> 277 0.6744958 0.6744958 NA NA 2.7857533 9.296354 NA #> 279 1.0236475 1.0236475 NA NA 4.8512276 6.437306 6.0863878 #> 280 1.2704995 1.2704995 NA NA 4.6939228 8.992824 6.2252369 #> 282 0.9935949 0.9935949 NA NA 1.0757865 13.013089 NA #> 285 0.9088697 0.6346895 1.9563733 8.228779 1.2951527 8.263990 NA #> 286 1.5377875 1.5377875 NA NA 2.7988468 4.326174 1.5855784 #> 287 0.8865933 0.8865933 NA NA 2.4985646 7.870504 6.1363560 #> 288 0.8865933 0.8865933 NA NA 2.4985646 7.870504 6.1363560 #> 291 1.0796687 1.0796687 NA NA 2.7253570 4.184793 2.8427097 #> 293 0.5863849 0.5863849 NA NA 4.7734831 10.039788 NA #> 295 0.6009495 0.6009495 NA NA 2.8542954 7.788941 NA #> 296 1.0516373 1.0516373 NA NA 3.1885322 12.201088 NA #> 297 0.5732814 0.5732814 NA NA 2.7886737 10.875528 NA #> 298 0.5732814 0.5732814 NA NA 2.7886737 10.875528 NA #> 299 1.6474111 1.6474111 NA NA 5.2621976 8.351219 5.9699880 #> 301 0.7755726 0.7755726 NA NA 2.4332531 12.867502 NA #> 303 0.4170526 0.4170526 NA NA 2.3412265 9.694792 NA #> 305 1.3091568 1.3091568 NA NA 5.7300529 12.494781 6.6231563 #> 306 0.8942070 0.8942070 NA NA 2.1527950 12.630242 NA #> 307 0.8942070 0.8942070 NA NA 2.1527950 12.630242 NA #> 308 0.7564918 0.4945771 0.9950738 6.811249 0.7116013 6.836896 NA #> 312 0.7474307 0.7474307 2.0515078 12.916014 0.7635263 12.484552 NA #> 314 1.9400638 1.9400638 NA NA 0.7046383 9.323165 1.4207090 #> 317 1.3754663 0.6962531 2.3761712 9.503131 1.4872255 9.619952 NA #> 320 1.5861301 1.0776961 2.6022023 7.692274 2.5611897 7.692102 6.0661427 #> 321 1.5861301 1.0776961 2.6022023 7.692274 2.5611897 7.692102 6.0661427 #> 322 0.7486829 0.7486829 NA NA 0.4060345 9.594926 NA #> 327 0.9864697 0.9864697 NA NA 0.3747033 4.560371 0.5245918 #> 328 1.4292787 1.4292787 NA NA 4.6236368 7.881004 5.4871571 #> 330 1.1401586 1.1401586 NA NA 4.9624796 10.032205 6.5188896 #> 331 1.0483855 1.0483855 NA NA 1.1429980 9.778219 6.0703879 #> 332 1.0483855 1.0483855 NA NA 1.1429980 9.778219 6.0703879 #> 333 0.9156340 0.9048096 0.2414632 8.679245 1.7158581 8.351301 4.1888113 #> 336 3.1244464 2.4160958 2.3109888 2.014368 0.5585263 3.371832 0.2148246 #> 338 0.9549307 0.9549307 NA NA 2.1758702 10.711139 NA #> 339 0.9310508 0.9310508 1.4831086 3.812898 0.5958514 3.293249 0.4972954 #> 340 1.0463324 0.6417115 2.2028885 6.831337 1.0293193 6.702129 NA #> 341 3.2718424 2.0436913 2.1273579 2.251932 1.5320368 2.364633 0.3746104 #> 342 1.8857419 1.8857419 NA NA 3.1002661 3.426236 0.8947707 #> 343 0.7001329 0.3935249 2.3238072 10.790947 1.0252456 10.945529 NA #> 344 1.1746841 1.1746841 NA NA 0.9378058 10.050411 4.4938667 #> 346 1.5034330 1.5034330 NA NA 2.0040254 10.127068 4.2662140 #> 350 1.2917549 1.2917549 NA NA 5.0794294 6.107429 5.2227689 #> 354 0.9567759 0.9567759 NA NA 4.7558343 5.935145 5.6919180 #> 357 3.5130625 3.5130625 NA NA 3.5491119 8.015232 4.0271629 #> 358 2.0350385 2.0350385 NA NA 2.9236395 6.807651 4.4565040 #> 360 2.0350385 2.0350385 NA NA 2.9236395 6.807651 4.4565040 #> 361 1.0233370 1.0233370 NA NA 5.8164566 9.766679 6.5575207 #> 362 1.3549521 1.3549521 2.1014401 10.237026 0.8960631 11.059375 1.4831675 #> 364 0.7991327 0.7991327 NA NA 2.3202186 10.293947 NA #> 365 0.4818421 0.4818421 NA NA 2.4603955 17.723646 NA #> 368 2.0649632 2.0649632 NA NA 4.5338399 13.457133 6.0836994 #> 370 1.6440213 1.6440213 NA NA 0.9909542 5.520385 1.0195641 #> 372 2.1088211 1.5588160 1.9596785 10.597668 0.9547622 10.752436 5.8228192 #> 375 1.5309438 1.5309438 NA NA 5.1540813 12.312167 6.2821526 #> 376 1.0939749 1.0939749 NA NA 2.6202881 6.915611 4.4538453 #> 378 0.4899501 0.4899501 NA NA 5.6128703 13.102239 NA #> 379 0.6274954 0.6274954 NA NA 1.1382454 9.393800 NA #> 380 1.0448656 1.0448656 NA NA 5.9525599 10.083847 NA #> 384 3.3807780 3.3807780 NA NA 1.6037111 6.783508 2.0659663 #> 386 0.5490811 0.5490811 NA NA 1.9855173 10.221115 NA #> 387 0.5490811 0.5490811 NA NA 1.9855173 10.221115 NA #> 389 3.0879871 3.0879871 NA NA 5.1982036 6.213375 4.1613342 #> 391 0.7062619 0.7062619 NA NA 2.4242366 8.113277 NA #> 394 0.7998367 0.7998367 NA NA 3.3387424 9.213679 NA #> 395 1.4755404 1.4755404 NA NA 1.6108049 10.518916 5.6764686 #> 397 2.0091568 1.0916682 2.4592236 5.583165 1.3712891 5.789522 0.9112142 #> 399 0.6258898 0.4734413 1.5471002 8.360099 4.1335653 8.016154 NA #> 401 1.2548501 0.6624716 2.3566612 9.932889 0.9613178 9.693764 NA #> 404 0.8939560 0.8939560 NA NA 4.5803310 12.759363 NA #> 405 1.2111865 1.2111865 NA NA 2.3856251 5.307925 2.6674183 #> 408 1.0680948 0.7193997 2.2781947 10.081042 1.0862987 10.181390 NA #> 409 1.8924337 1.8924337 NA NA 2.5443588 9.279148 4.5849528 #> 410 1.0002044 1.0002044 NA NA 5.0301698 10.543528 NA #> 411 2.8615248 1.6457951 1.8299680 11.581750 1.1535189 11.788575 5.2775387 #> 414 0.8811392 0.8811392 NA NA 2.3795238 7.958016 6.2267455 #> 416 0.7982567 0.4162785 2.3845901 14.207251 1.1340646 14.330141 NA #> 419 1.6551785 1.6551785 NA NA 5.5456395 8.804236 6.0260053 #> 420 1.0612794 1.0612794 NA NA 5.4871921 8.817652 6.3760243 #> 422 3.5996821 3.5996821 NA NA 1.6192638 4.837125 0.7291239 #> 427 2.8125159 1.6250497 2.3940384 5.272021 0.7601546 5.868179 0.8418771 #> 428 2.8125159 1.6250497 2.3940384 5.272021 0.7601546 5.868179 0.8418771 #> 430 0.7148534 0.7148534 NA NA 5.4786411 7.816309 NA #> 433 0.8109830 0.8109830 NA NA 5.2343760 11.557750 NA #> 437 0.8412667 0.8412667 NA NA 2.4015542 7.305064 5.9981247 #> 439 2.8504330 2.8504330 NA NA 5.6894334 4.489525 4.7539040 #> 441 2.8504330 2.8504330 NA NA 5.6894334 4.489525 4.7539040 #> 443 1.9375683 1.9375683 NA NA 2.2303608 8.455138 2.6705919 #> 445 0.8217456 0.8217456 NA NA 0.6120840 12.631560 NA #> 447 1.5447062 1.5447062 NA NA 5.5743185 10.487248 6.1181961 #> 450 1.2515303 1.2515303 NA NA 1.2754451 11.225814 6.0753329 #> 453 0.5794020 0.3418123 1.9158332 11.829012 0.9001452 11.750018 NA #> 454 0.5794020 0.3418123 1.9158332 11.829012 0.9001452 11.750018 NA #> 457 0.9890126 0.9890126 NA NA 5.7470070 9.480314 NA #> 458 0.9890126 0.9890126 NA NA 5.7470070 9.480314 NA #> 460 1.7150692 1.7150692 NA NA 2.5095297 5.546443 2.3953832 #> 461 0.6495001 0.6495001 NA NA 2.5033097 11.843515 NA #> 464 0.6734981 0.6734981 NA NA 2.8253294 11.249968 NA #> 465 0.8555082 0.8555082 NA NA 4.6189505 7.934053 6.4142382 #> 466 0.5824151 0.5824151 NA NA 4.7196194 13.372298 NA #> 467 1.4382279 1.4382279 NA NA 5.7829063 10.979115 6.4375378 #> 468 0.8953279 0.8953279 NA NA 5.4417552 8.646469 6.6200856 #> 469 1.2353559 1.2353559 3.1550982 4.903797 1.3227556 5.673476 1.5114489 #> 470 0.4869360 0.4869360 NA NA 2.4936551 15.780050 NA #> 471 1.3494676 1.3494676 NA NA 4.9812861 6.631962 5.4957409 #> 473 1.3750723 1.3750723 NA NA 2.2375015 14.669470 NA #> 475 1.2652916 1.2652916 NA NA 5.1762835 13.253081 NA #> 476 0.9041080 0.9041080 NA NA 0.8406026 7.948348 3.3682176 #> 479 0.5719003 0.5719003 NA NA 2.3476324 18.260754 NA #> 480 0.5719003 0.5719003 NA NA 2.3476324 18.260754 NA #> 481 1.0913351 0.6057451 2.3988301 10.102904 1.1650959 10.248284 NA #> 482 1.8038011 1.8038011 NA NA 3.1061545 6.328173 3.7267989 #> 483 0.7929511 0.7929511 NA NA 2.9478731 9.743279 NA #> 486 2.0494969 1.3828849 2.0444975 9.873982 5.2731825 11.242051 5.9137826 #> 488 1.7795910 1.7795910 NA NA 0.6267952 5.133676 0.3931901 #> 490 1.5018619 1.5018619 NA NA 0.7181485 7.883001 1.7061269 #> 491 1.5018619 1.5018619 NA NA 0.7181485 7.883001 1.7061269 #> 492 0.9936782 0.9936782 NA NA 5.6440554 10.841074 NA #> 493 0.7003740 0.7003740 NA NA 2.3163527 16.575701 NA #> 494 1.6525193 1.6525193 1.5925756 5.174180 0.5751369 4.233156 0.4008074 #> 495 0.5950246 0.5950246 3.0740721 10.519111 0.8381449 10.909025 NA #> 496 1.4582159 1.4582159 NA NA 3.1350631 7.680595 3.1791194 #> 498 0.7529821 0.7529821 NA NA 2.0945933 8.905403 NA #> 499 0.7529821 0.7529821 NA NA 2.0945933 8.905403 NA #> 500 1.9384481 1.3530192 2.2554363 2.496550 5.8011356 3.847010 0.7231269 #> 501 1.9945506 1.1718772 2.0127634 9.659675 0.5750563 10.128055 6.1142017 #> 502 1.1588701 1.1588701 NA NA 3.0861609 7.464089 3.9623005 #> 504 1.4049630 1.4049630 NA NA 4.4260549 5.602646 4.5019378 #> 505 1.6786893 1.1524342 2.0401909 12.025919 1.4522317 12.075055 NA #> 509 1.1207480 1.1207480 NA NA 5.6739990 11.314837 NA #> 513 1.6477686 1.6477686 NA NA 5.6272766 3.586008 4.4854464 #> 514 1.6477686 1.6477686 NA NA 5.6272766 3.586008 4.4854464 #> 515 1.4557079 1.4557079 NA NA 3.0303834 4.402824 1.7025240 #> 517 2.7369191 2.7369191 NA NA 5.8469929 5.645054 4.9467474 #> 518 1.3259250 1.3259250 2.7907400 6.379267 0.6026376 7.244570 0.9306075 #> 519 0.9527003 0.9527003 NA NA 5.0802181 12.242515 NA #> 520 0.7702132 0.7702132 NA NA 4.9509541 5.340189 6.0080600 #> 521 1.2534643 0.8761367 1.7099772 4.490342 0.7375741 4.087043 0.5751990 #> 522 1.2238207 1.2238207 NA NA 6.0576628 11.870086 6.5680391 #> 523 2.3341764 2.3341764 NA NA 2.1622238 10.928227 4.9554389 #> 525 0.8999648 0.8999648 1.2611850 4.217990 0.5657408 3.801510 0.4566400 #> 526 1.2614322 1.2614322 NA NA 2.8527457 3.500472 1.5548265 #> 527 1.9460898 1.9460898 1.4500622 3.910806 0.4011651 4.874150 0.2639840 #> 528 0.8116440 0.8116440 NA NA 2.3482523 10.419075 NA #> 531 3.3631722 3.3631722 NA NA 5.0398424 6.225849 5.0923826 #> 534 1.5216846 1.0295037 1.8803822 10.071887 4.9758852 11.102445 NA #> 535 2.2285391 2.2285391 NA NA 5.8914318 7.684056 5.5312271 #> 540 3.1308885 3.1308885 NA NA 3.8712302 5.561327 3.1637050 #> 542 1.2090070 1.2090070 1.5821319 3.789952 0.6363127 4.486881 0.6282973 #> 543 1.3570049 0.6941344 2.2981412 12.985739 1.3780037 13.117415 NA #> 544 1.4017054 1.4017054 NA NA 5.0717760 11.966532 6.4927085 #> 546 1.2344106 1.2344106 NA NA 2.8051842 3.001640 1.3319009 #> 547 2.6339438 2.6339438 NA NA 5.2437498 3.509009 3.9184068 #> 549 2.0801672 2.0801672 1.5956017 2.363039 0.4677758 3.432336 0.2651867 #> 551 1.7353224 1.7353224 NA NA 0.8742707 4.728587 0.3591303 #> 552 3.1999446 3.1999446 NA NA 2.5538772 4.216161 0.6182950 #> 553 2.0616360 2.0616360 NA NA 2.5250685 4.078962 1.0594414 #> 554 1.5426292 1.5426292 NA NA 5.9163079 4.160652 5.7105061 #> 556 0.6086139 0.6086139 NA NA 0.5237934 9.259982 NA #> 558 0.9495953 0.9495953 NA NA 2.6312532 7.308597 4.8404495 #> 559 1.3525467 0.9762831 1.8611164 8.973165 4.7863633 9.753361 6.4487178 #> 561 1.1047895 1.1047895 NA NA 5.0063684 5.890640 5.8855757 #> 562 1.1047895 1.1047895 NA NA 5.0063684 5.890640 5.8855757 #> 565 0.7082924 0.7082924 NA NA 0.8415993 8.580752 NA #> 566 1.1161200 1.1161200 NA NA 0.7867886 6.733326 1.1989832 #> 567 2.4773277 2.4773277 NA NA 5.5764311 4.930684 4.8975036 #> 569 1.5364463 1.5364463 NA NA 2.8093253 5.938641 2.2820679 #> 571 1.4460633 0.9619972 1.8081921 4.217660 5.0186649 3.142332 1.0034930 #> 572 1.9620447 1.9620447 NA NA 1.6083100 5.416645 1.6697987 #> 573 1.2941718 1.2941718 NA NA 2.5161241 12.209234 6.0040817 #> 574 0.6072923 0.6072923 NA NA 0.2013097 7.102537 NA #> 575 1.3284196 1.3284196 NA NA 5.7071899 6.637866 5.6955727 #> 577 1.6331559 0.9393843 2.2045847 4.838591 1.7413609 4.875934 1.2937236 #> 579 2.7957572 2.7957572 NA NA 3.0768950 3.650346 0.5581026 #> 580 2.7957572 2.7957572 NA NA 3.0768950 3.650346 0.5581026 #> 581 1.0908882 1.0908882 3.5301455 6.762864 1.0281330 7.567542 1.9155915 #> 583 1.1239704 1.1239704 NA NA 4.2421167 16.376903 NA #> 585 1.0866179 1.0866179 NA NA 5.4593968 6.426767 5.9394660 #> 586 1.2818324 1.2818324 2.1464196 4.665653 0.7105856 5.459371 0.8008469 #> 587 1.2818324 1.2818324 2.1464196 4.665653 0.7105856 5.459371 0.8008469 #> 588 1.1820174 0.7790280 2.9011092 11.929683 1.8336510 12.003729 NA #> 590 3.5641413 3.5641413 NA NA 2.2498351 13.038588 2.2008147 #> 591 1.6381642 1.6381642 NA NA 4.6149428 8.343102 5.7104719 #> 593 1.5961529 1.5961529 NA NA 5.2575129 3.447811 3.5740658 #> 595 1.2637546 1.2637546 NA NA 2.1014113 7.103232 3.9372516 #> 596 2.4832543 1.3875575 2.6722696 7.051389 0.5554829 6.120341 0.6992043 #> 597 1.8241896 1.8241896 NA NA 4.2864651 4.812738 4.5279917 #> 598 0.7835917 0.7835917 NA NA 0.9822553 8.516488 NA #> 599 0.7835917 0.7835917 NA NA 0.9822553 8.516488 NA #> 603 2.5521288 2.5521288 NA NA 2.0314323 4.133559 0.8708476 #> 604 1.4684527 0.7399295 2.7306565 9.440219 0.6335929 8.998999 NA #> 606 1.3048116 1.3048116 NA NA 2.7359803 15.538588 NA #> 608 1.5003885 1.5003885 NA NA 5.3399279 6.752960 5.8776276 #> 610 1.5629901 1.5629901 NA NA 5.8277486 3.386062 5.1779858 #> 611 1.7713152 1.7713152 NA NA 0.5680459 9.271025 1.2444712 #> 612 0.7519149 0.7519149 NA NA 5.3784638 9.779899 NA #> 614 0.7519149 0.7519149 NA NA 5.3784638 9.779899 NA #> 615 0.7519149 0.7519149 NA NA 5.3784638 9.779899 NA #> 616 1.1123253 1.1123253 NA NA 2.4110229 5.515970 3.0471797 #> 617 1.0347975 1.0347975 NA NA 2.8121975 8.613401 6.0472213 #> 618 3.0183770 1.7655821 2.1816572 2.983543 0.9510929 3.711516 0.4370000 #> 619 3.0183770 1.7655821 2.1816572 2.983543 0.9510929 3.711516 0.4370000 #> 620 2.0074095 2.0074095 NA NA 5.6077502 3.361662 4.4929978 #> 623 2.2144941 2.2144941 1.6229804 4.992113 1.0642503 3.281539 0.9871425 #> 624 2.0734706 2.0734706 NA NA 2.8650025 6.279761 1.7217797 #> 626 1.7075076 1.7075076 NA NA 3.9228053 5.531208 3.8287058 #> 630 2.2773688 2.2773688 NA NA 6.0081475 7.301299 5.8988929 #> 631 1.6071190 1.6071190 NA NA 3.9104454 11.564767 6.1083670 #> 634 1.3536434 1.3536434 NA NA 2.1742522 6.998808 3.2110326 #> 641 1.4448595 1.4448595 NA NA 0.4939544 13.007338 4.2861144 #> 642 0.5907318 0.5907318 NA NA 0.9251921 15.888375 NA #> 643 0.7444761 0.7444761 NA NA 2.7449083 17.353863 NA #> 644 0.7667073 0.7667073 NA NA 5.1501661 15.680364 NA #> 645 1.3303544 1.3303544 NA NA 0.4900168 12.087363 4.9350083 #> 646 0.8000254 0.8000254 NA NA 5.1991367 15.898344 NA #> BMR.xfold BMD.zSD.lower BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper #> 1 6.609214 0.97850954 4.0686985 Inf Inf #> 2 5.347706 0.20008806 1.1095586 Inf Inf #> 5 7.073198 0.75185882 1.4649978 Inf Inf #> 6 7.073198 0.75185882 1.4649978 Inf Inf #> 7 7.073198 0.75185882 1.4649978 Inf Inf #> 8 6.172119 0.05543773 0.6804425 0.561154372 Inf #> 9 5.588358 0.08095270 0.7936032 0.329293169 Inf #> 10 8.017772 0.42468408 1.0520363 Inf Inf #> 11 6.181671 0.07579775 0.7005182 Inf Inf #> 12 6.802633 0.03694799 0.4209217 Inf Inf #> 13 5.820719 1.67433198 5.3037292 Inf Inf #> 14 5.164472 1.25236329 2.8522870 7.563758934 Inf #> 16 5.863188 1.32631865 6.0595553 5.925234838 Inf #> 17 5.863188 1.32631865 6.0595553 5.925234838 Inf #> 18 5.465323 2.76071129 7.1933590 7.679066254 Inf #> 19 5.465323 2.76071129 7.1933590 7.679066254 Inf #> 20 5.465323 2.76071129 7.1933590 7.679066254 Inf #> 22 4.380763 0.36442365 2.2863213 Inf Inf #> 23 4.380763 0.36442365 2.2863213 Inf Inf #> 25 4.862095 1.87728956 5.7928776 Inf Inf #> 26 4.425610 0.63687870 2.6421628 1.607320792 Inf #> 27 4.425610 0.63687870 2.6421628 1.607320792 Inf #> 28 4.425610 0.63687870 2.6421628 1.607320792 Inf #> 29 4.425610 0.63687870 2.6421628 1.607320792 Inf #> 30 4.425610 0.63687870 2.6421628 1.607320792 Inf #> 31 5.860923 0.37773418 3.4329037 2.120911243 Inf #> 32 5.860923 0.37773418 3.4329037 2.120911243 Inf #> 33 5.860923 0.37773418 3.4329037 2.120911243 Inf #> 34 5.860923 0.37773418 3.4329037 2.120911243 Inf #> 35 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 36 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 37 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 39 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 40 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 41 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 42 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 43 6.168631 0.24887531 4.0820798 2.791737594 Inf #> 44 4.573695 0.32686514 3.1332891 5.738912687 Inf #> 46 6.316887 2.43380228 6.3223929 Inf Inf #> 47 6.316887 2.43380228 6.3223929 Inf Inf #> 48 4.940865 0.35799216 4.2882933 2.320483615 Inf #> 49 4.940865 0.35799216 4.2882933 2.320483615 Inf #> 50 4.940865 0.35799216 4.2882933 2.320483615 Inf #> 51 4.940865 0.35799216 4.2882933 2.320483615 Inf #> 52 5.737279 0.30086446 3.1833272 1.307410467 Inf #> 53 5.737279 0.30086446 3.1833272 1.307410467 Inf #> 54 5.737279 0.30086446 3.1833272 1.307410467 Inf #> 55 5.737279 0.30086446 3.1833272 1.307410467 Inf #> 56 5.737279 0.30086446 3.1833272 1.307410467 Inf #> 57 5.737279 0.30086446 3.1833272 1.307410467 Inf #> 58 5.177716 0.46124416 1.4873687 Inf Inf #> 59 4.962470 1.03576992 3.6179409 Inf Inf #> 60 4.962470 1.03576992 3.6179409 Inf Inf #> 61 5.158766 0.48406738 1.2370211 0.808097974 Inf #> 62 5.158766 0.48406738 1.2370211 0.808097974 Inf #> 63 5.158766 0.48406738 1.2370211 0.808097974 Inf #> 64 5.158766 0.48406738 1.2370211 0.808097974 Inf #> 65 5.158766 0.48406738 1.2370211 0.808097974 Inf #> 67 6.677576 0.26644454 4.2871164 Inf Inf #> 68 6.677576 0.26644454 4.2871164 Inf Inf #> 69 6.677576 0.26644454 4.2871164 Inf Inf #> 70 6.677576 0.26644454 4.2871164 Inf Inf #> 71 5.840236 3.09334793 6.6162300 7.323353364 Inf #> 72 5.840236 3.09334793 6.6162300 7.323353364 Inf #> 73 5.840236 3.09334793 6.6162300 7.323353364 Inf #> 75 4.972785 0.63064808 4.7996149 5.945710618 Inf #> 76 4.972785 0.63064808 4.7996149 5.945710618 Inf #> 77 4.972785 0.63064808 4.7996149 5.945710618 Inf #> 79 5.236113 0.52106128 5.9739107 5.919751612 Inf #> 80 5.236113 0.52106128 5.9739107 5.919751612 Inf #> 81 5.980553 0.42901273 4.3080978 5.248292886 Inf #> 82 5.980553 0.42901273 4.3080978 5.248292886 Inf #> 83 5.980553 0.42901273 4.3080978 5.248292886 Inf #> 84 5.980553 0.42901273 4.3080978 5.248292886 Inf #> 85 9.765941 1.25476148 2.7589141 3.944767854 Inf #> 86 11.185391 0.24296501 0.8254193 2.323655028 Inf #> 87 11.170683 0.28244583 0.9252443 2.789277177 Inf #> 88 18.051589 2.64963205 5.5727686 Inf Inf #> 91 11.653948 1.04939875 1.9503267 Inf Inf #> 95 17.349849 1.99350628 5.0456881 6.137589248 Inf #> 99 13.184704 1.71972684 5.2546852 Inf Inf #> 102 8.452213 1.52079518 3.8197300 Inf Inf #> 103 7.959897 0.46760570 2.8942164 1.944016318 Inf #> 104 7.959897 0.46760570 2.8942164 1.944016318 Inf #> 106 6.609855 0.21744945 1.5471627 0.428071765 2.5957963 #> 109 16.612407 1.54015368 3.7311047 Inf Inf #> 110 9.268869 0.37327629 1.2105842 1.951273142 Inf #> 111 4.893943 0.66647676 1.8535634 0.596011289 1.4002372 #> 115 13.532062 1.25523264 2.4785285 2.184471756 4.1050629 #> 116 8.370532 2.62074447 6.1892385 6.399079578 Inf #> 117 19.057483 1.33538267 3.2723752 Inf Inf #> 120 10.178300 0.49685136 4.8689356 0.720186659 5.3855908 #> 121 11.774346 1.07799896 3.2324908 6.134109074 Inf #> 122 13.018466 0.65089363 1.4477020 4.613686534 5.6633468 #> 123 11.262816 0.39650460 1.0210325 0.935884812 6.2063487 #> 125 11.840420 1.64980574 4.7578654 Inf Inf #> 127 16.661441 2.10623596 4.9747503 Inf Inf #> 128 10.745467 0.45072040 4.9073419 5.535135720 Inf #> 129 13.115891 2.95611239 5.6794781 5.141147897 6.5072131 #> 131 6.444924 0.37828212 3.9147635 0.641720795 4.6921648 #> 132 6.444924 0.37828212 3.9147635 0.641720795 4.6921648 #> 133 7.809562 0.28329273 0.9217664 0.371304357 1.0567280 #> 134 12.192005 0.45119793 1.3369379 Inf Inf #> 137 13.697747 2.88540036 6.0230168 Inf Inf #> 140 12.424335 1.39985045 3.1165736 Inf Inf #> 141 11.715823 0.46705147 4.3039298 Inf Inf #> 142 9.550364 1.05386556 3.3648968 5.999140808 Inf #> 143 8.280927 1.21655973 3.4294082 2.333290039 4.5329127 #> 147 16.074408 2.74155251 5.3503657 Inf Inf #> 148 16.170012 1.72942685 4.9473537 6.574719043 Inf #> 149 8.613130 2.33640966 5.5962017 3.329265202 6.0213409 #> 150 6.228886 1.30775683 3.0619063 2.633213483 5.1889529 #> 152 11.281891 1.68457955 4.6751632 6.392129679 Inf #> 156 4.680233 1.62293691 5.9759268 1.316830682 3.9749306 #> 157 8.874974 0.27525816 1.6705374 0.448004236 2.7783783 #> 158 11.113667 1.07953096 2.3571861 4.876575034 Inf #> 161 12.091282 0.60384236 5.6978525 Inf Inf #> 164 16.600846 0.48326750 1.0560307 Inf Inf #> 165 9.457594 1.02570209 1.9393894 2.006597935 3.1213521 #> 166 10.568354 0.76647179 5.2718059 Inf Inf #> 167 12.039017 1.17918063 2.7286462 Inf Inf #> 169 9.162234 0.96799087 1.9783744 3.147459144 5.1690481 #> 170 8.642749 0.50907354 5.1415268 1.604331864 Inf #> 171 8.200742 1.50525257 3.4011695 1.955152886 3.7737373 #> 172 8.200742 1.50525257 3.4011695 1.955152886 3.7737373 #> 174 6.999308 0.53075189 4.6854038 0.666553386 5.0930986 #> 178 10.396666 0.53046359 1.1464625 1.683797922 Inf #> 179 11.958109 1.14853940 2.4913773 Inf Inf #> 180 12.563182 1.54875481 3.5028687 Inf Inf #> 181 12.563182 1.54875481 3.5028687 Inf Inf #> 183 7.409795 0.08183997 0.5980861 0.191250605 1.6257035 #> 184 7.049409 1.75885065 4.3783442 2.228962042 5.6185328 #> 185 7.049409 1.75885065 4.3783442 2.228962042 5.6185328 #> 186 8.010933 2.93461899 6.0719877 5.699704509 Inf #> 188 4.584806 0.50278548 1.8172104 0.398838259 1.5901895 #> 190 11.131602 0.58190671 1.6479060 Inf Inf #> 193 3.345752 0.35654678 1.4592305 0.166561570 1.1565046 #> 196 11.050749 0.43301683 0.8971572 0.740395116 1.2380754 #> 197 7.936032 1.74049030 5.8493362 5.058661772 Inf #> 198 9.315398 1.73688931 5.1197523 5.388840084 Inf #> 199 11.305100 1.60498652 5.3246257 5.203053760 Inf #> 200 11.305100 1.60498652 5.3246257 5.203053760 Inf #> 201 8.688686 0.35183367 1.7316828 0.362628186 1.4953577 #> 202 8.688686 0.35183367 1.7316828 0.362628186 1.4953577 #> 203 8.688686 0.35183367 1.7316828 0.362628186 1.4953577 #> 205 15.094411 0.22352954 1.1815352 Inf Inf #> 207 3.519730 0.29910845 1.8306631 0.199774284 1.4325785 #> 208 7.620460 0.57763293 5.4940389 1.085545152 Inf #> 211 12.775912 0.41079211 1.5942930 Inf Inf #> 214 6.991935 0.36258894 1.5883451 0.452166289 1.6600473 #> 215 11.356575 2.93270043 5.9633443 4.127378018 6.3349736 #> 217 9.617183 1.66564175 4.0856814 6.348263101 Inf #> 218 11.997807 0.32329297 1.0469995 0.647250519 1.6091792 #> 220 4.760624 1.37853596 3.1602333 1.011869465 2.2765836 #> 221 11.830089 0.31337952 1.3891586 Inf Inf #> 222 10.248211 1.43903732 3.7180841 Inf Inf #> 225 10.579865 2.77040545 5.6067950 Inf Inf #> 228 11.354430 0.48380870 1.5400128 Inf Inf #> 229 9.806566 1.26495795 3.0819744 6.038177033 Inf #> 232 4.292819 0.40044672 6.5720202 0.372516223 5.9756156 #> 233 4.292819 0.40044672 6.5720202 0.372516223 5.9756156 #> 235 10.664641 0.31820753 1.5828158 1.301585711 Inf #> 236 12.010635 2.82937876 5.8296064 6.441719601 Inf #> 237 14.176242 2.74132244 5.6217506 6.059639702 Inf #> 238 14.176242 2.74132244 5.6217506 6.059639702 Inf #> 240 14.176242 2.74132244 5.6217506 6.059639702 Inf #> 242 6.179380 1.79369036 5.2099565 1.536556109 4.4694075 #> 243 11.464039 0.45259236 0.9852054 Inf Inf #> 245 9.140814 2.60802191 6.3443547 4.773931693 Inf #> 246 9.140814 2.60802191 6.3443547 4.773931693 Inf #> 248 7.699912 1.67808514 5.1396151 2.700837923 Inf #> 251 19.719388 1.32526215 3.0171899 Inf Inf #> 252 19.719388 1.32526215 3.0171899 Inf Inf #> 253 10.397567 0.27487668 0.8684603 1.202020409 Inf #> 255 4.536452 3.02720467 5.8125246 1.185965587 4.9408312 #> 256 10.146508 0.34554032 1.8031346 Inf Inf #> 257 10.058956 3.07025121 5.3715482 6.218567131 Inf #> 259 12.006209 1.26617938 2.6694875 Inf Inf #> 260 5.843978 0.30500837 2.5122396 0.378730892 2.5190324 #> 263 11.553591 1.79186086 4.7606571 Inf Inf #> 265 10.512135 1.00581659 1.7217125 5.981752578 Inf #> 266 4.549637 0.11229115 1.1562790 0.089201977 0.6082146 #> 270 7.935151 0.36344217 4.8271636 0.571262850 6.0547691 #> 272 5.665327 1.54004500 3.3695645 1.714682866 3.6378978 #> 274 11.742147 0.33666389 1.6530558 Inf Inf #> 275 12.207078 2.02820057 5.2593819 6.170577485 Inf #> 276 6.266743 0.27957255 1.4053942 0.623857241 2.4264491 #> 277 9.914290 1.67314034 4.8813673 6.260515221 Inf #> 279 6.021363 2.57101943 5.4991451 5.108155638 6.3704832 #> 280 8.345108 2.69574649 5.3067808 5.531666056 6.4733040 #> 282 12.073975 0.40904089 2.6783910 5.386193008 Inf #> 285 9.353255 0.52323314 5.1004381 6.448319399 Inf #> 286 4.044806 1.57343097 4.8340198 1.004095616 2.8864003 #> 287 7.384116 1.60569926 3.5771123 4.539553747 Inf #> 288 7.384116 1.60569926 3.5771123 4.539553747 Inf #> 291 4.165686 1.61304816 4.7440724 2.054933805 4.8155223 #> 293 10.806789 2.28426943 5.7598313 Inf Inf #> 295 7.242857 1.83136398 4.8498220 6.283356411 Inf #> 296 11.222799 1.61923981 4.8209580 6.446861410 Inf #> 297 10.004959 1.75515628 4.9422872 Inf Inf #> 298 10.004959 1.75515628 4.9422872 Inf Inf #> 299 7.920161 2.73919695 5.6174409 4.537291440 6.2201378 #> 301 13.841195 1.57349911 3.9836203 Inf Inf #> 303 8.857838 1.45426811 3.6196180 Inf Inf #> 305 11.680806 2.74748436 5.9480926 5.959090533 Inf #> 306 11.628496 1.31584676 3.3142276 Inf Inf #> 307 11.628496 1.31584676 3.3142276 Inf Inf #> 308 7.780480 0.29506317 3.0199956 4.254602701 Inf #> 312 10.951725 0.32210163 1.4464539 Inf Inf #> 314 9.708470 0.31620212 1.3244315 0.720852189 2.8039243 #> 317 11.200578 0.68145620 6.3611319 1.486068665 Inf #> 320 6.465456 0.51980328 5.7343561 0.917012370 6.5546099 #> 321 6.465456 0.51980328 5.7343561 0.917012370 6.5546099 #> 322 8.849025 0.14938717 1.0207350 Inf Inf #> 327 4.452148 0.11967646 1.0401929 0.195348516 1.6577848 #> 328 8.176794 2.15278536 5.5732310 3.537939516 6.1536307 #> 330 9.245448 2.89498554 5.3843413 6.133839112 Inf #> 331 10.557257 0.77715707 1.4762641 5.166992021 Inf #> 332 10.557257 0.77715707 1.4762641 5.166992021 Inf #> 333 7.801578 0.93447304 2.7014552 2.315744699 Inf #> 336 3.987417 0.29731794 1.3828385 0.151027996 0.7370402 #> 338 11.437571 1.50483906 3.0487558 5.435990512 Inf #> 339 3.170032 0.18131969 1.0088612 0.144550692 0.9361412 #> 340 5.784045 0.52312717 5.1958248 1.581893139 Inf #> 341 3.132075 0.55429742 5.7718558 0.247759130 0.9522847 #> 342 2.799031 1.79473176 5.9520232 0.518304035 1.9018699 #> 343 12.302919 0.55519755 5.6084166 Inf Inf #> 344 9.386265 0.41050756 2.0533236 2.060511247 Inf #> 346 10.639970 1.12797148 3.0911075 3.100615311 6.0301429 #> 350 6.153925 2.53621646 5.9795879 2.919533786 6.0579747 #> 354 6.170927 2.27138538 5.7358531 3.729207860 6.3344473 #> 357 8.211528 2.17027304 4.7299005 2.748717002 5.1089096 #> 358 6.401878 1.70642374 4.3812001 3.592866018 5.4785532 #> 360 6.401878 1.70642374 4.3812001 3.592866018 5.4785532 #> 361 10.315734 2.77164549 6.0800988 5.523600805 Inf #> 362 10.432780 0.42488885 1.3612369 0.950713483 Inf #> 364 11.015759 1.35631407 3.9482498 6.116495321 Inf #> 365 16.112188 1.51507954 3.7087226 Inf Inf #> 368 12.513653 2.68685892 5.2631287 5.372229614 6.3760247 #> 370 5.506560 0.38959091 2.2272543 0.503732566 2.2560210 #> 372 12.262440 0.43168938 4.3245839 5.064185119 Inf #> 375 12.986136 2.72596759 5.7854763 5.147440713 Inf #> 376 6.613113 1.67497503 4.2608419 3.230241717 Inf #> 378 11.983534 2.80885642 6.0493796 Inf Inf #> 379 10.044223 0.39958196 2.7913521 Inf Inf #> 380 9.484078 3.19121913 6.0330978 5.757855434 Inf #> 384 6.947876 0.83900191 2.4495022 1.324503954 3.0764073 #> 386 11.062375 1.39139459 2.7623036 Inf Inf #> 387 11.062375 1.39139459 2.7623036 Inf Inf #> 389 5.626303 2.38471036 5.6950050 1.252120890 5.0100759 #> 391 7.534332 1.43162683 3.9766076 5.881437620 Inf #> 394 8.473869 1.59053072 4.8455224 6.493876912 Inf #> 395 11.419236 0.91299791 2.4019524 5.236758636 6.0300079 #> 397 6.007350 0.47269962 6.1399990 0.441645392 1.9386531 #> 399 7.386885 0.58678810 4.9024458 Inf Inf #> 401 8.406459 0.41247948 1.9597416 1.808817338 Inf #> 404 11.704820 2.30323194 5.5256078 Inf Inf #> 405 5.359396 1.48802589 3.7982782 1.854106107 4.4410965 #> 408 11.472711 0.49209232 4.7797975 Inf Inf #> 409 9.824131 1.37232930 4.1176062 3.397031389 5.6597211 #> 410 9.750089 2.67782583 5.6571666 6.277773208 Inf #> 411 14.077228 0.55214614 5.2737085 0.835070682 Inf #> 414 7.446790 1.54644313 3.7563391 4.675890098 Inf #> 416 16.048153 0.57103992 5.7120187 Inf Inf #> 419 9.154518 3.01643160 5.7582422 4.454667912 6.2076045 #> 420 9.282633 2.59941851 5.8239482 4.994810496 Inf #> 422 4.353907 1.08136166 2.2450742 0.537628909 1.0784105 #> 427 5.813539 0.44049003 1.2312424 0.600531494 1.3655704 #> 428 5.813539 0.44049003 1.2312424 0.600531494 1.3655704 #> 430 8.294884 3.11003882 5.9115909 5.959748471 Inf #> 433 10.591996 2.95429136 5.5813685 Inf Inf #> 437 6.848772 1.53954471 3.7611556 4.431727423 Inf #> 439 3.843277 2.73897612 5.8700482 1.077465424 5.0228002 #> 441 3.843277 2.73897612 5.8700482 1.077465424 5.0228002 #> 443 8.583773 1.55012771 3.4384422 2.006763926 4.3319657 #> 445 11.635874 0.24885181 1.7730190 Inf Inf #> 447 10.861058 2.67167471 6.0475253 4.118430525 6.5466679 #> 450 10.319887 0.89713656 1.6364676 5.143636334 Inf #> 453 10.432280 0.49291097 4.8114751 Inf Inf #> 454 10.432280 0.49291097 4.8114751 Inf Inf #> 457 8.924420 2.95397705 6.1224920 5.862093858 Inf #> 458 8.924420 2.95397705 6.1224920 5.862093858 Inf #> 460 5.575966 1.51555559 4.1284671 1.763094570 3.8398589 #> 461 12.758150 1.49781631 4.2006366 Inf Inf #> 464 10.383239 1.67750256 5.1249891 Inf Inf #> 465 8.406276 2.26344376 5.5834726 5.190863194 Inf #> 466 12.247232 2.47216394 5.8384901 Inf Inf #> 467 10.370996 2.98412616 5.8970326 5.436215750 Inf #> 468 7.937128 3.14646412 5.5666368 6.350040973 Inf #> 469 5.525237 0.67558337 2.1715428 0.883026937 2.2991852 #> 470 17.156626 1.53615953 4.1273934 Inf Inf #> 471 6.449810 2.44381942 5.9263373 3.362335705 6.2025526 #> 473 15.626026 1.37851705 3.3887887 4.957730063 Inf #> 475 14.012261 2.57284026 6.0635216 5.596013765 Inf #> 476 8.390585 0.35181993 1.9120592 1.372831217 Inf #> 479 16.616906 1.55051252 3.5295052 Inf Inf #> 480 16.616906 1.55051252 3.5295052 Inf Inf #> 481 11.647343 0.59290513 5.3000812 Inf Inf #> 482 6.476746 1.49174316 4.6941363 2.091069038 5.2154405 #> 483 10.329842 1.75332519 5.0613449 5.435643045 Inf #> 486 11.594653 0.62508123 5.7068993 1.327086711 Inf #> 488 4.922630 0.20461711 1.6845815 0.139945658 1.2568975 #> 490 7.477196 0.32356830 1.4338426 0.971756751 3.0973018 #> 491 7.477196 0.32356830 1.4338426 0.971756751 3.0973018 #> 492 11.411100 2.93033518 6.1810802 5.558239912 Inf #> 493 15.138321 1.51510806 3.7392715 Inf Inf #> 494 3.873826 0.18220941 1.0969609 0.091699534 0.8486895 #> 495 12.225549 0.37518670 1.5715812 Inf Inf #> 496 7.690283 1.92555525 5.6517887 2.146849617 6.0728723 #> 498 9.534307 1.36558810 3.0277098 5.762503096 Inf #> 499 9.534307 1.36558810 3.0277098 5.762503096 Inf #> 500 2.773781 0.65667378 6.2902176 0.373623234 3.3879709 #> 501 11.530584 0.33731937 0.9197975 1.508058972 Inf #> 502 7.617208 1.89109990 5.3403262 2.591205923 Inf #> 504 5.621811 2.21981342 5.6568219 2.434121632 5.6965397 #> 505 13.807391 0.56359317 5.2624212 5.795155090 Inf #> 509 10.490827 3.02457964 5.8110892 6.145402448 Inf #> 513 3.142488 2.82669704 6.1106136 1.280102495 5.2509376 #> 514 3.142488 2.82669704 6.1106136 1.280102495 5.2509376 #> 515 4.111318 1.69251190 5.7986233 1.068040105 3.3844872 #> 517 4.913670 2.50987504 6.1097889 1.020668794 5.2178549 #> 518 6.934673 0.18681863 1.2570076 0.472633948 1.7840523 #> 519 13.060251 2.75999567 5.7929246 6.338097788 Inf #> 520 5.599368 2.23211655 5.7085379 4.411957201 6.4752810 #> 521 3.975625 0.46888113 3.5368956 0.412540372 2.2564088 #> 522 12.413718 3.13307067 6.1573656 5.357729276 Inf #> 523 10.059264 1.30750373 3.0842068 4.408059421 5.4651710 #> 525 3.649828 0.07147828 1.0011930 0.048498052 0.8778772 #> 526 3.253565 1.66602930 4.7801024 1.004363597 2.7209529 #> 527 5.271207 0.01267481 1.1158721 0.007461455 0.7837629 #> 528 11.144810 1.49647165 3.5311407 6.064214526 Inf #> 531 6.189467 3.29076592 5.3622969 3.470973856 5.4352797 #> 534 11.620474 0.57587075 5.6502320 1.732666151 Inf #> 535 7.360651 3.25243304 6.1717262 2.147243007 5.7371913 #> 540 5.311273 2.09802632 5.1109137 1.458393435 4.7290176 #> 542 4.499063 0.23982321 0.9989047 0.284675115 1.0044222 #> 543 15.013470 0.65235594 6.4260092 6.453090944 Inf #> 544 11.053088 2.83893468 5.4797507 6.000722553 Inf #> 546 2.727377 1.63856489 5.0203541 0.849091855 2.7034316 #> 547 2.950722 2.72792323 5.7943095 0.996722114 4.8068402 #> 549 3.998886 0.05399315 1.2442471 0.014320654 0.6768693 #> 551 4.314200 0.27996824 2.9680621 0.123733758 1.4896918 #> 552 3.282101 1.41586955 4.2784519 0.347847946 1.0906435 #> 553 3.623286 1.47301680 4.2390445 0.687419859 1.8230891 #> 554 4.303831 3.07905725 6.0763029 2.724727869 5.8528389 #> 556 9.871577 0.16711640 2.2687004 3.193289198 Inf #> 558 7.624966 1.63151673 4.5223468 3.363446161 Inf #> 559 10.284372 0.59329962 5.2313257 5.176477559 Inf #> 561 5.582769 2.66774103 5.5482430 4.568013933 6.2629489 #> 562 5.582769 2.66774103 5.5482430 4.568013933 6.2629489 #> 565 7.922913 0.35369329 1.9728850 5.976877847 Inf #> 566 6.894596 0.27440127 2.3879979 0.444798465 4.1009270 #> 567 4.510380 3.04662568 5.8599969 1.798504986 5.3875293 #> 569 5.816472 1.52421746 4.8285511 1.473564631 3.9997281 #> 571 4.106953 0.64467381 5.9696696 0.448604431 4.9243516 #> 572 5.434839 1.14264286 2.0822884 1.309733690 2.2514858 #> 573 12.889978 1.48412235 4.3165892 4.269357714 Inf #> 574 7.540718 0.07897595 0.5722466 0.563141265 Inf #> 575 6.631907 2.89367417 6.1680412 3.315818503 6.2557758 #> 577 4.979126 0.65520412 6.2161646 0.688726137 5.5777863 #> 579 2.588375 1.71979781 5.8334366 0.292697242 1.3384842 #> 580 2.588375 1.71979781 5.8334366 0.292697242 1.3384842 #> 581 7.068377 0.58618031 1.5088144 1.409797613 2.6648659 #> 583 17.733195 2.39865845 5.2992829 Inf Inf #> 585 6.239645 2.80289650 6.1722055 3.950276091 Inf #> 586 5.352737 0.31331655 1.2981207 0.454482941 1.4204213 #> 587 5.352737 0.31331655 1.2981207 0.454482941 1.4204213 #> 588 13.565940 0.45906313 4.8255941 Inf Inf #> 590 13.012239 1.42463855 3.5561543 1.534388758 3.5978054 #> 591 8.805313 2.83479706 5.2682879 4.675475928 6.0753703 #> 593 2.980407 2.37205969 6.2112758 1.061635135 5.0651393 #> 595 6.753352 1.23459866 3.1105371 3.053248092 5.6494469 #> 596 6.230214 0.34643538 0.7061183 0.537200768 1.0041140 #> 597 4.740045 2.16859102 5.3652514 2.729597152 5.5368126 #> 598 9.029268 0.38133494 2.3607272 1.910081924 Inf #> 599 9.029268 0.38133494 2.3607272 1.910081924 Inf #> 603 3.686875 1.29618555 3.0446474 0.632688925 1.4249475 #> 604 7.830261 0.38533255 0.8633687 1.665082423 Inf #> 606 16.500239 1.43708766 4.9137897 5.328617842 Inf #> 608 6.458822 2.75629900 5.7006541 4.245740698 6.1784483 #> 610 3.062052 3.04378438 6.0023852 1.537420079 5.4386620 #> 611 9.677450 0.25304437 1.2327338 0.630969777 2.7434432 #> 612 10.538488 3.21741886 5.6953260 6.601291694 Inf #> 614 10.538488 3.21741886 5.6953260 6.601291694 Inf #> 615 10.538488 3.21741886 5.6953260 6.601291694 Inf #> 616 5.622682 1.54405416 3.9836789 2.208028998 5.2513390 #> 617 9.239567 1.42731619 4.4521191 5.208723101 Inf #> 618 4.274213 0.56990952 5.7413732 0.323578135 0.7493740 #> 619 4.274213 0.56990952 5.7413732 0.323578135 0.7493740 #> 620 2.864603 2.80741209 6.0552893 1.044011927 5.0611838 #> 623 3.055381 0.60165852 1.1093746 0.524417704 1.0267987 #> 624 5.922283 1.71616519 5.0135594 1.113832033 3.2169318 #> 626 5.507754 1.98395938 5.4333538 1.954210759 5.5281589 #> 630 7.436280 2.99251277 6.0940883 2.959495418 5.9721761 #> 631 10.694167 2.10569740 5.1906991 5.449552006 6.5326155 #> 634 7.210455 1.25345804 3.2662254 2.404386300 4.8405299 #> 641 12.014582 0.23273791 0.8898776 2.227135223 Inf #> 642 17.229744 0.36546453 2.4410258 Inf Inf #> 643 18.750255 1.63965480 4.8149657 Inf Inf #> 644 17.085796 3.03104811 5.4289920 Inf Inf #> 645 11.110192 0.27124408 0.8292255 2.915067056 Inf #> 646 17.312389 2.97458678 5.4604316 Inf Inf #> nboot.successful path_class #> 1 1000 Lipid metabolism #> 2 957 Lipid metabolism #> 5 1000 Biosynthesis of other secondary metabolites #> 6 1000 Membrane transport #> 7 1000 Signal transduction #> 8 648 Lipid metabolism #> 9 620 Lipid metabolism #> 10 872 Lipid metabolism #> 11 909 Lipid metabolism #> 12 565 Lipid metabolism #> 13 1000 Lipid metabolism #> 14 1000 Lipid metabolism #> 16 1000 Membrane transport #> 17 1000 Signal transduction #> 18 718 Amino acid metabolism #> 19 718 Biosynthesis of other secondary metabolites #> 20 718 Translation #> 22 975 Membrane transport #> 23 975 Signal transduction #> 25 938 Membrane transport #> 26 962 Amino acid metabolism #> 27 962 Metabolism of other amino acids #> 28 962 Biosynthesis of other secondary metabolites #> 29 962 Translation #> 30 962 Membrane transport #> 31 979 Amino acid metabolism #> 32 979 Biosynthesis of other secondary metabolites #> 33 979 Translation #> 34 979 Membrane transport #> 35 851 Amino acid metabolism #> 36 851 Metabolism of other amino acids #> 37 851 Lipid metabolism #> 39 851 Energy metabolism #> 40 851 Translation #> 41 851 Biosynthesis of other secondary metabolites #> 42 851 Membrane transport #> 43 851 Signal transduction #> 44 1000 Amino acid metabolism #> 46 859 Energy metabolism #> 47 859 Signal transduction #> 48 833 Amino acid metabolism #> 49 833 Metabolism of other amino acids #> 50 833 Biosynthesis of other secondary metabolites #> 51 833 Membrane transport #> 52 890 Amino acid metabolism #> 53 890 Metabolism of other amino acids #> 54 890 Energy metabolism #> 55 890 Translation #> 56 890 Membrane transport #> 57 890 Signal transduction #> 58 940 Amino acid metabolism #> 59 1000 Lipid metabolism #> 60 1000 Amino acid metabolism #> 61 635 Amino acid metabolism #> 62 635 Metabolism of other amino acids #> 63 635 Biosynthesis of other secondary metabolites #> 64 635 Translation #> 65 635 Membrane transport #> 67 820 Amino acid metabolism #> 68 820 Metabolism of other amino acids #> 69 820 Biosynthesis of other secondary metabolites #> 70 820 Membrane transport #> 71 722 Energy metabolism #> 72 722 Membrane transport #> 73 722 Signal transduction #> 75 953 Amino acid metabolism #> 76 953 Lipid metabolism #> 77 953 Energy metabolism #> 79 962 Amino acid metabolism #> 80 962 Signal transduction #> 81 998 Amino acid metabolism #> 82 998 Metabolism of other amino acids #> 83 998 Translation #> 84 998 Membrane transport #> 85 500 Energy metabolism #> 86 497 Nucleotide metabolism #> 87 495 Nucleotide metabolism #> 88 332 Translation #> 91 500 Energy metabolism #> 95 469 Translation #> 99 500 Amino acid metabolism #> 102 500 Lipid metabolism #> 103 443 Metabolism of other amino acids #> 104 443 Amino acid metabolism #> 106 491 Translation #> 109 500 Energy metabolism #> 110 417 Lipid metabolism #> 111 395 Lipid metabolism #> 115 500 Energy metabolism #> 116 353 Nucleotide metabolism #> 117 500 Translation #> 120 492 Translation #> 121 483 Nucleotide metabolism #> 122 498 Energy metabolism #> 123 304 Membrane transport #> 125 500 Nucleotide metabolism #> 127 481 Translation #> 128 291 Amino acid metabolism #> 129 279 Signal transduction #> 131 483 Metabolism of other amino acids #> 132 483 Amino acid metabolism #> 133 497 Translation #> 134 405 Metabolism of other amino acids #> 137 253 Amino acid metabolism #> 140 500 Translation #> 141 481 Lipid metabolism #> 142 500 Amino acid metabolism #> 143 499 Translation #> 147 363 Amino acid metabolism #> 148 500 Translation #> 149 393 Amino acid metabolism #> 150 500 Metabolism of other amino acids #> 152 500 Translation #> 156 500 Lipid metabolism #> 157 487 Membrane transport #> 158 500 Amino acid metabolism #> 161 439 Metabolism of terpenoids and polyketides #> 164 423 Energy metabolism #> 165 500 Energy metabolism #> 166 261 Signal transduction #> 167 500 Translation #> 169 500 Nucleotide metabolism #> 170 466 Signal transduction #> 171 500 Amino acid metabolism #> 172 500 Metabolism of other amino acids #> 174 359 Translation #> 178 336 Nucleotide metabolism #> 179 500 Translation #> 180 500 Metabolism of other amino acids #> 181 500 Nucleotide metabolism #> 183 478 Nucleotide metabolism #> 184 500 Metabolism of other amino acids #> 185 500 Amino acid metabolism #> 186 295 Nucleotide metabolism #> 188 482 Metabolism of terpenoids and polyketides #> 190 344 Translation #> 193 483 Membrane transport #> 196 498 Transport and catabolism #> 197 500 Energy metabolism #> 198 500 Metabolism of terpenoids and polyketides #> 199 500 Amino acid metabolism #> 200 500 Energy metabolism #> 201 476 Energy metabolism #> 202 476 Metabolism of other amino acids #> 203 476 Amino acid metabolism #> 205 496 Lipid metabolism #> 207 479 Energy metabolism #> 208 260 Amino acid metabolism #> 211 493 Translation #> 214 494 Translation #> 215 305 Energy metabolism #> 217 500 Energy metabolism #> 218 497 Lipid metabolism #> 220 500 Energy metabolism #> 221 279 Translation #> 222 500 Lipid metabolism #> 225 317 Translation #> 228 261 Nucleotide metabolism #> 229 500 Amino acid metabolism #> 232 415 Amino acid metabolism #> 233 415 Metabolism of other amino acids #> 235 480 Nucleotide metabolism #> 236 251 Lipid metabolism #> 237 336 Amino acid metabolism #> 238 336 Metabolism of other amino acids #> 240 336 Energy metabolism #> 242 500 Signal transduction #> 243 333 Transport and catabolism #> 245 315 Nucleotide metabolism #> 246 315 Metabolism of other amino acids #> 248 500 Translation #> 251 500 Energy metabolism #> 252 500 Metabolism of terpenoids and polyketides #> 253 499 Nucleotide metabolism #> 255 275 Lipid metabolism #> 256 479 Membrane transport #> 257 293 Lipid metabolism #> 259 500 Lipid metabolism #> 260 473 Translation #> 263 500 Amino acid metabolism #> 265 500 Lipid metabolism #> 266 446 Metabolism of terpenoids and polyketides #> 270 303 Translation #> 272 500 Energy metabolism #> 274 468 Energy metabolism #> 275 454 Metabolism of terpenoids and polyketides #> 276 491 Metabolism of terpenoids and polyketides #> 277 500 Energy metabolism #> 279 370 Amino acid metabolism #> 280 376 Amino acid metabolism #> 282 441 Lipid metabolism #> 285 415 Translation #> 286 500 Translation #> 287 500 Energy metabolism #> 288 500 Amino acid metabolism #> 291 500 Metabolism of terpenoids and polyketides #> 293 377 Metabolism of terpenoids and polyketides #> 295 500 Translation #> 296 483 Lipid metabolism #> 297 500 Signal transduction #> 298 500 Lipid metabolism #> 299 326 Amino acid metabolism #> 301 500 Metabolism of other amino acids #> 303 500 Translation #> 305 275 Metabolism of terpenoids and polyketides #> 306 500 Metabolism of other amino acids #> 307 500 Amino acid metabolism #> 308 498 Amino acid metabolism #> 312 491 Transport and catabolism #> 314 497 Amino acid metabolism #> 317 360 Lipid metabolism #> 320 297 Energy metabolism #> 321 297 Amino acid metabolism #> 322 483 Energy metabolism #> 327 474 Nucleotide metabolism #> 328 414 Amino acid metabolism #> 330 335 Amino acid metabolism #> 331 500 Lipid metabolism #> 332 500 Signal transduction #> 333 494 Lipid metabolism #> 336 464 Translation #> 338 500 Metabolism of terpenoids and polyketides #> 339 488 Translation #> 340 388 Translation #> 341 403 Transport and catabolism #> 342 500 Signal transduction #> 343 439 Amino acid metabolism #> 344 469 Nucleotide metabolism #> 346 500 Metabolism of other amino acids #> 350 371 Amino acid metabolism #> 354 385 Nucleotide metabolism #> 357 476 Lipid metabolism #> 358 499 Lipid metabolism #> 360 499 Amino acid metabolism #> 361 291 Nucleotide metabolism #> 362 487 Lipid metabolism #> 364 500 Nucleotide metabolism #> 365 500 Energy metabolism #> 368 399 Energy metabolism #> 370 465 Transport and catabolism #> 372 406 Metabolism of other amino acids #> 375 330 Transport and catabolism #> 376 500 Translation #> 378 318 Lipid metabolism #> 379 436 Amino acid metabolism #> 380 252 Energy metabolism #> 384 490 Translation #> 386 500 Amino acid metabolism #> 387 500 Energy metabolism #> 389 345 Metabolism of other amino acids #> 391 500 Lipid metabolism #> 394 475 Translation #> 395 500 Amino acid metabolism #> 397 300 Translation #> 399 466 Transport and catabolism #> 401 413 Energy metabolism #> 404 396 Lipid metabolism #> 405 500 Metabolism of terpenoids and polyketides #> 408 303 Metabolism of other amino acids #> 409 497 Amino acid metabolism #> 410 352 Translation #> 411 480 Energy metabolism #> 414 500 Energy metabolism #> 416 385 Lipid metabolism #> 419 266 Transport and catabolism #> 420 303 Translation #> 422 500 Energy metabolism #> 427 336 Lipid metabolism #> 428 336 Amino acid metabolism #> 430 317 Membrane transport #> 433 311 Energy metabolism #> 437 500 Translation #> 439 261 Amino acid metabolism #> 441 261 Lipid metabolism #> 443 500 Lipid metabolism #> 445 485 Metabolism of other amino acids #> 447 291 Translation #> 450 500 Energy metabolism #> 453 486 Translation #> 454 486 Signal transduction #> 457 282 Signal transduction #> 458 282 Transport and catabolism #> 460 500 Membrane transport #> 461 500 Translation #> 464 500 Lipid metabolism #> 465 420 Amino acid metabolism #> 466 375 Energy metabolism #> 467 298 Energy metabolism #> 468 265 Translation #> 469 489 Translation #> 470 500 Translation #> 471 377 Energy metabolism #> 473 500 Translation #> 475 366 Transport and catabolism #> 476 473 Metabolism of other amino acids #> 479 500 Energy metabolism #> 480 500 Signal transduction #> 481 336 Lipid metabolism #> 482 488 Translation #> 483 500 Translation #> 486 384 Translation #> 488 484 Metabolism of other amino acids #> 490 490 Membrane transport #> 491 490 Energy metabolism #> 492 306 Transport and catabolism #> 493 500 Energy metabolism #> 494 492 Membrane transport #> 495 493 Lipid metabolism #> 496 500 Translation #> 498 500 Amino acid metabolism #> 499 500 Metabolism of other amino acids #> 500 271 Translation #> 501 499 Metabolism of other amino acids #> 502 500 Translation #> 504 431 Energy metabolism #> 505 385 Translation #> 509 276 Amino acid metabolism #> 513 309 Membrane transport #> 514 309 Signal transduction #> 515 500 Translation #> 517 289 Amino acid metabolism #> 518 498 Translation #> 519 360 Membrane transport #> 520 366 Energy metabolism #> 521 497 Translation #> 522 256 Energy metabolism #> 523 500 Energy metabolism #> 525 472 Translation #> 526 500 Translation #> 527 461 Translation #> 528 500 Translation #> 531 334 Lipid metabolism #> 534 434 Metabolism of other amino acids #> 535 293 Amino acid metabolism #> 540 461 Metabolism of other amino acids #> 542 490 Translation #> 543 405 Energy metabolism #> 544 332 Energy metabolism #> 546 500 Lipid metabolism #> 547 351 Lipid metabolism #> 549 483 Metabolism of terpenoids and polyketides #> 551 464 Membrane transport #> 552 500 Transport and catabolism #> 553 500 Metabolism of terpenoids and polyketides #> 554 266 Nucleotide metabolism #> 556 456 Energy metabolism #> 558 500 Amino acid metabolism #> 559 423 Translation #> 561 357 Lipid metabolism #> 562 357 Amino acid metabolism #> 565 481 Translation #> 566 470 Amino acid metabolism #> 567 268 Transport and catabolism #> 569 500 Lipid metabolism #> 571 448 Nucleotide metabolism #> 572 500 Translation #> 573 500 Lipid metabolism #> 574 391 Lipid metabolism #> 575 301 Amino acid metabolism #> 577 371 Nucleotide metabolism #> 579 500 Membrane transport #> 580 500 Signal transduction #> 581 500 Translation #> 583 433 Translation #> 585 324 Amino acid metabolism #> 586 493 Transport and catabolism #> 587 493 Membrane transport #> 588 275 Translation #> 590 500 Energy metabolism #> 591 368 Amino acid metabolism #> 593 343 Metabolism of other amino acids #> 595 500 Amino acid metabolism #> 596 332 Energy metabolism #> 597 440 Nucleotide metabolism #> 598 452 Signal transduction #> 599 452 Lipid metabolism #> 603 500 Lipid metabolism #> 604 250 Nucleotide metabolism #> 606 500 Energy metabolism #> 608 301 Nucleotide metabolism #> 610 280 Nucleotide metabolism #> 611 494 Metabolism of terpenoids and polyketides #> 612 283 Lipid metabolism #> 614 283 Metabolism of other amino acids #> 615 283 Amino acid metabolism #> 616 500 Amino acid metabolism #> 617 491 Lipid metabolism #> 618 461 Lipid metabolism #> 619 461 Amino acid metabolism #> 620 312 Nucleotide metabolism #> 623 251 Lipid metabolism #> 624 500 Energy metabolism #> 626 462 Lipid metabolism #> 630 264 Nucleotide metabolism #> 631 460 Amino acid metabolism #> 634 500 Amino acid metabolism #> 641 496 Nucleotide metabolism #> 642 450 Translation #> 643 500 Translation #> 644 296 Translation #> 645 497 Nucleotide metabolism #> 646 305 Translation #> molecular.level #> 1 metabolites #> 2 metabolites #> 5 metabolites #> 6 metabolites #> 7 metabolites #> 8 metabolites #> 9 metabolites #> 10 metabolites #> 11 metabolites #> 12 metabolites #> 13 metabolites #> 14 metabolites #> 16 metabolites #> 17 metabolites #> 18 metabolites #> 19 metabolites #> 20 metabolites #> 22 metabolites #> 23 metabolites #> 25 metabolites #> 26 metabolites #> 27 metabolites #> 28 metabolites #> 29 metabolites #> 30 metabolites #> 31 metabolites #> 32 metabolites #> 33 metabolites #> 34 metabolites #> 35 metabolites #> 36 metabolites #> 37 metabolites #> 39 metabolites #> 40 metabolites #> 41 metabolites #> 42 metabolites #> 43 metabolites #> 44 metabolites #> 46 metabolites #> 47 metabolites #> 48 metabolites #> 49 metabolites #> 50 metabolites #> 51 metabolites #> 52 metabolites #> 53 metabolites #> 54 metabolites #> 55 metabolites #> 56 metabolites #> 57 metabolites #> 58 metabolites #> 59 metabolites #> 60 metabolites #> 61 metabolites #> 62 metabolites #> 63 metabolites #> 64 metabolites #> 65 metabolites #> 67 metabolites #> 68 metabolites #> 69 metabolites #> 70 metabolites #> 71 metabolites #> 72 metabolites #> 73 metabolites #> 75 metabolites #> 76 metabolites #> 77 metabolites #> 79 metabolites #> 80 metabolites #> 81 metabolites #> 82 metabolites #> 83 metabolites #> 84 metabolites #> 85 contigs #> 86 contigs #> 87 contigs #> 88 contigs #> 91 contigs #> 95 contigs #> 99 contigs #> 102 contigs #> 103 contigs #> 104 contigs #> 106 contigs #> 109 contigs #> 110 contigs #> 111 contigs #> 115 contigs #> 116 contigs #> 117 contigs #> 120 contigs #> 121 contigs #> 122 contigs #> 123 contigs #> 125 contigs #> 127 contigs #> 128 contigs #> 129 contigs #> 131 contigs #> 132 contigs #> 133 contigs #> 134 contigs #> 137 contigs #> 140 contigs #> 141 contigs #> 142 contigs #> 143 contigs #> 147 contigs #> 148 contigs #> 149 contigs #> 150 contigs #> 152 contigs #> 156 contigs #> 157 contigs #> 158 contigs #> 161 contigs #> 164 contigs #> 165 contigs #> 166 contigs #> 167 contigs #> 169 contigs #> 170 contigs #> 171 contigs #> 172 contigs #> 174 contigs #> 178 contigs #> 179 contigs #> 180 contigs #> 181 contigs #> 183 contigs #> 184 contigs #> 185 contigs #> 186 contigs #> 188 contigs #> 190 contigs #> 193 contigs #> 196 contigs #> 197 contigs #> 198 contigs #> 199 contigs #> 200 contigs #> 201 contigs #> 202 contigs #> 203 contigs #> 205 contigs #> 207 contigs #> 208 contigs #> 211 contigs #> 214 contigs #> 215 contigs #> 217 contigs #> 218 contigs #> 220 contigs #> 221 contigs #> 222 contigs #> 225 contigs #> 228 contigs #> 229 contigs #> 232 contigs #> 233 contigs #> 235 contigs #> 236 contigs #> 237 contigs #> 238 contigs #> 240 contigs #> 242 contigs #> 243 contigs #> 245 contigs #> 246 contigs #> 248 contigs #> 251 contigs #> 252 contigs #> 253 contigs #> 255 contigs #> 256 contigs #> 257 contigs #> 259 contigs #> 260 contigs #> 263 contigs #> 265 contigs #> 266 contigs #> 270 contigs #> 272 contigs #> 274 contigs #> 275 contigs #> 276 contigs #> 277 contigs #> 279 contigs #> 280 contigs #> 282 contigs #> 285 contigs #> 286 contigs #> 287 contigs #> 288 contigs #> 291 contigs #> 293 contigs #> 295 contigs #> 296 contigs #> 297 contigs #> 298 contigs #> 299 contigs #> 301 contigs #> 303 contigs #> 305 contigs #> 306 contigs #> 307 contigs #> 308 contigs #> 312 contigs #> 314 contigs #> 317 contigs #> 320 contigs #> 321 contigs #> 322 contigs #> 327 contigs #> 328 contigs #> 330 contigs #> 331 contigs #> 332 contigs #> 333 contigs #> 336 contigs #> 338 contigs #> 339 contigs #> 340 contigs #> 341 contigs #> 342 contigs #> 343 contigs #> 344 contigs #> 346 contigs #> 350 contigs #> 354 contigs #> 357 contigs #> 358 contigs #> 360 contigs #> 361 contigs #> 362 contigs #> 364 contigs #> 365 contigs #> 368 contigs #> 370 contigs #> 372 contigs #> 375 contigs #> 376 contigs #> 378 contigs #> 379 contigs #> 380 contigs #> 384 contigs #> 386 contigs #> 387 contigs #> 389 contigs #> 391 contigs #> 394 contigs #> 395 contigs #> 397 contigs #> 399 contigs #> 401 contigs #> 404 contigs #> 405 contigs #> 408 contigs #> 409 contigs #> 410 contigs #> 411 contigs #> 414 contigs #> 416 contigs #> 419 contigs #> 420 contigs #> 422 contigs #> 427 contigs #> 428 contigs #> 430 contigs #> 433 contigs #> 437 contigs #> 439 contigs #> 441 contigs #> 443 contigs #> 445 contigs #> 447 contigs #> 450 contigs #> 453 contigs #> 454 contigs #> 457 contigs #> 458 contigs #> 460 contigs #> 461 contigs #> 464 contigs #> 465 contigs #> 466 contigs #> 467 contigs #> 468 contigs #> 469 contigs #> 470 contigs #> 471 contigs #> 473 contigs #> 475 contigs #> 476 contigs #> 479 contigs #> 480 contigs #> 481 contigs #> 482 contigs #> 483 contigs #> 486 contigs #> 488 contigs #> 490 contigs #> 491 contigs #> 492 contigs #> 493 contigs #> 494 contigs #> 495 contigs #> 496 contigs #> 498 contigs #> 499 contigs #> 500 contigs #> 501 contigs #> 502 contigs #> 504 contigs #> 505 contigs #> 509 contigs #> 513 contigs #> 514 contigs #> 515 contigs #> 517 contigs #> 518 contigs #> 519 contigs #> 520 contigs #> 521 contigs #> 522 contigs #> 523 contigs #> 525 contigs #> 526 contigs #> 527 contigs #> 528 contigs #> 531 contigs #> 534 contigs #> 535 contigs #> 540 contigs #> 542 contigs #> 543 contigs #> 544 contigs #> 546 contigs #> 547 contigs #> 549 contigs #> 551 contigs #> 552 contigs #> 553 contigs #> 554 contigs #> 556 contigs #> 558 contigs #> 559 contigs #> 561 contigs #> 562 contigs #> 565 contigs #> 566 contigs #> 567 contigs #> 569 contigs #> 571 contigs #> 572 contigs #> 573 contigs #> 574 contigs #> 575 contigs #> 577 contigs #> 579 contigs #> 580 contigs #> 581 contigs #> 583 contigs #> 585 contigs #> 586 contigs #> 587 contigs #> 588 contigs #> 590 contigs #> 591 contigs #> 593 contigs #> 595 contigs #> 596 contigs #> 597 contigs #> 598 contigs #> 599 contigs #> 603 contigs #> 604 contigs #> 606 contigs #> 608 contigs #> 610 contigs #> 611 contigs #> 612 contigs #> 614 contigs #> 615 contigs #> 616 contigs #> 617 contigs #> 618 contigs #> 619 contigs #> 620 contigs #> 623 contigs #> 624 contigs #> 626 contigs #> 630 contigs #> 631 contigs #> 634 contigs #> 641 contigs #> 642 contigs #> 643 contigs #> 644 contigs #> 645 contigs #> 646 contigs sensitivityplot(extendedres.3, BMDtype = \"zSD\", group = \"path_class\", colorby = \"molecular.level\", BMDsummary = \"first.quartile\") # }"},{"path":"/reference/sensitivityplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of a summary of BMD values per group of items — sensitivityplot","title":"Plot of a summary of BMD values per group of items — sensitivityplot","text":"Plot summary BMD values per group items (groups defined example biological annotation), groups ordered values chosen summary (ECDF plot) ordered definition factor coding , points sized numbers items per group.","code":""},{"path":"/reference/sensitivityplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of a summary of BMD values per group of items — sensitivityplot","text":"","code":"sensitivityplot(extendedres, BMDtype = c(\"zSD\", \"xfold\"), group, ECDF_plot = TRUE, colorby, BMDsummary = c(\"first.quartile\", \"median\" , \"median.and.IQR\"), BMD_log_transfo = TRUE, line.size = 0.5, line.alpha = 0.5, point.alpha = 0.5)"},{"path":"/reference/sensitivityplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of a summary of BMD values per group of items — sensitivityplot","text":"extendedres dataframe results provided bmdcalc (res) subset data frame (selected lines). dataframe can extended additional columns coming example annotation items, lines can replicated corresponding item one annotation. extended dataframe must least contain column giving chosen BMD values compute sensitivity (column BMD.zSD BMD.xfold). BMDtype type BMD used, \"zSD\" (default choice) \"xfold\". group name column extendedres coding groups want estimate global sensitivity. ECDF_plot FALSE, column factor ordered want groups appear plot bottom . ECDF_plot TRUE (default choice) groups appear ordered values BMD summary value bottom , else ordered corresponding levels factor given group. colorby given, ECDF_plot fixed FALSE. colorby optional argument naming column extendedres coding additional level grouping materialized color. missing, ECDF_plot fixed FALSE. BMDsummary type summary used sensitivity plot, \"first.quartile\" (default choice) plot first quartiles BMD values per group, \"median\" plot medians BMD values per group \"median..IQR\" plot medians interval corresponding inter-quartile range (IQR). BMD_log_transfo TRUE, default choice, log transformation BMD used plot. line.size Width lines. line.alpha Transparency lines. point.alpha Transparency points.","code":""},{"path":"/reference/sensitivityplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot of a summary of BMD values per group of items — sensitivityplot","text":"chosen summary calculated BMD values group (groups can example defined pathways biological annotation items) plotted ECDF plot (ordered BMD summary) order levels factor defining groups bottom . plot point sized according number items corresponding group. Optionally different levels (e.g. different molecular levels multi-omics approach) can coded different colors.","code":""},{"path":"/reference/sensitivityplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot of a summary of BMD values per group of items — sensitivityplot","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/sensitivityplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot of a summary of BMD values per group of items — sensitivityplot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/sensitivityplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot of a summary of BMD values per group of items — sensitivityplot","text":"","code":"# (1) An example from data published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # a dataframe with metabolomic results (output $res of bmdcalc() or bmdboot() functions) resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # a dataframe with annotation of each item identified in the previous file # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe # bootstrap results and annotation annotres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(annotres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport ### an ECDFplot of 25th quantiles of BMD-zSD calculated by pathway sensitivityplot(annotres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\") # \\donttest{ # same plot in raw BMD scale (so not in log scale) sensitivityplot(annotres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"first.quartile\", BMD_log_transfo = FALSE) ### Plot of 25th quantiles of BMD-zSD calculated by pathway ### in the order of the levels as defined in the group input levels(annotres$path_class) #> [1] \"Amino acid metabolism\" #> [2] \"Biosynthesis of other secondary metabolites\" #> [3] \"Carbohydrate metabolism\" #> [4] \"Energy metabolism\" #> [5] \"Lipid metabolism\" #> [6] \"Membrane transport\" #> [7] \"Metabolism of other amino acids\" #> [8] \"Signal transduction\" #> [9] \"Translation\" sensitivityplot(annotres, BMDtype = \"zSD\", group = \"path_class\", ECDF_plot = FALSE, BMDsummary = \"first.quartile\") ### an ECDFplot of medians of BMD-zSD calculated by pathway sensitivityplot(annotres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"median\") ### an ECDFplot of medians of BMD-zSD calculated by pathway ### with addition of interquartile ranges (IQRs) sensitivityplot(annotres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"median.and.IQR\") ### The same plot playing with graphical parameters sensitivityplot(annotres, BMDtype = \"zSD\", group = \"path_class\", BMDsummary = \"median.and.IQR\", line.size = 1.5, line.alpha = 0.4, point.alpha = 1) # (2) # An example with two molecular levels # ### Rename metabolomic results metabextendedres <- annotres # Import the dataframe with transcriptomic results contigresfilename <- system.file(\"extdata\", \"triclosanSVcontigres.txt\", package = \"DRomics\") contigres <- read.table(contigresfilename, header = TRUE, stringsAsFactors = TRUE) str(contigres) #> 'data.frame':\t447 obs. of 27 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... # Import the dataframe with functional annotation (or any other descriptor/category # you want to use, here KEGG pathway classes) contigannotfilename <- system.file(\"extdata\", \"triclosanSVcontigannot.txt\", package = \"DRomics\") contigannot <- read.table(contigannotfilename, header = TRUE, stringsAsFactors = TRUE) str(contigannot) #> 'data.frame':\t562 obs. of 2 variables: #> $ contig : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ path_class: Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... # Merging of both previous dataframes contigextendedres <- merge(x = contigres, y = contigannot, by.x = \"id\", by.y = \"contig\") # to see the structure of this dataframe str(contigextendedres) #> 'data.frame':\t562 obs. of 28 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... #> $ path_class : Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... ### Merge metabolomic and transcriptomic results extendedres <- rbind(metabextendedres, contigextendedres) extendedres$molecular.level <- factor(c(rep(\"metabolites\", nrow(metabextendedres)), rep(\"contigs\", nrow(contigextendedres)))) str(extendedres) #> 'data.frame':\t646 obs. of 29 variables: #> $ id : Factor w/ 478 levels \"NAP47_51\",\"NAP_2\",..: 1 2 3 4 4 4 4 5 6 7 ... #> $ irow : int 46 2 21 28 28 28 28 34 38 47 ... #> $ adjpvalue : num 7.16e-04 6.23e-05 1.11e-05 1.03e-05 1.03e-05 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 3 3 3 3 3 2 2 4 ... #> $ nbpar : int 2 3 2 2 2 2 2 3 3 5 ... #> $ b : num -0.056 0.4598 -0.0595 -0.0451 -0.0451 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 7.34 5.94 5.39 7.86 7.86 ... #> $ e : num NA -1.65 NA NA NA ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.1245 0.126 0.0793 0.052 0.052 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 7 7 7 7 7 2 2 9 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 3 3 3 3 1 ... #> $ y0 : num 7.34 5.94 5.39 7.86 7.86 ... #> $ yrange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ maxychange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 2.224 0.528 1.333 1.154 1.154 ... #> $ BMR.zSD : num 7.22 5.82 5.31 7.81 7.81 ... #> $ BMD.xfold : num NA NA NA NA NA ... #> $ BMR.xfold : num 6.61 5.35 4.85 7.07 7.07 ... #> $ BMD.zSD.lower : num 0.979 0.2 0.853 0.752 0.752 ... #> $ BMD.zSD.upper : num 4.07 1.11 1.75 1.46 1.46 ... #> $ BMD.xfold.lower : num Inf Inf 7.61 Inf Inf ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 1000 957 1000 1000 1000 1000 1000 648 620 872 ... #> $ path_class : Factor w/ 18 levels \"Amino acid metabolism\",..: 5 5 3 3 2 6 8 5 5 5 ... #> $ molecular.level : Factor w/ 2 levels \"contigs\",\"metabolites\": 2 2 2 2 2 2 2 2 2 2 ... ### Plot of 25th quantiles of BMD-zSD calculated by pathway ### and colored by molecular level # optional inverse alphabetic ordering of groups for the plot extendedres$path_class <- factor(extendedres$path_class, levels = sort(levels(extendedres$path_class), decreasing = TRUE)) sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", colorby = \"molecular.level\", BMDsummary = \"first.quartile\") ### Plot of medians and IQRs of BMD-zSD calculated by pathway ### and colored by molecular level sensitivityplot(extendedres, BMDtype = \"zSD\", group = \"path_class\", colorby = \"molecular.level\", BMDsummary = \"median.and.IQR\", line.size = 1.2, line.alpha = 0.4, point.alpha = 0.8) # }"},{"path":"/reference/targetplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Dose-reponse plot for target items — targetplot","title":"Dose-reponse plot for target items — targetplot","text":"Plots dose-response raw data target items (whether response considered significant) fitted curves available.","code":""},{"path":"/reference/targetplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dose-reponse plot for target items — targetplot","text":"","code":"targetplot(items, f, add.fit = TRUE, dose_log_transfo = TRUE)"},{"path":"/reference/targetplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dose-reponse plot for target items — targetplot","text":"items character vector specifying identifiers items plot. f object class \"drcfit\". add.fit TRUE fitted curve added items selected responsive items best fit model obtained. dose_log_transfo TRUE, default choice, log transformation used dose axis.","code":""},{"path":"/reference/targetplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dose-reponse plot for target items — targetplot","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/targetplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dose-reponse plot for target items — targetplot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/targetplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dose-reponse plot for target items — targetplot","text":"","code":"# A toy example on a very small subsample of a microarray data set) # datafilename <- system.file(\"extdata\", \"transcripto_very_small_sample.txt\", package=\"DRomics\") o <- microarraydata(datafilename, check = TRUE, norm.method = \"cyclicloess\") #> Just wait, the normalization using cyclicloess may take a few minutes. s_quad <- itemselect(o, select.method = \"quadratic\", FDR = 0.01) #> Removing intercept from test coefficients f <- drcfit(s_quad, progressbar = TRUE) #> The fitting may be long if the number of selected items is high. #> | | | 0% | |==== | 6% | |======== | 12% | |============ | 18% | |================ | 24% | |===================== | 29% | |========================= | 35% | |============================= | 41% | |================================= | 47% | |===================================== | 53% | |========================================= | 59% | |============================================= | 65% | |================================================= | 71% | |====================================================== | 76% | |========================================================== | 82% | |============================================================== | 88% | |================================================================== | 94% | |======================================================================| 100% # Plot of chosen items with fitted curves when available # targetitems <- c(\"88.1\", \"1\", \"3\", \"15\") targetplot(targetitems, f = f) #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # \\donttest{ # The same plot in raw scale instead of default log scale # targetplot(targetitems, f = f, dose_log_transfo = FALSE) # The same plot in x log scale choosing x limits for plot # to enlarge the space between the control and the non null doses # if (require(ggplot2)) targetplot(targetitems, f = f, dose_log_transfo = TRUE) + scale_x_log10(limits = c(0.1, 10)) #> Scale for x is already present. #> Adding another scale for x, which will replace the existing scale. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. #> Warning: log-10 transformation introduced infinite values. # The same plot without fitted curves # targetplot(targetitems, f = f, add.fit = FALSE) # }"},{"path":"/reference/trendplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of the repartition of trends per group — trendplot","title":"Plot of the repartition of trends per group — trendplot","text":"Provides plot repartition dose-response trends per group items.","code":""},{"path":"/reference/trendplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of the repartition of trends per group — trendplot","text":"","code":"trendplot(extendedres, group, facetby, ncol4faceting, add.color = TRUE)"},{"path":"/reference/trendplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of the repartition of trends per group — trendplot","text":"extendedres dataframe results provided drcfit (fitres) bmdcalc (res) subset data frame (selected lines). dataframe extended additional columns coming group (example functional annotation items) /another level (example molecular level), lines can replicated corresponding item one annotation. extended dataframe must least contain results dose-response modelling column giving trend (trend). group name column extendedres coding groups want see repartition dose-response trends. column factor ordered want groups appear plot bottom . facetby optional argument naming column extendedres chosen split plot facets using ggplot2::facet_wrap (split omitted). ncol4faceting number columns faceting. add.color TRUE color added coding trend.","code":""},{"path":"/reference/trendplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot of the repartition of trends per group — trendplot","text":"ggplot object.","code":""},{"path":[]},{"path":"/reference/trendplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot of the repartition of trends per group — trendplot","text":"Marie-Laure Delignette-Muller","code":""},{"path":"/reference/trendplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot of the repartition of trends per group — trendplot","text":"","code":"# (1) # An example from the paper published by Larras et al. 2020 # in Journal of Hazardous Materials # https://doi.org/10.1016/j.jhazmat.2020.122727 # the dataframe with metabolomic results resfilename <- system.file(\"extdata\", \"triclosanSVmetabres.txt\", package=\"DRomics\") res <- read.table(resfilename, header = TRUE, stringsAsFactors = TRUE) str(res) #> 'data.frame':\t31 obs. of 27 variables: #> $ id : Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 5 6 7 8 9 10 11 ... #> $ irow : int 2 21 28 34 38 47 49 51 53 67 ... #> $ adjpvalue : num 6.23e-05 1.11e-05 1.03e-05 1.89e-03 4.16e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 2 3 3 2 2 4 2 2 3 3 ... #> $ nbpar : int 3 2 2 3 3 5 3 3 2 2 ... #> $ b : num 0.4598 -0.0595 -0.0451 0.6011 0.6721 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 5.94 5.39 7.86 6.86 6.21 ... #> $ e : num -1.648 NA NA -0.321 -0.323 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.126 0.0793 0.052 0.2338 0.2897 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 2 7 7 2 2 9 2 2 7 7 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 1 3 3 3 3 ... #> $ y0 : num 5.94 5.39 7.86 6.86 6.21 ... #> $ yrange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ maxychange : num 0.456 0.461 0.35 0.601 0.672 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 0.528 1.333 1.154 0.158 0.182 ... #> $ BMR.zSD : num 5.82 5.31 7.81 6.62 5.92 ... #> $ BMD.xfold : num NA NA NA NA 0.832 ... #> $ BMR.xfold : num 5.35 4.85 7.07 6.17 5.59 ... #> $ BMD.zSD.lower : num 0.2001 0.8534 0.7519 0.0554 0.081 ... #> $ BMD.zSD.upper : num 1.11 1.746 1.465 0.68 0.794 ... #> $ BMD.xfold.lower : num Inf 7.611 Inf 0.561 0.329 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 957 1000 1000 648 620 872 909 565 1000 1000 ... # the dataframe with annotation of each item identified in the previous file # each item may have more than one annotation (-> more than one line) annotfilename <- system.file(\"extdata\", \"triclosanSVmetabannot.txt\", package=\"DRomics\") annot <- read.table(annotfilename, header = TRUE, stringsAsFactors = TRUE) str(annot) #> 'data.frame':\t84 obs. of 2 variables: #> $ metab.code: Factor w/ 31 levels \"NAP47_51\",\"NAP_2\",..: 2 3 4 4 4 4 5 6 7 8 ... #> $ path_class: Factor w/ 9 levels \"Amino acid metabolism\",..: 5 3 3 2 6 8 5 5 5 5 ... # Merging of both previous dataframes # in order to obtain an extenderes dataframe extendedres <- merge(x = res, y = annot, by.x = \"id\", by.y = \"metab.code\") head(extendedres) #> id irow adjpvalue model nbpar b c d #> 1 NAP47_51 46 7.158246e-04 linear 2 -0.05600559 NA 7.343571 #> 2 NAP_2 2 6.232579e-05 exponential 3 0.45981242 NA 5.941896 #> 3 NAP_23 21 1.106958e-05 linear 2 -0.05946618 NA 5.387252 #> 4 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 5 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> 6 NAP_30 28 1.028343e-05 linear 2 -0.04507832 NA 7.859109 #> e f SDres typology trend y0 yrange maxychange #> 1 NA NA 0.12454183 L.dec dec 7.343571 0.4346034 0.4346034 #> 2 -1.647958 NA 0.12604568 E.dec.convex dec 5.941896 0.4556672 0.4556672 #> 3 NA NA 0.07929266 L.dec dec 5.387252 0.4614576 0.4614576 #> 4 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 5 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> 6 NA NA 0.05203245 L.dec dec 7.859109 0.3498078 0.3498078 #> xextrem yextrem BMD.zSD BMR.zSD BMD.xfold BMR.xfold BMD.zSD.lower #> 1 NA NA 2.2237393 7.219029 NA 6.609214 0.9785095 #> 2 NA NA 0.5279668 5.815850 NA 5.347706 0.2000881 #> 3 NA NA 1.3334076 5.307960 NA 4.848527 0.8533711 #> 4 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 5 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> 6 NA NA 1.1542677 7.807077 NA 7.073198 0.7518588 #> BMD.zSD.upper BMD.xfold.lower BMD.xfold.upper nboot.successful #> 1 4.068699 Inf Inf 1000 #> 2 1.109559 Inf Inf 957 #> 3 1.746010 7.610936 Inf 1000 #> 4 1.464998 Inf Inf 1000 #> 5 1.464998 Inf Inf 1000 #> 6 1.464998 Inf Inf 1000 #> path_class #> 1 Lipid metabolism #> 2 Lipid metabolism #> 3 Carbohydrate metabolism #> 4 Carbohydrate metabolism #> 5 Biosynthesis of other secondary metabolites #> 6 Membrane transport # (1.a) Trendplot by pathway trendplot(extendedres, group = \"path_class\") # \\donttest{ # (1.b) Trendplot by pathway without color trendplot(extendedres, group = \"path_class\", add.color = FALSE) # (1.c) Reordering of the groups before plotting extendedres$path_class <- factor(extendedres$path_class, levels = sort(levels(extendedres$path_class), decreasing = TRUE)) trendplot(extendedres, group = \"path_class\", add.color = FALSE) # (2) # An example with two molecular levels # ### Rename metabolomic results metabextendedres <- extendedres # Import the dataframe with transcriptomic results contigresfilename <- system.file(\"extdata\", \"triclosanSVcontigres.txt\", package = \"DRomics\") contigres <- read.table(contigresfilename, header = TRUE, stringsAsFactors = TRUE) str(contigres) #> 'data.frame':\t447 obs. of 27 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... # Import the dataframe with functional annotation (or any other descriptor/category # you want to use, here KEGG pathway classes) contigannotfilename <- system.file(\"extdata\", \"triclosanSVcontigannot.txt\", package = \"DRomics\") contigannot <- read.table(contigannotfilename, header = TRUE, stringsAsFactors = TRUE) str(contigannot) #> 'data.frame':\t562 obs. of 2 variables: #> $ contig : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ path_class: Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... # Merging of both previous dataframes contigextendedres <- merge(x = contigres, y = contigannot, by.x = \"id\", by.y = \"contig\") # to see the structure of this dataframe str(contigextendedres) #> 'data.frame':\t562 obs. of 28 variables: #> $ id : Factor w/ 447 levels \"c00134\",\"c00276\",..: 1 2 3 4 5 6 7 8 9 10 ... #> $ irow : int 2802 39331 41217 52577 52590 53968 54508 57776 58705 60306 ... #> $ adjpvalue : num 2.76e-04 9.40e-07 2.89e-06 1.88e-03 1.83e-03 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 2 2 2 2 3 2 1 3 ... #> $ nbpar : int 2 3 3 3 3 3 2 3 4 2 ... #> $ b : num -0.21794 1.49944 1.40817 0.00181 1.48605 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 10.9 12.4 12.4 16.4 15.3 ... #> $ e : num NA -2.2 -2.41 1.15 -2.31 ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.417 0.287 0.281 0.145 0.523 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 2 4 2 2 7 1 5 8 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 4 3 3 3 3 1 4 ... #> $ y0 : num 10.9 12.4 12.4 16.4 15.3 ... #> $ yrange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ maxychange : num 1.445 1.426 1.319 0.567 1.402 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 1.913 0.467 0.536 5.073 1.004 ... #> $ BMR.zSD : num 10.4 12.1 12.1 16.6 14.8 ... #> $ BMD.xfold : num 4.98 3.88 5.13 NA NA ... #> $ BMR.xfold : num 9.77 11.19 11.17 18.05 13.8 ... #> $ BMD.zSD.lower : num 1.255 0.243 0.282 2.65 0.388 ... #> $ BMD.zSD.upper : num 2.759 0.825 0.925 5.573 2.355 ... #> $ BMD.xfold.lower : num 3.94 2.32 2.79 Inf 3.06 ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 500 497 495 332 466 469 500 321 260 500 ... #> $ path_class : Factor w/ 17 levels \"Amino acid metabolism\",..: 3 11 11 15 8 4 3 4 8 2 ... ### Merge metabolomic and transcriptomic results extendedres <- rbind(metabextendedres, contigextendedres) extendedres$molecular.level <- factor(c(rep(\"metabolites\", nrow(metabextendedres)), rep(\"contigs\", nrow(contigextendedres)))) str(extendedres) #> 'data.frame':\t646 obs. of 29 variables: #> $ id : Factor w/ 478 levels \"NAP47_51\",\"NAP_2\",..: 1 2 3 4 4 4 4 5 6 7 ... #> $ irow : int 46 2 21 28 28 28 28 34 38 47 ... #> $ adjpvalue : num 7.16e-04 6.23e-05 1.11e-05 1.03e-05 1.03e-05 ... #> $ model : Factor w/ 4 levels \"Gauss-probit\",..: 3 2 3 3 3 3 3 2 2 4 ... #> $ nbpar : int 2 3 2 2 2 2 2 3 3 5 ... #> $ b : num -0.056 0.4598 -0.0595 -0.0451 -0.0451 ... #> $ c : num NA NA NA NA NA ... #> $ d : num 7.34 5.94 5.39 7.86 7.86 ... #> $ e : num NA -1.65 NA NA NA ... #> $ f : num NA NA NA NA NA ... #> $ SDres : num 0.1245 0.126 0.0793 0.052 0.052 ... #> $ typology : Factor w/ 10 levels \"E.dec.concave\",..: 7 2 7 7 7 7 7 2 2 9 ... #> $ trend : Factor w/ 4 levels \"U\",\"bell\",\"dec\",..: 3 3 3 3 3 3 3 3 3 1 ... #> $ y0 : num 7.34 5.94 5.39 7.86 7.86 ... #> $ yrange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ maxychange : num 0.435 0.456 0.461 0.35 0.35 ... #> $ xextrem : num NA NA NA NA NA ... #> $ yextrem : num NA NA NA NA NA ... #> $ BMD.zSD : num 2.224 0.528 1.333 1.154 1.154 ... #> $ BMR.zSD : num 7.22 5.82 5.31 7.81 7.81 ... #> $ BMD.xfold : num NA NA NA NA NA ... #> $ BMR.xfold : num 6.61 5.35 4.85 7.07 7.07 ... #> $ BMD.zSD.lower : num 0.979 0.2 0.853 0.752 0.752 ... #> $ BMD.zSD.upper : num 4.07 1.11 1.75 1.46 1.46 ... #> $ BMD.xfold.lower : num Inf Inf 7.61 Inf Inf ... #> $ BMD.xfold.upper : num Inf Inf Inf Inf Inf ... #> $ nboot.successful: int 1000 957 1000 1000 1000 1000 1000 648 620 872 ... #> $ path_class : Factor w/ 18 levels \"Translation\",..: 5 5 7 7 8 4 2 5 5 5 ... #> $ molecular.level : Factor w/ 2 levels \"contigs\",\"metabolites\": 2 2 2 2 2 2 2 2 2 2 ... ### trend plot of both molecular levels # optional inverse alphabetic ordering of groups for the plot extendedres$path_class <- factor(extendedres$path_class, levels = sort(levels(extendedres$path_class), decreasing = TRUE)) trendplot(extendedres, group = \"path_class\", facetby = \"molecular.level\") # }"},{"path":"/reference/zebraf.html","id":null,"dir":"Reference","previous_headings":"","what":"Transcriptomic dose-response to ionizing radiation in zebrafish with batch effect — zebraf","title":"Transcriptomic dose-response to ionizing radiation in zebrafish with batch effect — zebraf","text":"sample RNAseq data set dose-response chronic exposure ionizing radiation zebrafish embryo fertilization 48 hours post-fertilization corresponding batch effect experiment.","code":""},{"path":"/reference/zebraf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transcriptomic dose-response to ionizing radiation in zebrafish with batch effect — zebraf","text":"","code":"data(zebraf)"},{"path":"/reference/zebraf.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Transcriptomic dose-response to ionizing radiation in zebrafish with batch effect — zebraf","text":"zebraf contains list three objects, zebraf$counts integer matrix counts reads (1000 rows sample pf 1000 transcripts 16 columns 16 sampels), zebraf$dose, numeric vector coding dose sample zebraf$batch factor coding batch sample.","code":""},{"path":[]},{"path":"/reference/zebraf.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transcriptomic dose-response to ionizing radiation in zebrafish with batch effect — zebraf","text":"Murat El Houdigui, S., Adam-Guillermin, C., Loro, G., Arcanjo, C., Frelon, S., Floriani, M., ... & Armant, O. 2019. systems biology approach reveals neuronal muscle developmental defects chronic exposure ionising radiation zebrafish. Scientific reports, 9(1), 1-15.","code":""},{"path":"/reference/zebraf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Transcriptomic dose-response to ionizing radiation in zebrafish with batch effect — zebraf","text":"Zhang, Y., Parmigiani, G., & Johnson, W. E. (2020). ComBat-seq: batch effect adjustment RNA-seq count data. NAR genomics bioinformatics, 2(3), lqaa078.","code":""},{"path":"/reference/zebraf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transcriptomic dose-response to ionizing radiation in zebrafish with batch effect — zebraf","text":"","code":"# (1) load of data # data(zebraf) str(zebraf) #> List of 3 #> $ counts: int [1:1000, 1:16] 453 331 897 12 326 533 1948 904 583 154 ... #> ..- attr(*, \"dimnames\")=List of 2 #> .. ..$ : chr [1:1000] \"ENSDARG00000102141\" \"ENSDARG00000102123\" \"ENSDARG00000114503\" \"ENSDARG00000115971\" ... #> .. ..$ : chr [1:16] \"I10_05mG_E5\" \"I10_05mG_E6\" \"I10_05mG_E7\" \"I10_C5\" ... #> $ dose : num [1:16] 500 500 500 0 0 0 0 50000 50000 50000 ... #> $ batch : Factor w/ 2 levels \"I10\",\"I17\": 1 1 1 1 1 1 1 2 2 2 ... # (2) formating of data for use in DRomics # data4DRomics <- formatdata4DRomics(signalmatrix = zebraf$counts, dose = zebraf$dose) # (3) Normalization and transformation of data followed # by PCA plot with vizualisation of the batch effect # o <- RNAseqdata(data4DRomics, transfo.method = \"vst\") #> converting counts to integer mode #> converting counts to integer mode #> Warning: #> To optimize the dose-response modelling, it is recommended to use a #> dose-response design with at least six different tested doses. PCAdataplot(o, batch = zebraf$batch) # \\donttest{ PCAdataplot(o, label = TRUE) # (4) Batch effect correction using ComBat_seq{sva} # if(!requireNamespace(\"sva\", quietly = TRUE)) { BECcounts <- ComBat_seq(as.matrix(o$raw.counts), batch = as.factor(zebraf$batch), group = as.factor(o$dose)) BECdata4DRomics <- formatdata4DRomics(signalmatrix = BECcounts, dose = o$dose) (o.BEC <- RNAseqdata(BECdata4DRomics, transfo.method = \"vst\")) plot(o.BEC) PCAdataplot(o.BEC, batch = zebraf$batch) PCAdataplot(o.BEC, label = TRUE) } # }"},{"path":"/news/index.html","id":"dromics-development-version","dir":"Changelog","previous_headings":"","what":"DRomics (development version)","title":"DRomics (development version)","text":"NEW FEATURES Add component output RNAseqdata, continuousomicdata(), continuousanchoringdata(), microarraydata() (data.sd, gives, item, sd response per condition - NA replicate condition). Add new argument drcfit(), named deltaAICminfromnullmodel, order relax requirements information criterion keep best fitted model (see ? drcfit()). Modification curvesplot() able put argument dose_log_transfo default TRUE functions. Curvesplot now use minimum maximum values chosen BMD fix rage theoretical curve calculated (plotted) ad chosen BMD required input function. Add argument dose_log_transfo plot.continuousanchoringdata(), default TRUE. plot x log scale, add label x axis. Add output drcfit named information.criterion.val information criterion value null model change names components (replacement AIC InfoCrit names). BUG FIXES Add sample names column names output formatdata4DRomics. Change default value range4boxplot (plot.RNAseqdata(), plot.continuousomicdata(), plot.microarraydata()) 0 instead 1e6 whiskers always go extrems.","code":""},{"path":"/news/index.html","id":"dromics-25-2","dir":"Changelog","previous_headings":"","what":"DRomics 2.5-2","title":"DRomics 2.5-2","text":"CRAN release: 2024-01-31 NEW FEATURES Put argument dose_log_transfo default TRUE functions plot.drcfit(), plotfit2pdf(), targetplot() BMD_log_transfo TRUE functions bmdplot(), bmdplotwithgradient() sensitivityplot(). Add argument BMD_log_transfo default TRUE functions plot.bmdcalc() plot.bmdboot(). Put argument scaling default TRUE curvesplot() bmdplotwithgradient(). Add xlab ylab plots curvesplot() (signal scaled signal y-axis) change color lab “scaled signal” plots bmdplotwithgradient() signal scaled. Add possibility (new argument addBMD curvesplot()) add points BMD-BMR values curvesplots put default TRUE. Add Peer Community Journal citation. Add function bmdfilter() proposing filters retain items associated best estimated BMD values DRomics workflow output. Add arguments line.size, line.alpha point.alpha sensitivityplot() bmdplot() Add free y scale plots residuals, make readable even anchoring data endpoints different orders magnitude. BUG FIXES Fix bug appeared occasionally bootstrap procedure (error bmdboot() due fail call uniroot()). Define scale nb items sensitivityplot() trendplot() get 4 integer values min max rounded 0.5 0.75 quartiles. Fix bug plotfit2pdf : now items appear order (p-value selection) even BMD values added plot fitted curves. Fix bug drcfit occur anchoring data sets many NA values.","code":""},{"path":"/news/index.html","id":"dromics-25-0","dir":"Changelog","previous_headings":"","what":"DRomics 2.5-0","title":"DRomics 2.5-0","text":"CRAN release: 2023-01-24 NEW FEATURES Add function selectgroups() select represented /sensitive groups focus biological interpretation. Add RNAseq data batch effect (zebraf) example use ComBat_seq{sva} correct batch effect. Add PCAdataplot() function visualize omic data. Add column named maxychange (maximal absolute y change () control) output drcfit() (bmdcalc() bmdboot()) Add argument named scaling curvesplot() bmdplotwithgradient() enables scaling shifted signal (y - y0) dividing maxychange (new output drcfit). Add function formatdata4DRomics() format data DRomics matrix signal measurements vector observed/tested doses. Add range4boxplot default fixed 1e6 arguments plot functions RNAseqdata(), microarraydata() continuousomicdata() objects, prevent automatic plot many outliers individual points produce lighter plot files. Change default value transfo.method RNAseqdata() (put “vst” number samples larger 30) BUG FIXES Make sensitivityplot works even BMDsummary given input (“first.quartile” defined default)","code":""},{"path":"/news/index.html","id":"dromics-24-0","dir":"Changelog","previous_headings":"","what":"DRomics 2.4-0","title":"DRomics 2.4-0","text":"CRAN release: 2022-01-06 NEW FEATURES Forbid use “ANOVA” method select items half doses without replicates (e.g. situ data) Add example data set named insitu_RNAseq_sample.txt tests examples Add arguments BMDoutput BMDtype plot.drcfit() plotfit2pdf make possible add BMD values confidence intervals plot fits. Add argument (enablesfequal0inGP) default TRUE drcfit(), enable simplification Gauss-probit model 5 parameters version f = 0 (corresponds probit model) prevent overfitting parameter f close 0 (evaluated using information criterion). Add argument (enablesfequal0inLGP) default TRUE drcfit(), enable simplification log-Gauss-probit model 5 parameters version f = 0 (corresponds log-probit model) prevent overfitting parameter f close 0 (evaluated using information criterion). Add argument (preventsfitsoutofrange) default TRUE drcfit() prevent fits biphasic models giving extreme value range observed signal, happen rare cases. Add defensive code microarraydata(), continuousomicdata(), continuousanchoringdata(), RNAseqdata(), argument backgrounddose added prevent use DRomics design dose zero. case observationnal data, prevent calculation BMDs extrapolation, doses considered corresponding background exposition must fixed 0, example using new argument.","code":""},{"path":"/news/index.html","id":"dromics-23-0","dir":"Changelog","previous_headings":"","what":"DRomics 2.3-0","title":"DRomics 2.3-0","text":"CRAN release: 2021-10-04 NEW FEATURES argument facetby2 added bmdplotwithgradient() curvesplot() able split plots rows columns using facet_grid(). Add function trendplot() plot repartition dose-response trend per group items. Add function sensitivityplot() plot various summaries BMD values per group items. Add function bmdplot() takes extendedres first argument trendplot(), bmdplotwithgradient(), sensitivityplot() curvesplot(). Removing drcfit() argument sigmoid.model confusing users useful common use package.","code":""},{"path":"/news/index.html","id":"dromics-22-0","dir":"Changelog","previous_headings":"","what":"DRomics 2.2-0","title":"DRomics 2.2-0","text":"CRAN release: 2021-02-09 NEW FEATURES second-order Akaike criterion (AICc) recommended prevent overfitting small number data poinst dose-response crives implemented defined default option argument information.criterion drcfit(). example file package (?DRomics) replaced vignette help use package Shiny application. Improvement computation low BMD values designs high ratio maximal minimal (non null) tested doses, add two arguments bmdcalc, minBMD ratio2switchinlog. Add two columns output bmdcalc (BMR.zSD BMR.xfold) Add function plotfit2pdf() plot fits (residual plots) pdf file, using raw scale log scale dose removing option saveplot2pdf drcfit(). Replacement class ‘metabolomicdata’ class ‘continuousomicdata’ add function continuousomicdata() called metabolomicdata() used types continuous omics data proteomics data. default color changed bmdplotwithgradient() (green replaced blue color blind people) Removing three four datasets Zou et al. 2017 make package lighter Add test residuals heteroscedasticity output drcfit: residualtests BUG FIXES handling RNAseqdata() cases vst() may give stop message.","code":""},{"path":"/news/index.html","id":"dromics-21-3","dir":"Changelog","previous_headings":"","what":"DRomics 2.1-3","title":"DRomics 2.1-3","text":"CRAN release: 2020-09-23 NEW FEATURES Add function bmdplotwithgradient() Add function ecdfquantileplot() Add argument named information.criterion drcfit() choose use AIC BIC best fit model selection process. Add possibility enter data R object class data.frame Add published datasets (Zhou et al. 2017, Larras et al. 2020) corresponding help pages Add function continuousanchoringdata modification itemselect() enable selection significant responses continuous anchoring data. Add argument dose_log_transfo plot.drcfit enable use log tranformation x-axis. Add element list drcfit() output : unfitres giving information selected items modelling step successful Add function plot raw data target items optionally fitted curves items selected step 2 step 3 successful (new function targetplot()). Add argument transfo.blind RNAseqdata() Add argument free.y.scales curvesplot() enable free y scales facets dose_log_transfo use x log scale plot calculation signal. Add examples DRomics.Rd help multi-omics approach Add argument round.counts enable rounding read counts come Kallisto Salmon. BUG FIXES make direct use varianceStabilizingTransformation() automatic small RNAseq data sets (low number items: < 1000) fix bug RNAseqdata() occured using vst() small datasets.","code":""},{"path":"/news/index.html","id":"dromics-20-1","dir":"Changelog","previous_headings":"","what":"DRomics 2.0-1","title":"DRomics 2.0-1","text":"CRAN release: 2019-09-16 NEW FEATURES Add filter itemselect(), exclude selection items high proportion non detected values (assuming imputed common minimum value). Add argument point.type enables change point type , ecdfplotwithCI coding given factor. Add argument plot.type function plot.drcfit() enable residual plots.","code":""},{"path":"/news/index.html","id":"dromics-20-0","dir":"Changelog","previous_headings":"","what":"DRomics 2.0-0","title":"DRomics 2.0-0","text":"NEW FEATURES Replacement function omicdata() function microarraydata() add two new data importation functions, RNAseqdata() metabolomicdata().","code":""},{"path":"/news/index.html","id":"dromics-11-3","dir":"Changelog","previous_headings":"","what":"DRomics 1.1-3","title":"DRomics 1.1-3","text":"NEW FEATURES Replacement argument named bytypology plot.bmdcalc argument named can taka three values, “none”, “trend”, “model” “typology”. Add function plot fitted curves (new function curvesplot()).","code":""},{"path":"/news/index.html","id":"dromics-11-2","dir":"Changelog","previous_headings":"","what":"DRomics 1.1-2","title":"DRomics 1.1-2","text":"NEW FEATURES Add function plot distribution variable ecdf plot, confidence intervals variable (new function ecdfplotwithCI)","code":""},{"path":"/news/index.html","id":"dromics-11-1","dir":"Changelog","previous_headings":"","what":"DRomics 1.1-1","title":"DRomics 1.1-1","text":"NEW FEATURES Add bootstrap computation confidence intervals benchmark doses (new function bmdboot) Add function plot distribution variable ecdf plot, confidence intervals variable (new function ecdfplotwithCI)","code":""},{"path":"/news/index.html","id":"dromics-10-1","dir":"Changelog","previous_headings":"","what":"DRomics 1.0-1","title":"DRomics 1.0-1","text":"NEW FEATURES Add column yextrem results drcfit (y value extremum biphasic curves)","code":""},{"path":"/news/index.html","id":"dromics-10-0","dir":"Changelog","previous_headings":"","what":"DRomics 1.0-0","title":"DRomics 1.0-0","text":"Initial release.","code":""}]