comparison Sequenza_analysis.R @ 0:acdb95d9de7e draft

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0:acdb95d9de7e

#!/usr/bin/env Rscript


#parse command line
args <- commandArgs(trailingOnly = TRUE)
USAGE ="\n\nUSAGE: ./Sequenza_analysis.R -normal normal.mpileup -tumor tumor.mpileup -out resdir -gcContent gcContentFile -name sampleName -cellularity cellularityValue -ploidy ploidyValue\n\nkill execution"
if("-h"%in%args | "--h"%in%args |"-help"%in%args | "--help"%in%args){
	write(paste("\n---Help---",USAGE,sep=""),stdout())
	quit()
}
if(length(args)!=14){
	write(paste("\n---ERROR---\n\nThis script take exactly 7 arguments",USAGE,sep=""),stdout())	
	quit()
}

#input files
NORMALPILEUP =args[(which(args=="-normal")+1)]
TUMORPILEUP =args[(which(args=="-tumor")+1)]
SAMPLEID =args[(which(args=="-name")+1)]
GCCONTENTFILE =args[(which(args=="-gcContent")+1)]
CELLULARITY =args[(which(args=="-cellularity")+1)]
PLOIDY =args[(which(args=="-ploidy")+1)]
OUT_DIR =args[(which(args=="-out")+1)]

###################Python PATH####################
PYTHON_PATH = "python2.7"
##################################################

library("sequenza")

#Note on the helper program sequenza-utils.py.
UTILS =system.file("exec", "sequenza-utils.py", package="sequenza")

##########################################################
NORMALPILEUP_GZIP =NORMALPILEUP
TUMORPILEUP_GZIP =TUMORPILEUP
##########################################################

#Generate a seqz file
system(paste("time ",PYTHON_PATH," ",UTILS," pileup2seqz -gc ",GCCONTENTFILE," -n ",NORMALPILEUP_GZIP," -t ",TUMORPILEUP_GZIP," | gzip > ",OUT_DIR,"/out.seqz.gz",sep=""))
system(paste("time ",PYTHON_PATH," ",UTILS," seqz-binning -w 50 -s ",OUT_DIR,"/out.seqz.gz | gzip > ",OUT_DIR,"/out.small.seqz.gz",sep=""))

#### Exploring the seqz file and depth ratio normalization details
##Read the seqz file
data.file =paste(OUT_DIR,"/out.small.seqz.gz",sep="")
seqz.data <- read.seqz(data.file)

#Normalization of depth ratio
gc.stats <- gc.sample.stats(data.file)

gc.vect <- setNames(gc.stats$raw.mean, gc.stats$gc.values)

seqz.data$adjusted.ratio <- seqz.data$depth.ratio/gc.vect[as.character(seqz.data$GC.percent)]

png(paste(OUT_DIR,"/",SAMPLEID,"_gc_stat.png",sep=""),width=1000)
    par(mfrow = c(1,2), cex = 1, las = 1, bty = 'l')
    matplot(gc.stats$gc.values, gc.stats$raw, type = 'b', col = 1, pch = c(1, 19, 1), lty = c(2, 1, 2), xlab = 'GC content (%)', ylab = 'Uncorrected depth ratio')
    legend('topright', legend = colnames(gc.stats$raw), pch = c(1, 19, 1))
    hist2(seqz.data$depth.ratio, seqz.data$adjusted.ratio, breaks = prettyLog, key = vkey, panel.first = abline(0, 1, lty = 2), xlab = 'Uncorrected depth ratio', ylab = 'GC-adjusted depth ratio')
dev.off()



if (as.numeric(PLOIDY)==0){

	#### Analyzing sequencing data with sequenza

	##Extract the information from the seqz file.
	test <- sequenza.extract(data.file)

	#Plot chromosome view with mutations, BAF, depth ratio and segments
	chromosome.view(mut.tab = test$mutations[[1]], baf.windows = test$BAF[[1]], ratio.windows = test$ratio[[1]], min.N.ratio = 1, segments = test$segments[[1]], main = test$chromosomes[1])


	#Inference of cellularity and ploidy
	CP <- sequenza.fit(test)

	#Results of model fitting
	sequenza.results(sequenza.extract = test, cp.table = CP, sample.id =SAMPLEID, out.dir=OUT_DIR)

	#Confidence intervals, confidence region and point estimate
	cint <- get.ci(CP)
	cp.plot(CP)
	cp.plot.contours(CP, add = TRUE, likThresh = c(0.95))

	EstimatedValues <- read.table(file=paste(OUT_DIR,"/",SAMPLEID,"_alternative_solutions.txt",sep=""),header=TRUE)

	pdf(file=paste(OUT_DIR,"/",SAMPLEID,"_analyse.pdf",sep=""),height=10,width=25)

		for(i in 1:length(EstimatedValues[,1])){

			cellularity=EstimatedValues[i,1]
			ploidy=EstimatedValues[i,2]
			avg.depth.ratio=mean(test$gc$adj[,2])
			seg.tab=na.exclude(do.call(rbind,test$segments))

			cn.alleles<-baf.bayes(Bf=seg.tab$Bf,depth.ratio=seg.tab$depth.ratio,cellularity=cellularity,ploidy=ploidy,avg.depth.ratio=avg.depth.ratio)
			seg.tab=cbind(seg.tab,cn.alleles)

			write.table(seg.tab, file = paste(OUT_DIR,"/segments_",i,".txt",sep=""), col.names = TRUE, row.names = FALSE, sep = "\t")

			if (i==1){

				par(mfrow=c(2,1))

				genome.view(seg.cn=seg.tab, info.type="CNt")
				legend("bottomright",bty="n",c("Tumor Copy Number"),col=c("red"),inset=c(0.0,-0.1),pch=15,xpd=TRUE)
				mtext(text=paste("\n",SAMPLEID," Choice ",i," ploidy=",ploidy," cellularity=",cellularity,sep=""), side = 3, line = -2, outer = TRUE, cex=1.5, font=2)

				genome.view(seg.cn=seg.tab, info.type="AB")
				legend("bottomright",bty="n",c("Tumor Copy Number"),col=c("red"),inset=c(0.0,-0.1),pch=15,xpd=TRUE)

			}else{

				genome.view(seg.cn=seg.tab, info.type="CNt")
				mtext(text=paste("\n",SAMPLEID," Choice ",i," ploidy=",ploidy," cellularity=",cellularity,sep=""), side = 3, line = 1, outer = TRUE, cex=1.5, font=2)

				genome.view(seg.cn=seg.tab, info.type="AB")
				legend("bottomright",bty="n",c("Tumor Copy Number"),col=c("red"),inset=c(0.0,-0.08),pch=15,xpd=TRUE)
			}

			sequenza:::plotRawGenome(test, cellularity = cellularity,  ploidy = ploidy)
			mtext(text=paste("\n",SAMPLEID," Choice ",i," ploidy=",ploidy," cellularity=",cellularity,sep=""), side = 3, line = 1, outer = TRUE, cex=1.5, font=2)
		}

	dev.off()
	
	cellularity <- cint$max.cellularity
	ploidy <- cint$max.ploidy
	avg.depth.ratio <- mean(test$gc$adj[, 2])
	write(paste("cellularity : ",cellularity,"\nploidy : ",ploidy,"\naverage.depth.ratio : ",avg.depth.ratio,sep=""),file=paste(OUT_DIR,"/",SAMPLEID,"_cellularity_ploidy.txt",sep=""))

} else {

	cellularity.user <- as.numeric(CELLULARITY)
	ploidy.user <- as.numeric(PLOIDY)

	#### Analyzing sequencing data with sequenza

	##Extract the information from the seqz file.
	test <- sequenza.extract(data.file)
	
	#Plot chromosome view with mutations, BAF, depth ratio and segments
	chromosome.view(mut.tab = test$mutations[[1]], baf.windows = test$BAF[[1]], ratio.windows = test$ratio[[1]], min.N.ratio = 1, segments = test$segments[[1]], main = test$chromosomes[1])


	#Inference of cellularity and ploidy
	CP <- sequenza.fit(test)

	#Results of model fitting
	sequenza.results(sequenza.extract = test, cp.table = CP, sample.id=SAMPLEID, out.dir=OUT_DIR,ploidy=ploidy.user,cellularity=cellularity.user)


	#Confidence intervals, confidence region and point estimate
	cint <- get.ci(CP)
	cp.plot(CP)
	cp.plot.contours(CP, add = TRUE, likThresh = c(0.95))

	EstimatedValues <- read.table(file=paste(OUT_DIR,"/",SAMPLEID,"_alternative_solutions.txt",sep=""),header=TRUE)
	userValues=c(as.numeric(cellularity.user),as.numeric(ploidy.user),as.numeric(0.0))
	EstimatedValues=rbind(userValues,EstimatedValues)

	pdf(file=paste(OUT_DIR,"/",SAMPLEID,"_analyse.pdf",sep=""),height=10,width=25)

		for(i in 1:length(EstimatedValues[,1])){

			cellularity=EstimatedValues[i,1]
			ploidy=EstimatedValues[i,2]
			avg.depth.ratio=mean(test$gc$adj[,2])
			seg.tab=na.exclude(do.call(rbind,test$segments))

			cn.alleles<-baf.bayes(Bf=seg.tab$Bf,depth.ratio=seg.tab$depth.ratio,cellularity=cellularity,ploidy=ploidy,avg.depth.ratio=avg.depth.ratio)
			seg.tab=cbind(seg.tab,cn.alleles)

			write.table(seg.tab, file = paste(OUT_DIR,"/segments_",i,".txt",sep=""), col.names = TRUE, row.names = FALSE, sep = "\t")

			if (i==1){

				par(mfrow=c(2,1))

				genome.view(seg.cn=seg.tab, info.type="CNt")
				legend("bottomright",bty="n",c("Tumor Copy Number"),col=c("red"),inset=c(0.0,-0.1),pch=15,xpd=TRUE)
				mtext(text=paste("\n",SAMPLEID," Choice ",i," ploidy=",ploidy," cellularity=",cellularity,sep=""), side = 3, line = -2, outer = TRUE, cex=1.5, font=2)

				genome.view(seg.cn=seg.tab, info.type="AB")
				legend("bottomright",bty="n",c("Tumor Copy Number"),col=c("red"),inset=c(0.0,-0.1),pch=15,xpd=TRUE)

			}else{

				genome.view(seg.cn=seg.tab, info.type="CNt")
				mtext(text=paste("\n",SAMPLEID," Choice ",i," ploidy=",ploidy," cellularity=",cellularity,sep=""), side = 3, line = 1, outer = TRUE, cex=1.5, font=2)

				genome.view(seg.cn=seg.tab, info.type="AB")
				legend("bottomright",bty="n",c("Tumor Copy Number"),col=c("red"),inset=c(0.0,-0.08),pch=15,xpd=TRUE)
			}

			sequenza:::plotRawGenome(test, cellularity = cellularity,  ploidy = ploidy)
			mtext(text=paste("\n",SAMPLEID," Choice ",i," ploidy=",ploidy," cellularity=",cellularity,sep=""), side = 3, line = 1, outer = TRUE, cex=1.5, font=2)
		}

	dev.off()

	#Call CNVs and mutations using the estimated parameters
	
	avg.depth.ratio <- mean(test$gc$adj[, 2])
	write(paste("cellularity : ",cellularity.user,"\nploidy : ",ploidy.user,"\naverage.depth.ratio : ",avg.depth.ratio,sep=""),file=paste(OUT_DIR,"/",SAMPLEID,"_cellularity_ploidy.txt",sep=""))



}