A Linear Discriminant Analysis (LEfSe) wrapper.
Because Koeken needs scripts found within the QIIME package, it is easiest to use when you are in a MacQIIME session. If you have MacQIIME installed, you must first initialize it before installing Koeken. If you do not have macqiime installed, you can still run koeken as long as you have the scripts available in your path. See http://qiime.org/install/install.html for more information.
macqiimeRun the command below while in a MacQIIME session. The command will install new R packages. If you do not have R installed, please see https://cran.r-project.org/, and install the package first before running.
pip install https://github.com/twbattaglia/koeken/zipball/master
koeken.py --helpIn this example we have a typical workflow using koeken. We have an otu-table in .biom format, a mapping text file, and an output folder to place the results. The --class parameter corresponds to the column name which describes the different groups in your mapping file. The --split parameter corresponds to the column name which describes the different timepoints in your data. --clade will produce the cladograms popular with LEfSe.
koeken.py \
--input otu_table.biom \
--output output_folder/ \
--map mapping_file.txt \
--class Treatment \
--split Day \
--clade Koeken is a wrapper around QIIME and LEfSe to create a more convienient and easier workflow from a QIIME OTU table to biomarker microbial species. It was specifically developed for the experiments which have more than one time point and many groups. Koeken allows the user to specify the variable which correspond to the 'Time' of a users dataset to run LEfSe on each 'Time' factor. Additionally it has the capabilities to specify particular groups to compare to one another, without any additionally subsetting.
- Run LEfSe over many time points
- Specify groups (2+) to compare to eachother
- Automatically generate cladograms for each comparison.
- Clean GreenGenes naming conventions (e.g k__,g__)
- Find significant biomarker PICRUST Level 3 features.
- Combine the data from many timepoints into a 'heatmap-like' table. (See 'pretty_lefse.py -h' for more information)
- Modify thresholds for testing parameters (e.g effect size, p-value, strictness)
- Collapse OTU data on different levels [Default = L6(Genus)]
Koeken generates many intermediate files as it iterates over each time point. For each timepoint, koeken runs a QIIME command (summarize_taxa.py), LEfSe formatting and LEfSe significance tests, but each of the steps are separated into their respective folders to minimize confusion. A breakdown of the folders are shown below.
output_folder/
|--summarize_taxa/ (Output from running QIIME commands)
|--lefse_output/ (Root folder for storing LEfSe outputs)
|----format_lefse/ (Output from running LEfSe formatting command)
|----run_lefse/ (Output from running main LEfSe command)
If you want to use the galaxy version of LEfSe to generate figures, you can use the provided output from koeken. During file upload you must select lefse_res for the files in the 'run_lefse/' folder and must select lefse_for for the files in the 'format_lefse/'.
Big thank you to the QIIME group:
"QIIME allows analysis of high-throughput community sequencing data"
J Gregory Caporaso, Justin Kuczynski, Jesse Stombaugh, Kyle Bittinger, Frederic D Bushman, Elizabeth K Costello, Noah Fierer, Antonio Gonzalez Pena, Julia K Goodrich, Jeffrey I Gordon, Gavin A Huttley, Scott T Kelley, Dan Knights, Jeremy E Koenig, Ruth E Ley, Catherine A Lozupone, Daniel McDonald, Brian D Muegge, Meg Pirrung, Jens Reeder, Joel R Sevinsky, Peter J Turnbaugh, William A Walters, Jeremy Widmann, Tanya Yatsunenko, Jesse Zaneveld and Rob Knight; Nature Methods, 2010; doi:10.1038/nmeth.f.303
Big thank you to the LEfSe group:
"Metagenomic biomarker discovery and explanation"
Nicola Segata, Jacques Izard, Levi Waldron, Dirk Gevers, Larisa Miropolsky, Wendy S Garrett, and Curtis Huttenhower
Genome Biology, 12:R60, 2011
usage: koeken.py [-h] [-v] [-d] -i INPUT_BIOM -o OUTPUTDIR -m MAP_FP
[-l {2,3,4,5,6,7}] -cl CLASSID [-sc SUBCLASSID]
[-su SUBJECTID] [-p P_CUTOFF] [-e LDA_CUTOFF] [-str {0,1}]
[-c COMPARE [COMPARE ...]] -sp SPLIT [-pc]
[-it {png,pdf,svg}] [-dp DPI] [-pi]
Performs Linear Discriminant Analysis (LEfSe) on A Longitudinal Dataset.
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
-d, --debug Enable debugging
-i INPUT_BIOM, --input INPUT_BIOM
Location of the OTU Table for main analysis. (Must be
.biom format)
-o OUTPUTDIR, --output OUTPUTDIR
Location of the folder to place all resulting files.
If folder does not exist, the program will create it.
-m MAP_FP, --map MAP_FP
Location of the mapping file associated with OTU
Table.
-l {2,3,4,5,6,7}, --level {2,3,4,5,6,7}
Level for which to use for summarize_taxa.py. [default
= 6]
-cl CLASSID, --class CLASSID
Location of the OTU Table for main analysis. (Must be
.biom format)
-sc SUBCLASSID, --subclass SUBCLASSID
Directory to place all the files.
-su SUBJECTID, --subject SUBJECTID
Only change if your Sample-ID column names differs.
[default] = #SampleID.
-p P_CUTOFF, --pval P_CUTOFF
Change alpha value for the Anova test (default 0.05)
-e LDA_CUTOFF, --effect LDA_CUTOFF
Change the cutoff for logarithmic LDA score (default
2.0).
-str {0,1}, --strict {0,1}
Change the strictness of the comparisons. Can be
changed to less strict (1). [default = 0](more-
strict).
-c COMPARE [COMPARE ...], --compare COMPARE [COMPARE ...]
Which groups should be kept to be compared against one
another. [default = all factors]
-sp SPLIT, --split SPLIT
The name of the timepoint variable in you mapping
file. This variable will be used to split the table
for each value in this variable.
-pc, --clade Plot Lefse Cladogram for each output time point.
Outputs are placed in a new folder created in the
lefse results location.
-it {png,pdf,svg}, --image {png,pdf,svg}
Set the file type for the image create when using
cladogram setting
-dp DPI, --dpi DPI Set DPI resolution for cladogram
-pi, --picrust Run analysis with PICRUSt biom file. Must use the
cateogirze by function level 3. Next updates will
reflect the difference levels.- install LEfSe files separately and much better
- add results table for a combined all time points analysis
- add more package dependency verification
- export2graphlan.py support
- add more run_lefse.py parameters
- add more plot_cladogram.py parameters