# HG changeset patch
# User theo.collard
# Date 1507037135 14400
# Node ID 4290f0f3d908d9f8458ddce17a059a544e25a386
# Parent  a225c3f406752b36295eb90dcffdf570478fac80
Uploaded

diff -r a225c3f40675 -r 4290f0f3d908 ballgown.R
--- a/ballgown.R	Tue Oct 03 09:25:22 2017 -0400
+++ b/ballgown.R	Tue Oct 03 09:25:35 2017 -0400
@@ -1,73 +1,79 @@
-#!/usr/bin/Rscript
-
-# Enabling commands line arguments. Using optparse which allows to use options.
-# ----------------------------------------------------------------------------------------
-
-suppressMessages(library(optparse, warn.conflicts = FALSE))
-opt_list=list(
-make_option(c("-d", "--directory"), type="character", default=NULL, help="directory containing the samples", metavar="character"),
-make_option(c("-p", "--phendat"), type="character", default=NULL, help="phenotype data(must be a .csv file)", metavar="character"),
-make_option(c("-t","--outputtranscript"), type="character", default="output_transcript.csv", help="output_transcript.csv: contains the transcripts of the expirements", metavar="character"),
-make_option(c("-g","--outputgenes"), type="character", default="output_genes.csv", help="output_genes.csv: contains the genes of the expirements", metavar="character"),
-make_option(c("-e","--texpression"), type="double", default="0.5", help="transcripts expression filter", metavar="character"),
-make_option(c("--bgout"), type="character", default="", help="save the ballgown object created in the process", metavar="character")
-)
-opt_parser=OptionParser(option_list=opt_list)
-opt=parse_args(opt_parser)
-
-# Loading required libraries. suppressMessages() remove all noisy attachement messages
-# ----------------------------------------------------------------------------------------
-
-suppressMessages(library(ballgown, warn.conflicts = FALSE))
-suppressMessages(library(genefilter, warn.conflicts = FALSE))
-suppressMessages(library(dplyr, warn.conflicts = FALSE))
-
-# Setup for the tool with some bases variables.
-# ----------------------------------------------------------------------------------------
-
-
-filtstr = opt$texpression
-pdat = 2
-phendata = read.csv(opt$phendat)
-setwd(opt$dir)
-
-# Checking if the pdata file has the right samples names.
-# ----------------------------------------------------------------------------------------
-
-if (all(phendata$ids == list.files(".")) != TRUE)
-{
-  stop("Your phenotype data table does not match the samples names. ")
-}
-
-# Creation of the ballgown object based on data
-# ----------------------------------------------------------------------------------------
-bgi = ballgown(dataDir= "." , samplePattern="", pData = phendata, verbose = FALSE)
-
-# Filter the genes with an expression superior to the input filter
-# ----------------------------------------------------------------------------------------
-bgi_filt= subset(bgi, paste("rowVars(texpr(bgi)) >",filtstr), genomesubset = TRUE)
-
-# Creating the variables containing the transcripts and the genes and sorting them through the arrange() command.
-# Checking if there's one or more adjust variables in the phenotype data file
-# ----------------------------------------------------------------------------------------
-
-if (ncol(pData(bgi))<=3) {
-  results_transcripts=stattest(bgi_filt,feature = "transcript", covariate = colnames(pData(bgi))[pdat], adjustvars = colnames(pData(bgi)[pdat+1]), getFC = TRUE, meas = "FPKM")
-  results_genes=stattest(bgi_filt,feature = "gene", covariate = colnames(pData(bgi))[pdat], adjustvars = colnames(pData(bgi)[pdat+1]), getFC = TRUE, meas = "FPKM")
-} else {
-  results_transcripts=stattest(bgi_filt,feature = "transcript", covariate = colnames(pData(bgi))[pdat], adjustvars = c(colnames(pData(bgi)[pdat+1:ncol(pData(bgi))])), getFC = TRUE, meas = "FPKM")
-  results_genes=stattest(bgi_filt,feature = "gene", covariate = colnames(pData(bgi))[pdat], adjustvars = c(colnames(pData(bgi)[pdat+1:ncol(pData(bgi))])), getFC = TRUE, meas = "FPKM")
-}
-
-results_transcripts = data.frame(geneNames=ballgown::geneNames(bgi_filt), geneIDs=ballgown::geneIDs(bgi_filt), results_transcripts)
-results_transcripts = arrange(results_transcripts,pval)
-results_genes = arrange(results_genes,pval)
-
-# Main output of the wrapper, two .csv files containing the genes and transcripts with their qvalue and pvalue
-#This part also output the data of the ballgown object created in the process and save it in a R data file
-# ----------------------------------------------------------------------------------------
-write.csv(results_transcripts, opt$outputtranscript, row.names = FALSE)
-write.csv(results_genes, opt$outputgenes, row.names = FALSE)
-if (opt$bgout != ""){
-  save(bgi, file=opt$bgout)
-}
+#!/usr/bin/env Rscript
+
+suppressMessages(library(optparse, warn.conflicts = FALSE))
+opt_list=list(
+make_option(c("-d", "--directory"), type="character", default=NULL, help="directory containing the samples", metavar="character"),
+make_option(c("-p", "--phendat"), type="character", default=NULL, help="phenotype data(must be a .csv file)", metavar="character"),
+make_option(c("-t","--outputtranscript"), type="character", default="output_transcript.csv", help="output_transcript.csv: contains the transcripts of the experiments", metavar="character"),
+make_option(c("-g","--outputgenes"), type="character", default="output_genes.csv", help="output_genes.csv: contains the genes of the experiments", metavar="character"),
+make_option(c("-e","--texpression"), type="double", default="0.5", help="transcripts expression filter", metavar="character"),
+make_option(c("--bgout"), type="character", default="", help="save the ballgown object created in the process", metavar="character"),
+make_option(c("-f","--format"), type="character", default="tsv", help="Create csv or tsv files as output", metavar="character"),
+make_option(c("-T","--tsvoutputtranscript"), type="character", default="output_transcript.tsv", help="output_transcript.tsv: contains the transcripts of the experiments", metavar="character"),
+make_option(c("-G","--tsvoutputgenes"), type="character", default="output_genes.tsv", help="output_genes.tsv: contains the genes of the experiments", metavar="character")
+)
+opt_parser=OptionParser(option_list=opt_list)
+opt=parse_args(opt_parser)
+
+# Loading required libraries. suppressMessages() remove all noisy attachement messages
+# ----------------------------------------------------------------------------------------
+
+suppressMessages(library(ballgown, warn.conflicts = FALSE))
+suppressMessages(library(genefilter, warn.conflicts = FALSE))
+suppressMessages(library(dplyr, warn.conflicts = FALSE))
+
+# Setup for the tool with some bases variables.
+# ----------------------------------------------------------------------------------------
+
+
+filtstr = opt$texpression
+pdat = 2
+phendata = read.csv(opt$phendat)
+
+
+# Checking if the pdata file has the right samples names.
+# ----------------------------------------------------------------------------------------
+
+if (all(phendata$ids == list.files(opt$directory)) != TRUE)
+{
+  stop("Your phenotype data table does not match the samples names. ")
+}
+
+# Creation of the ballgown object based on data
+# ----------------------------------------------------------------------------------------
+bgi = ballgown(dataDir= opt$directory , samplePattern="", pData = phendata, verbose = FALSE)
+
+# Filter the genes with an expression superior to the input filter
+# ----------------------------------------------------------------------------------------
+bgi_filt= subset(bgi, paste("rowVars(texpr(bgi)) >",filtstr), genomesubset = TRUE)
+
+# Creating the variables containing the transcripts and the genes and sorting them through the arrange() command.
+# Checking if there's one or more adjust variables in the phenotype data file
+# ----------------------------------------------------------------------------------------
+
+if (ncol(pData(bgi))<=3) {
+  results_transcripts=stattest(bgi_filt, feature = "transcript", covariate = colnames(pData(bgi))[pdat], adjustvars = colnames(pData(bgi)[pdat+1]), getFC = TRUE, meas = "FPKM")
+  results_genes=stattest(bgi_filt, feature = "gene", covariate = colnames(pData(bgi))[pdat], adjustvars = colnames(pData(bgi)[pdat+1]), getFC = TRUE, meas = "FPKM")
+} else {
+  results_transcripts=stattest(bgi_filt, feature = "transcript", covariate = colnames(pData(bgi))[pdat], adjustvars = c(colnames(pData(bgi)[pdat+1:ncol(pData(bgi))])), getFC = TRUE, meas = "FPKM")
+  results_genes=stattest(bgi_filt, feature = "gene", covariate = colnames(pData(bgi))[pdat], adjustvars = c(colnames(pData(bgi)[pdat+1:ncol(pData(bgi))])), getFC = TRUE, meas = "FPKM")
+}
+
+results_transcripts = data.frame(geneNames=ballgown::geneNames(bgi_filt), geneIDs=ballgown::geneIDs(bgi_filt), results_transcripts)
+results_transcripts = arrange(results_transcripts, pval)
+results_genes = arrange(results_genes, pval)
+
+# Main output of the wrapper, two .csv files containing the genes and transcripts with their qvalue and pvalue
+#This part also output the data of the ballgown object created in the process and save it in a R data file
+# ----------------------------------------------------------------------------------------
+if (opt$format == "tsv"){
+  write.table(results_transcripts, file=opt$tsvoutputtranscript, quote=FALSE, sep='\t', col.names = NA)
+  write.table(results_genes, file=opt$tsvoutputgenes, quote=FALSE, sep='\t', col.names = NA)
+} else {
+  write.csv(results_transcripts, opt$outputtranscript, row.names = FALSE)
+  write.csv(results_genes, opt$outputgenes, row.names = FALSE)
+}
+
+if (opt$bgout != ""){
+  save(bgi, file=opt$bgout)
+}