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template_script_edgeR_CL.r
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template_script_edgeR_CL.r
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################################################################################
### R script to compare several conditions with the SARTools and edgeR packages
### Hugo Varet
### March 23rd, 2022
### designed to be executed with SARTools 1.8.1
### run "Rscript template_script_edgeR_CL.r --help" to get some help
################################################################################
rm(list=ls()) # remove all the objects from the R session
library(optparse) # to run the script in command lines
# options list with associated default value.
option_list <- list(
make_option(c("-P", "--projectName"),
default=basename(getwd()),
dest="projectName",
help="name of the project used for the report [default: name of the current directory]."),
make_option(c("-A", "--author"),
default=Sys.info()[7],
dest="author",
help="name of the report author [default: %default]."),
make_option(c("-t", "--targetFile"),
default="target.txt",
dest="targetFile",
help="path to the design/target file [default: %default]."),
make_option(c("-r", "--rawDir"),
default="raw",
dest="rawDir",
help="path to the directory containing the HTSeq files [default: %default]."),
make_option(c("-F", "--featuresToRemove"),
default="alignment_not_unique,ambiguous,no_feature,not_aligned,too_low_aQual",
dest="FTR",
help="names of the features to be removed, more than once can be specified [default: %default]"),
make_option(c("-v", "--varInt"),
default="group",
dest="varInt",
help="factor of interest [default: %default]"),
make_option(c("-c", "--condRef"),
default="WT",
dest="condRef",
help="reference biological condition [default: %default]"),
make_option(c("-b", "--batch"),
default=NULL,
dest="batch",
help="blocking factor [default: %default] or \"batch\" for example"),
make_option(c("-a", "--alpha"),
default=0.05,
dest="alpha",
help="threshold of statistical significance [default: %default]"),
make_option(c("-p", "--pAdjustMethod"),
default="BH",
dest="pAdjustMethod",
help="p-value adjustment method: \"BH\" or \"BY\" [default: %default]"),
make_option(c("-m", "--cpmCutoff"),
default=1,
dest="cpmCutoff",
help="counts-per-million cut-off to filter low counts"),
make_option(c("-g", "--gene.selection"),
default="pairwise",
dest="gene.selection",
help="selection of the features in MDSPlot [default: %default]"),
make_option(c("-n", "--normalizationMethod"),
default="TMM",
dest="normalizationMethod",
help="normalization method in calcNormFactors: \"TMM\", \"RLE\" or \"upperquartile\" [default: %default]"),
make_option(c("-C", "--colors"),
default="#f3c300,#875692,#f38400,#a1caf1,#be0032,#c2b280,#848482,#008856,#e68fac,#0067a5",
dest="cols",
help="colors of each biological condition on the plots\n\t\t\"col1,col2,col3,col4\"\n\t\t[default: %default]"),
make_option(c("-f", "--forceCairoGraph"),
action="store_true",
default=FALSE,
dest="forceCairoGraph",
help="activate cairo type")
)
# now parse the command line to check which option is given and get associated values
parser <- OptionParser(usage="usage: %prog [options]",
option_list=option_list,
description="Compare two or more biological conditions in a RNA-Seq framework with edgeR.",
epilogue="For comments, bug reports etc... please contact Hugo Varet <hugo.varet@pasteur.fr>")
opt <- parse_args(parser, args=commandArgs(trailingOnly=TRUE), positional_arguments=0)$options
# get options and arguments
workDir <- getwd()
projectName <- opt$projectName # name of the project
author <- opt$author # author of the statistical analysis/report
targetFile <- opt$targetFile # path to the design/target file
rawDir <- opt$rawDir # path to the directory containing raw counts files
featuresToRemove <- unlist(strsplit(opt$FTR, ",")) # names of the features to be removed (specific HTSeq-count information and rRNA for example)
varInt <- opt$varInt # factor of interest
condRef <- opt$condRef # reference biological condition
batch <- opt$batch # blocking factor: NULL (default) or "batch" for example
alpha <- as.numeric(opt$alpha) # threshold of statistical significance
pAdjustMethod <- opt$pAdjustMethod # p-value adjustment method: "BH" (default) or "BY"
gene.selection <- opt$gene.selection # selection of the features in MDSPlot
normalizationMethod <- opt$normalizationMethod # normalization method in calcNormFactors
cpmCutoff <- opt$cpmCutoff # counts-per-million cut-off to filter low counts
colors <- unlist(strsplit(opt$cols, ",")) # vector of colors of each biologicial condition on the plots
forceCairoGraph <- opt$forceCairoGraph # force cairo as plotting device if enabled
# print(paste("workDir", workDir))
# print(paste("projectName", projectName))
# print(paste("author", author))
# print(paste("targetFile", targetFile))
# print(paste("rawDir", rawDir))
# print(paste("varInt", varInt))
# print(paste("condRef", condRef))
# print(paste("batch", batch))
# print(paste("alpha", alpha))
# print(paste("pAdjustMethod", pAdjustMethod))
# print(paste("featuresToRemove", featuresToRemove))
# print(paste("colors", colors))
# print(paste("gene.selection", gene.selection))
# print(paste("normalizationMethod", normalizationMethod))
# print(paste("cpmCutoff", cpmCutoff))
################################################################################
### running script ###
################################################################################
# setwd(workDir)
library(SARTools)
if (forceCairoGraph) options(bitmapType="cairo")
# checking parameters
problem <- checkParameters.edgeR(projectName=projectName,author=author,targetFile=targetFile,
rawDir=rawDir,featuresToRemove=featuresToRemove,varInt=varInt,
condRef=condRef,batch=batch,alpha=alpha,pAdjustMethod=pAdjustMethod,
cpmCutoff=cpmCutoff,gene.selection=gene.selection,
normalizationMethod=normalizationMethod,colors=colors)
if (problem) quit(save="yes")
# loading target file
target <- loadTargetFile(targetFile=targetFile, varInt=varInt, condRef=condRef, batch=batch)
# loading counts
counts <- loadCountData(target=target, rawDir=rawDir, featuresToRemove=featuresToRemove)
# description plots
majSequences <- descriptionPlots(counts=counts, group=target[,varInt], col=colors)
# edgeR analysis
out.edgeR <- run.edgeR(counts=counts, target=target, varInt=varInt, condRef=condRef,
batch=batch, cpmCutoff=cpmCutoff, normalizationMethod=normalizationMethod,
pAdjustMethod=pAdjustMethod)
# MDS + clustering
exploreCounts(object=out.edgeR$dge, group=target[,varInt], gene.selection=gene.selection, col=colors)
# summary of the analysis (boxplots, dispersions, export table, nDiffTotal, histograms, MA plot)
summaryResults <- summarizeResults.edgeR(out.edgeR, group=target[,varInt], counts=counts, alpha=alpha, col=colors)
# save image of the R session
save.image(file=paste0(projectName, ".RData"))
# generating HTML report
writeReport.edgeR(target=target, counts=counts, out.edgeR=out.edgeR, summaryResults=summaryResults,
majSequences=majSequences, workDir=workDir, projectName=projectName, author=author,
targetFile=targetFile, rawDir=rawDir, featuresToRemove=featuresToRemove, varInt=varInt,
condRef=condRef, batch=batch, alpha=alpha, pAdjustMethod=pAdjustMethod, cpmCutoff=cpmCutoff,
colors=colors, gene.selection=gene.selection, normalizationMethod=normalizationMethod)