87 lines
3.3 KiB
R
Executable File
87 lines
3.3 KiB
R
Executable File
#!/bin/env Rscript
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args <- commandArgs(TRUE)
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verbose = TRUE
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input = args[1]
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targetTITV = as.numeric(args[2])
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suppressLegend = ! is.na(args[3])
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# -----------------------------------------------------------------------------------------------
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# Useful general routines
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# -----------------------------------------------------------------------------------------------
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MIN_FP_RATE = 0.001 # 1 / 1000 is min error rate
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titvFPEst <- function(titvExpected, titvObserved) {
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max(min(1 - (titvObserved - 0.5) / (titvExpected - 0.5), 1), MIN_FP_RATE)
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}
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titvFPEstV <- function(titvExpected, titvs) {
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sapply(titvs, function(x) titvFPEst(titvExpected, x))
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}
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nTPFP <- function(nVariants, FDR) {
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return(list(TP = nVariants * (1 - FDR/100), FP = nVariants * (FDR / 100)))
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}
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leftShift <- function(x, leftValue = 0) {
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r = rep(leftValue, length(x))
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for ( i in 1:(length(x)-1) ) {
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#print(list(i=i))
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r[i] = x[i+1]
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}
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r
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}
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# -----------------------------------------------------------------------------------------------
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# Tranches plot
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# -----------------------------------------------------------------------------------------------
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data2 = read.table(paste(input,".tranches",sep=""),sep=",",head=T)
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cols = c("cornflowerblue", "cornflowerblue", "darkorange", "darkorange")
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density=c(20, -1, -1, 20)
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outfile = paste(input, ".FDRtranches.pdf", sep="")
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pdf(outfile, height=5, width=8)
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alpha = 1 - titvFPEstV(targetTITV, data2$novelTITV)
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#print(alpha)
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numGood = round(alpha * data2$numNovel);
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#numGood = round(data2$numNovel * (1-data2$FDRtranche/100))
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numBad = data2$numNovel - numGood;
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numPrevGood = leftShift(numGood, 0)
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numNewGood = numGood - numPrevGood
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numPrevBad = leftShift(numBad, 0)
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numNewBad = numBad - numPrevBad
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d=matrix(c(numPrevGood,numNewGood, numNewBad, numPrevBad),4,byrow=TRUE)
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#print(d)
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barplot(d/1000,horiz=TRUE,col=cols,space=0.2,xlab="Number of Novel Variants (1000s)", density=density, cex.axis=1.25, cex.lab=1.25) # , xlim=c(250000,350000))
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#abline(v= d[2,dim(d)[2]], lty=2)
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#abline(v= d[1,3], lty=2)
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if ( ! suppressLegend )
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legend(5, 2.25, c('Cumulative TPs','Tranch-specific TPs', 'Tranch-specific FPs', 'Cumulative FPs' ), fill=cols, density=density, bg='white', cex=1.25)
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mtext("Ti/Tv",2,line=2.25,at=length(data2$FDRtranche)*1.2,las=1, cex=1)
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mtext("FDR",2,line=0,at=length(data2$FDRtranche)*1.2,las=1, cex=1)
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axis(2,line=-1,at=0.7+(0:(length(data2$FDRtranche)-1))*1.2,tick=FALSE,labels=data2$FDRtranche, las=1, cex.axis=1.25)
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axis(2,line=1,at=0.7+(0:(length(data2$FDRtranche)-1))*1.2,tick=FALSE,labels=data2$novelTITV, las=1, cex.axis=1.25)
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dev.off()
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#
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#data2 = read.table(paste(input,".tranches",sep=""),sep=",",head=T)
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#cols = c("steelblue","orange")
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#outfile = paste(input, ".FDRtranches.pdf", sep="")
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#pdf(outfile, height=7, width=8)
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#alpha = (data2$novelTITV - 0.5) / (targetTITV - 0.5);
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#numGood = round(alpha * data2$numNovel);
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#numBad = data2$numNovel - numGood;
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#d=matrix(c(numGood,numBad),2,byrow=TRUE)
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#barplot(d,horiz=TRUE,col=cols,space=0.2,xlab="Number of Novel Variants",ylab="Novel Ti/Tv --> FDR (%)")
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#legend('topright',c('implied TP','implied FP'),col=cols,lty=1,lwd=16)
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#axis(2,line=-1,at=0.7+(0:(length(data2$FDRtranche)-1))*1.2,tick=FALSE,labels=data2$FDRtranche)
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#axis(2,line=0.4,at=0.7+(0:(length(data2$FDRtranche)-1))*1.2,tick=FALSE,labels=data2$novelTITV)
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#dev.off()
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