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DESCRIPTION
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DESCRIPTION
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Package: BASiCS
Type: Package
Title: Bayesian Analysis of Single-Cell Sequencing data
Version: 2.15.5
Date: 2024-03-25
Authors@R: c(person("Catalina", "Vallejos", role=c("aut", "cre"),
email="[email protected]",
comment=c(ORCID = "0000-0003-3638-1960")),
person("Nils", "Eling", role=c("aut")),
person("Alan", "O'Callaghan", role = c("aut")),
person("Sylvia", "Richardson", role = c("ctb")),
person("John", "Marioni", role=c("ctb")))
Description: Single-cell mRNA sequencing can uncover novel cell-to-cell
heterogeneity in gene expression levels in seemingly homogeneous populations
of cells. However, these experiments are prone to high levels of technical
noise, creating new challenges for identifying genes that show genuine
heterogeneous expression within the population of cells under study. BASiCS
(Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian
hierarchical model to perform statistical analyses of single-cell RNA
sequencing datasets in the context of supervised experiments (where the groups
of cells of interest are known a priori, e.g. experimental conditions or cell
types). BASiCS performs built-in data normalisation (global scaling) and
technical noise quantification (based on spike-in genes). BASiCS provides an
intuitive detection criterion for highly (or lowly) variable genes within a
single group of cells. Additionally, BASiCS can compare gene expression
patterns between two or more pre-specified groups of cells. Unlike traditional
differential expression tools, BASiCS quantifies changes in expression that lie
beyond comparisons of means, also allowing the study of changes in cell-to-cell
heterogeneity. The latter can be quantified via a biological over-dispersion
parameter that measures the excess of variability that is observed with respect
to Poisson sampling noise, after normalisation and technical noise removal.
Due to the strong mean/over-dispersion confounding that is typically observed
for scRNA-seq datasets, BASiCS also tests for changes in residual
over-dispersion, defined by residual values with respect to a global
mean/over-dispersion trend.
License: GPL-3
Depends:
R (>= 4.2),
SingleCellExperiment
Imports:
Biobase,
BiocGenerics,
coda,
cowplot,
ggExtra,
ggplot2,
graphics,
grDevices,
MASS,
methods,
Rcpp (>= 0.11.3),
S4Vectors,
scran,
scuttle,
stats,
stats4,
SummarizedExperiment,
viridis,
utils,
Matrix (>= 1.5.0),
matrixStats,
assertthat,
reshape2,
BiocParallel,
posterior,
hexbin
Suggests:
BiocStyle,
knitr,
rmarkdown,
testthat,
scRNAseq,
magick
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software,
GeneExpression, Transcriptomics, SingleCell,
DifferentialExpression, Bayesian, CellBiology, ImmunoOncology
SystemRequirements: C++11
NeedsCompilation: yes
URL: https://github.com/catavallejos/BASiCS
BugReports: https://github.com/catavallejos/BASiCS/issues
RoxygenNote: 7.3.1
Encoding: UTF-8
LazyData: false
Config/testthat/edition: 3
Collate:
'AllClasses.R'
'AllGenerics.R'
'BASiCS_CalculateERCC.R'
'BASiCS_CorrectOffset.R'
'BASiCS_DenoisedCounts.R'
'BASiCS_DenoisedRates.R'
'BASiCS_DetectHVG_LVG.R'
'BASiCS_DiagHist.R'
'BASiCS_DiagPlot.R'
'BASiCS_DivideAndConquer.R'
'BASiCS_Draw.R'
'BASiCS_EffectiveSize.R'
'BASiCS_Filter.R'
'BASiCS_LoadChain.R'
'BASiCS_MCMC.R'
'BASiCS_MockSCE.R'
'BASiCS_Package.R'
'BASiCS_PlotDE.R'
'BASiCS_PlotOffset.R'
'BASiCS_PlotVG.R'
'BASiCS_PlotVarianceDecomp.R'
'BASiCS_PriorParam.R'
'BASiCS_ShowFit.R'
'BASiCS_Sim.R'
'BASiCS_TestDE.R'
'BASiCS_VarThresholdSearchHVG_LVG.R'
'BASiCS_VarianceDecomp.R'
'HiddenBASiCS_Sim.R'
'HiddenHeaderBASiCS_Sim.R'
'HiddenHeaderTest_DE.R'
'HiddenVarDecomp.R'
'utils_Misc.R'
'Methods.R'
'RcppExports.R'
'data.R'
'makeExampleBASiCS_Data.R'
'newBASiCS_Chain.R'
'newBASiCS_Data.R'
'utils_Data.R'
'utils_DivideAndConquer.R'
'utils_MCMC.R'
'utils_Store.R'
'utils_Tests.R'
'utils_VG.R'
'welcome.R'