view edgeR_DGE.xml @ 7:61e42740b13a draft

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<?xml version="1.0" encoding="UTF-8"?>
<tool id="edger_dge" name="edgeR Differential GeneExpression Analysis">
	<description>RNA-Seq expression analysis using edgeR (R package)</description>
	
	<command>
		<!--
			The following script is written in the "Cheetah" language:
			http://www.cheetahtemplate.org/docs/users_guide_html_multipage/contents.html
		-->
		
		R CMD BATCH --vanilla --slave '--args
			$design_matrix
			$contrast
			
			$output_count_edgeR 
			$output_cpm
			output_FPXM
			$output_raw_counts
			
			$qc
			$output_MDSplot
			$output_BCVplot
			$output_MAplot
			smearPlot '
			$R_script $output_R
	</command>
	
	<inputs>
		<param name="design_matrix" type="data" format="tabular" help="Design matrix" />
		
		<param name="contrast" type="text" label="Contrast (biological question)" help="e.g. 'tumor-normal' or '(G1+G2)/2-G3' using the factors chosen in the design matrix. Read the 'makeContrasts' manual from Limma package for more info." />
		
		<param name="qc" type="select" label="Quality control reports">
			<option value="true">Yes</option>
			<option value="false" selected="true">No</option>
		</param>
		
		<param name="debug" type="select" label="R Debug output">
			<option value="true" selected="true"> Yes</option>
			<option value="false">No</option>
		</param>
	</inputs>
	
	<configfiles>
		<configfile name="R_script">
library(edgeR)
 
## Fetch commandline arguments
args &lt;- commandArgs(trailingOnly = TRUE)
designmatrix        = args[1]
contrast            = args[2]

output_1            = args[3]
output_2            = args[4]
output_3            = args[5]		##FPKM file - to be implemented
output_4            = args[6]

QC                  = nchar(args[7]) > 0

output_5            = args[8]
output_6            = args[9]
output_7            = args[10]

output_8            = args[11]


library(edgeR)
raw_data &lt;- read.delim(designmatrix,header=T,stringsAsFactors=T)

## Obtain read-counts
read_counts 		= read.delim(as.character(raw_data[1,1]),header=F,stringsAsFactors=F,row.names=1)
for(i in 2:length(raw_data[,1])) {
  print("parsing counts from:")
  print(raw_data[i,1])
  read_counts = cbind(read_counts,read.delim(as.character(raw_data[i,1]),header=F,stringsAsFactors=F,row.names=1))
  print(i)
}



## Filter for HTSeq predifined counts:
exclude_HTSeq = c("no_feature","ambiguous","too_low_aQual","not_aligned","alignment_not_unique")
exclude_DEXSeq = c("_ambiguous","_empty","_lowaqual","_notaligned")

exclude = match(c(exclude_HTSeq, exclude_DEXSeq),rownames(read_counts))
exclude = exclude[is.na(exclude)==0]
if(length(exclude) != 0)  {
  read_counts = read_counts[-exclude,]
}









colnames(read_counts) = raw_data[,2]
dge 					= DGEList(counts=read_counts,genes=rownames(read_counts))

design_tmp &lt;- raw_data[3:length(raw_data)]
rownames(design_tmp)     &lt;- colnames(dge)
formula = paste(c("~0",colnames(design_tmp)),collapse = " + ")
design &lt;- model.matrix(as.formula(formula),design_tmp)

prefixes = colnames(design_tmp)[attr(design,"assign")]
avoid = nchar(prefixes) == nchar(colnames(design))
replacements = substr(colnames(design),nchar(prefixes)+1,nchar(colnames(design)))
replacements[avoid] = colnames(design)[avoid]
colnames(design) = replacements



print("Calculating normalization factors...")
dge		= calcNormFactors(dge)
print("Estimating common dispersion...")
dge 	= estimateGLMCommonDisp(dge,design)
print("Estimating trended dispersion...")
dge 	= estimateGLMTrendedDisp(dge,design)
print("Estimating tagwise dispersion...")
dge 	= estimateGLMTagwiseDisp(dge,design)




if (QC == TRUE) {
  print("Creating QC plots...")
  #### MDS Plot
  pdf(output_5)
  plotMDS(dge, main="edgeR MDS Plot")
  dev.off()
  #### Biological coefficient of variation plot
  pdf(output_6)
  plotBCV(dge, cex=0.4, main="edgeR: Biological coefficient of variation (BCV) vs abundance")
  dev.off()
}



print("Fitting GLM...")
fit 	= glmFit(dge,design)

print(paste("Performing likelihood ratio test: ",contrast,sep=""))
cont &lt;- c(contrast)
cont &lt;- makeContrasts(contrasts=cont, levels=design)

lrt &lt;- glmLRT(fit, contrast=cont[,1])
print(paste("Exporting to file: ",output_1,sep=""))
write.table(file=output_1,topTags(lrt,n=nrow(read_counts))\$table,sep="\t",row.names=T)
write.table(file=output_2,cpm(dge,normalized.lib.sizes=TRUE),sep="\t")
## todo EXPORT FPKM
write.table(file=output_4,dge\$counts,sep="\t")



if (QC == TRUE) {
  print("Creating MA plots...")
  
  
  etable &lt;- topTags(lrt, n=nrow(dge))\$table
  etable &lt;- etable[order(etable\$FDR), ]
  pdf(output_7)
  with(etable, plot(logCPM, logFC, pch=20, main="edgeR: Fold change vs abundance"))
  with(subset(etable, FDR&lt;0.05), points(logCPM, logFC, pch=20, col="red"))
  abline(h=c(-1,1), col="blue")
  dev.off()
}
print("Done!")
		</configfile>
	</configfiles>
	
	<outputs>
		<data format="tabular" name="output_count_edgeR" label="edgeR DGE on ${design_matrix.hid}: ${design_matrix.name} - table" />
		<data format="tabular" name="output_cpm" label="edgeR DGE on ${design_matrix.hid}: ${design_matrix.name} - CPM" />
		<data format="tabular" name="output_raw_counts" label="edgeR DGE on ${design_matrix.hid}: ${design_matrix.name} - raw counts" />
		
		<data format="txt" name="output_R" label="edgeR DGE on ${design_matrix.hid}: ${design_matrix.name} - R output" >
			<filter>(debug == "true")</filter>
		</data>
		
		<data format="pdf" name="output_MDSplot" label="edgeR DGE on ${design_matrix.hid}: ${design_matrix.name} - MDS-plot">
			<filter>(qc == "true")</filter>
		</data>
		
		<data format="pdf" name="output_BCVplot" label="edgeR DGE on ${design_matrix.hid}: ${design_matrix.name} - BCV-plot">
			<filter>(qc == "true")</filter>
		</data>
		
		<data format="pdf" name="output_MAplot" label="edgeR DGE on ${design_matrix.hid}: ${design_matrix.name} - MA-plot">
			<filter>(qc == "true")</filter>
		</data>
	</outputs>
	
	<help>
		input: Design matrix using "create Design matrix" tool
		input: contrast
	</help>
</tool>