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DNAfunction.R
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DNAfunction.R
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# TODO: Add comment
#
# Author: FWang9
###############################################################################
############Using ASCAT
############segmentation and estimate DNA copy number based on germline mutation
############the formate of inputdata were chr, position, refallele counts in tumor, altAllele counts in tumor, refallele counts in normal, altAllele counts in normal
############tab seprate and without headline
############load ASCAT function
############install bbmle and emdbook package
##############################################################################################################
####part1.run ASCAT to get segmentation and allelic copy number in DNA level
###ASCAT in: get the inputdata of ASCAT
ASCATin<-function(data,sample){
Me=median(data$TlogR)
M=max(data$TlogR)
m=min(data$TlogR)
data$Tlog[data$TlogR>=Me]=(data$TlogR[data$TlogR>=Me]-Me)/(M-Me)
data$Tlog[data$TlogR<Me]=(data$TlogR[data$TlogR<Me]-Me)/(Me-m)
data$Nlog=1
tumor_logR=data.frame(SNP=paste("SNP",c(1:dim(data)[1]),sep=""),chr=as.character(data$chr),pos=data$pos,sample=data$Tlog)
tumor_BAF=data.frame(SNP=paste("SNP",c(1:dim(data)[1]),sep=""),chr=as.character(data$chr),pos=data$pos,sample=data$tfrac)
normal_logR=data.frame(SNP=paste("SNP",c(1:dim(data)[1]),sep=""),chr=as.character(data$chr),pos=data$pos,sample=data$Nlog)
normal_BAF=data.frame(SNP=paste("SNP",c(1:dim(data)[1]),sep=""),chr=as.character(data$chr),pos=data$pos,sample=data$nfrac)
colnames(tumor_logR)=c("SNP","chr","pos",sample)
colnames(tumor_BAF)=c("SNP","chr","pos",sample)
colnames(normal_logR)=c("SNP","chr","pos",sample)
colnames(normal_BAF)=c("SNP","chr","pos",sample)
tumor_logRname=paste(sample,"_tumorlogR.txt",sep="")
tumor_BAFname=paste(sample,"_tumorBAF.txt",sep="")
normal_logRname=paste(sample,"_normallogR.txt",sep="")
normal_BAFname=paste(sample,"_normalBAF.txt",sep="")
write.table(tumor_logR,tumor_logRname,sep="\t",col.names=TRUE,row.names = FALSE,quote = FALSE)
write.table(tumor_BAF,tumor_BAFname,sep="\t",col.names=TRUE,row.names = FALSE,quote = FALSE)
write.table(normal_logR,normal_logRname,sep="\t",col.names=TRUE,row.names = FALSE,quote = FALSE)
write.table(normal_BAF,normal_BAFname,sep="\t",col.names=TRUE,row.names = FALSE,quote = FALSE)
ASCATdata=c(tumor_logRname,tumor_BAFname,normal_logRname,normal_BAFname)
return (ASCATdata)
}
###ASCATout: get the output of ASCAT
ASCATout<-function(ASCATdata,sample,chromosome){
tumor_logRname=ASCATdata[1]
tumor_BAFname=ASCATdata[2]
normal_logRname=ASCATdata[3]
normal_BAFname=ASCATdata[4]
ascat.bc = ascat.loadData(tumor_logRname,tumor_BAFname,normal_logRname,normal_BAFname,chrs=chromosome)
ascat.bc = ascat.aspcf(ascat.bc)
ascat.output = ascat.runAscat(ascat.bc)
alpha=ascat.output$aberrantcellfraction
segment=ascat.output$segments
ACN=data.frame(nMajor=segment$nMajor,nMinor=segment$nMinor)
segment$nMajor=apply(ACN,1,max)
segment$nMinor=apply(ACN,1,min)
segment=data.frame(chr=segment$chr,startpos=segment$startpos,endpos=segment$endpos,nMajor=segment$nMajor,nMinor=segment$nMinor)
alpha=ascat.output$aberrantcellfraction
ploidy=ascat.output$ploidy
DNAout=list(alpha=alpha,segment=segment,ploidy=ploidy)
#save(DNAout,file=paste(sample,".somaticCN.Rdata",sep=""))
return(DNAout)
}
####part 1 end
#############################################################################################################
#############################################################################################################
####part 2: estimate the copy number of somatic mutation and iterative optimization
#MCN: get the integer copy number of somatic mutation
MCN<-function(alpha,segment,somatic){
for (j in 1:dim(segment)[1]){
tempsomatic=somatic[somatic$chr==as.character(segment$chr[j])&somatic$pos>=segment$startpos[j]&somatic$pos<=segment$endpos[j],]
tempsomatic$SMCN=tempsomatic$tfrac*(2*(1-alpha)+alpha*(segment$nMajor[j]+segment$nMinor[j]))/alpha
if (j==1){
somaticnew=tempsomatic
}else{
somaticnew=rbind(somaticnew,tempsomatic)
}
}
somaticnew$SACN=round(somaticnew$SMCN)
return(somaticnew)
}
#MutPlot: plot the copy number of somatic mutation
MutPlot<-function(segment,summaryres,mutation,plotname="SNV.CNV.pdf"){
segment=read.csv(segment,sep="\t")
summaryres=read.csv(summaryres,sep="\t",header=F)
data=read.csv(mutation,sep="\t")
purity=round(summaryres[1,2],2)
Heterogeneity=round(summaryres[2,2],2)
ploidy=round(summaryres[3,2],2)
pdf(plotname,width=20,height=7)
d=sum(as.numeric(segment$end)-as.numeric(segment$start))
nMajor=max(segment$Dmajor,segment$Dminor,data$altD)
plot(0,0,col="white",xlim=c(0,d),ylim=c(0,nMajor),xlab="",ylab="Absolute copy number",main=paste("Purity = ",purity,", Heterogeneity = ",Heterogeneity,", Ploidy = ",ploidy,sep=""),axes=FALSE)
chro=as.character(unique(segment$chr))
axisindex=0
for (j in 1:length(chro)){
subseg=segment[segment$chr==chro[j],]
subd=sum(subseg$end-subseg$start)
subsomatic=data[data$chr==chro[j]&data$type=="Somatic",]
for (k in 1:dim(subseg)[1]){
subsom=subsomatic[subsomatic$pos>=subseg$start[k]&subsomatic$pos<=subseg$end[k],]
subsom$index=subsom$pos-subseg$start[k]
#points(subsom$index+axisindex,subsom$SACN,pch=20,col=rgb(0,0,1,alpha=0.7),cex=1.2)
points(subsom$index+axisindex,subsom$altD,pch=20,col=rgb(0,0,1,alpha=0.7),cex=1.2)
if (subseg$Dmajor[k]==subseg$Dminor[k]){
x=c(axisindex,axisindex+subseg$end[k]-subseg$start[k])
y1=rep(subseg$Dmajor[k]-0.1,length(x))
lines(x,y1,col="purple",lwd=2)
y2=rep(subseg$Dmajor[k]+0.1,length(x))
lines(x,y2,col="purple",lwd=2)
z=c(subseg$Dmajor[k]-0.1,subseg$Dmajor[k]+0.1)
x1=c(axisindex,axisindex)
lines(x1,z,col="purple",lwd=2)
x2=c(axisindex+subseg$end[k]-subseg$start[k],axisindex+subseg$end[k]-subseg$start[k])
lines(x2,z,col="purple",lwd=2)
}else{
x=c(axisindex,axisindex+subseg$end[k]-subseg$start[k])
y1=rep(subseg$Dmajor[k]-0.1,length(x))
lines(x,y1,col="red",lwd=2)
y2=rep(subseg$Dmajor[k]+0.1,length(x))
lines(x,y2,col="red",lwd=2)
z=c(subseg$Dmajor[k]-0.1,subseg$Dmajor[k]+0.1)
x1=c(axisindex,axisindex)
lines(x1,z,col="red",lwd=2)
x2=c(axisindex+subseg$end[k]-subseg$start[k],axisindex+subseg$end[k]-subseg$start[k])
lines(x2,z,col="red",lwd=2)
y11=rep(subseg$Dminor[k]-0.1,length(x))
lines(x,y11,col="green",lwd=2)
y21=rep(subseg$Dminor[k]+0.1,length(x))
lines(x,y21,col="green",lwd=2)
z=c(subseg$Dminor[k]-0.1,subseg$Dminor[k]+0.1)
lines(x1,z,col="green",lwd=2)
lines(x2,z,col="green",lwd=2)
}
axisindex=axisindex+subseg$end[k]-subseg$start[k]+1
}
abline(v=axisindex-1,col="gray")
text((axisindex-1-sum(subseg$end-subseg$start)+axisindex-1)/2,nMajor,substr(chro[j],4,nchar(chro[j])))
}
axis(side=2)
dev.off()
}
#SNPplot<-function(segment,data,plotname,index,alpha){
# pdf(plotname,width=20,height=7)
# d=sum(segment$endpos-segment$startpos)
# nMajor=max(segment$nMajor,segment$nMinor,data$SMCN)
# plot(0,0,col="white",xlim=c(0,d),ylim=c(0,nMajor),xlab="",ylab="Absolute copy number",main="",axes=FALSE)
# chro=unique(segment$chr)
# axisindex=0
# for (j in 1:length(chro)){
# subseg=segment[segment$chr==chro[j],]
# subd=sum(subseg$endpos-subseg$startpos)
# subsomatic=data[data$chr==chro[j],]
# for (k in 1:dim(subseg)[1]){
# subsom=subsomatic[subsomatic$pos>=subseg$startpos[k]&subsomatic$pos<=subseg$endpos[k],]
# TCN=subseg$nMajor[k]+subseg$nMinor[k]
# subsom$SMCN=(subsom$tfrac*(2*(1-alpha)+alpha*TCN)-(1-alpha))/alpha
# subsom$SACN=round(subsom$SMCN)
# subsom$index=subsom$pos-subseg$startpos[k]
# if (index==1){
# points(subsom$index+axisindex,subsom$SACN,pch=20,col=rgb(0,0,1,alpha=0.7),cex=1.2)
# }
# else{
# points(subsom$index+axisindex,subsom$SMCN,pch=20,col=rgb(0,0,1,alpha=0.7),cex=1.2)
# }
# if (subseg$nMajor[k]==subseg$nMinor[k]){
# x=c(axisindex,axisindex+subseg$endpos[k]-subseg$startpos[k])
# y1=rep(subseg$nMajor[k]-0.1,length(x))
# lines(x,y1,col="purple",lwd=2)
# y2=rep(subseg$nMajor[k]+0.1,length(x))
# lines(x,y2,col="purple",lwd=2)
# z=c(subseg$nMajor[k]-0.1,subseg$nMajor[k]+0.1)
# x1=c(axisindex,axisindex)
# lines(x1,z,col="purple",lwd=2)
# x2=c(axisindex+subseg$endpos[k]-subseg$startpos[k],axisindex+subseg$endpos[k]-subseg$startpos[k])
# lines(x2,z,col="purple",lwd=2)
# }else{
# x=c(axisindex,axisindex+subseg$endpos[k]-subseg$startpos[k])
# y1=rep(subseg$nMajor[k]-0.1,length(x))
# lines(x,y1,col="red",lwd=2)
# y2=rep(subseg$nMajor[k]+0.1,length(x))
# lines(x,y2,col="red",lwd=2)
# z=c(subseg$nMajor[k]-0.1,subseg$nMajor[k]+0.1)
# x1=c(axisindex,axisindex)
# lines(x1,z,col="red",lwd=2)
# x2=c(axisindex+subseg$endpos[k]-subseg$startpos[k],axisindex+subseg$endpos[k]-subseg$startpos[k])
# lines(x2,z,col="red",lwd=2)
# y11=rep(subseg$nMinor[k]-0.1,length(x))
# lines(x,y11,col="green",lwd=2)
# y21=rep(subseg$nMinor[k]+0.1,length(x))
# lines(x,y21,col="green",lwd=2)
# z=c(subseg$nMinor[k]-0.1,subseg$nMinor[k]+0.1)
# lines(x1,z,col="green",lwd=2)
# lines(x2,z,col="green",lwd=2)
# }
# axisindex=axisindex+subseg$endpos[k]-subseg$startpos[k]+1
# }
# abline(v=axisindex-1,col="gray")
# text((axisindex-1-sum(subseg$endpos-subseg$startpos)+axisindex-1)/2,nMajor,substr(chro[j],4,nchar(chro[j])))
# }
# axis(side=2)
# dev.off()
#}
#randomBinom: random sampling based on binomial distribution
randomBinom<-function(y,times){
return(rbinom(times,y[1],y[2]))
}
#ransomSample: random sample based on times
randomSample<-function(data,times){
if (dim(data)[1]>0){
random=apply(data,1,randomBinom,times=times)
return(t(random))
}
}
#RMCN: get the allele copy number of random sampling
RMCN<-function(R,somatic,alpha,segment){
randomsomatic=data.frame(chr=somatic$chr,pos=somatic$pos,refNumT=somatic$refNumT,altNumT=R)
randomsomatic$tfrac=randomsomatic$altNumT/(randomsomatic$altNumT+randomsomatic$refNumT)
randomres=MCN(alpha,segment,somatic=randomsomatic)
return(randomres$SMCN)
}
#difCN: calculated the difference between allelic copy number and integer copy number
difCN<-function(x){
dif=abs(x-round(x))
return(dif)
}
####calculated the fraction of integer copy number = 0
#subclone: calculated the number of somatic mutation with copy number = 0
subclone<-function(x){
return(sum(round(x)==0))
}
###somaticPurity: calculated the tumor purity based on the copy number of somatic mutation
somaticPurity<-function(somatic,segment,DNAalpha){
alpha_up=c()
for (j in 1:dim(segment)[1]){
subdata=somatic[somatic$chr==as.character(segment$chr[j])&somatic$pos>=segment$startpos[j]&somatic$pos<=segment$endpos[j],]
if (dim(subdata)[1]>0){
TCN=segment$nMajor[j]+segment$nMinor[j]
alpha1=2*subdata$tfrac/(2*subdata$tfrac+subdata$SACN-subdata$tfrac*TCN)
alpha_up=c(alpha_up,alpha1)
}
}
alpha_up=alpha_up[!is.na(alpha_up)&alpha_up!=Inf]
if (length(alpha_up)<=1){
if (length(alpha_up)==0){
alpha1=DNAalpha
}else{
if (alpha_up>=0 & alpha_up <=1){
alpha1=alpha_up
}else{
alpha1=DNAalpha
}
}
}else{
alpha1=density(alpha_up)$x[which.max(density(alpha_up)$y)]
if (alpha1 > 1 | alpha1 < 0){
alpha_up=alpha_up[alpha_up >=0 & alpha_up <= 1]
if (length(alpha_up)>0){
if (length(alpha_up)==1){
alpha1=alpha_up
}else{
alpha1=density(alpha_up)$x[which.max(density(alpha_up)$y)]
}
}else{
alpha1=DNAalpha
}
}
}
return(alpha1)
}
##CNopt: determine the optimal copy number for each segment among mang candiate CNs
CNpeak<-function(CN){
peak=c()
peakvalue=c()
if (max(CN)<=0){
CN=CN+abs(min(CN))
}
CN=CN[CN>0]
if (length(CN)>3){
k=1
x=density(CN)$x
y=density(CN)$y
for (i in 2:(length(y)-1)){
if (y[i]>=y[i-1]&y[i]>=y[i+1]){
peak[k]=x[i]
peakvalue[k]=y[i]
k=k+1
}
}
peak=peak[peakvalue> (max(peakvalue)/10)]
}else{
peak=unique(CN)
}
return(peak)
}
CNopt<-function(data,expBAF){
dbetabinom(data$y,f1,data$N,theta,log=FALSE)
}
##upACN: update the copy number of segment and ploidy based on germline mutation
upACN<-function(data,segment,alpha){
ploidy=c()
for (j in 1:dim(segment)[1]){
subseg=segment[j,]
subdata=data[data$chr==as.character(segment$chr[j])&data$pos>=segment$startpos[j]&data$pos<=segment$endpo[j],]
if (dim(subdata)[1]>=10){
TCN=round((2*subdata$Ratio-2*(1-alpha))/alpha)
canTCN=round(CNpeak(TCN))
lCN=Inf
sCN=c()
if (length(canTCN)!=0){
for (i in 1:length(canTCN)){
nMajor=round((2*subdata$BAF*(1-alpha)+alpha*subdata$BAF*canTCN[i]-(1-alpha))/alpha)
canMajor=round(CNpeak(nMajor))
if (length(canMajor)>0){
for (k in 1:length(canMajor)){
expBAF=((1-alpha)+alpha*canMajor[k])/(2*(1-alpha)+alpha*canTCN[i])
if (expBAF <= 1 & expBAF >=0){
lCN1=-sum(dbinom(round(subdata$BAF*(subdata$refNumT+subdata$altNumT)),(subdata$refNumT+subdata$altNumT),expBAF,log=TRUE))
if (lCN1 < lCN){
sCN=c(canMajor[k],canTCN[i])
lCN=lCN1
}
}
}
}
}
if (length(sCN)!=0){
subseg$nMajor=sCN[1]
if (sCN[2]-sCN[1]<0){
subseg$nMinor=0
}else{
subseg$nMinor=sCN[2]-sCN[1]
}
}
}
}
if (j==1){
upsegment=subseg
}else{
upsegment=rbind(upsegment,subseg)
}
ploidy=c(ploidy,rep(subseg$nMajor+subseg$nMinor,length=dim(subdata)[1]))
}
upres=list(upsegment=upsegment,ploidy=mean(ploidy))
return(upres)
}
###iterOPT: output optimal result based on Iterative optimization
iterOPT<-function(SNP,somatic,segment,realDIF,realsub,randomdata,times,PD,PS,DNAalpha,cutoff){
out=list()
out1=list()
out2=list()
run=1
PD2=PD
PS2=PS
while(run < cutoff){
alpha1=somaticPurity(somatic=somatic,segment=segment,DNAalpha=DNAalpha)
res1=upACN(data=SNP,segment=segment,alpha=alpha1)
ploidy1=res1$ploidy
segment1=res1$upsegment
somaticres1=MCN(alpha=alpha1,segment=segment1,somatic=somatic)
somaticres1=somaticres1[match(paste(somatic$chr,somatic$pos,sep=":"),paste(somaticres1$chr,somaticres1$pos,sep=":")),]
realDIF1=sum(abs(somaticres1$SMCN-somaticres1$SACN))/dim(somaticres1)[1]
registerDoMC(cores = 4)
randomout1=foreach (j = 1:dim(randomdata)[2], .combine=cbind) %dopar% RMCN(R=randomdata[,j],somatic=somaticres1,alpha=alpha1,segment=segment1)
randomDIF1=apply(randomout1,2,difCN)
aveDIF1=apply(randomDIF1,2,sum)/dim(somaticres1)[1]
realDIF1=sum(abs(somaticres1$SMCN-somaticres1$SACN))/dim(somaticres1)[1]
realsub1=sum(somaticres1$SACN==0)/dim(somaticres1)[1]
if (realDIF1<realDIF | realsub1<realsub){
out$segment=segment1
out$somatic=somaticres1
out$alpha=alpha1
out$ploidy=ploidy1
}
randomsub1=apply(randomout1,2,subclone)
PD1=1-length(aveDIF1[aveDIF1>=realDIF1])/length(aveDIF1)
PS1=length(randomsub1[randomsub1<sum(somaticres1$SACN==0)])/times
if (PD1 < 0.05 & PS1 < 0.05){
out1$segment=segment1
out1$somatic=somaticres1
out1$alpha=alpha1
out1$ploidy=ploidy1
print.noquote(paste("iteration times = ",run,sep=""))
p=PD1+PS1
print.noquote(paste("p value = ",p,sep=""))
return(out1)
break
}
if ((PD1 + PS1) < (PD2+PS2)){
out2$segment=segment1
out2$somatic=somaticres1
out2$alpha=alpha1
out2$ploidy=ploidy1
PD2=PD1
PS2=PS1
}
realDIF=realDIF1
realsub=realsub1
somatic=somaticres1
segment=segment1
if(dim(somatic)[1]>=100){
if (abs(alpha1-DNAalpha)<0.01){
if (length(out2)!=0){
print.noquote(paste("iteration times = ",run,sep=""))
p=PD1+PS1
print.noquote(paste("p value = ",p,sep=""))
return(out2)
}else{
print.noquote(paste("iteration times = ",run,sep=""))
p=PD1+PS1
print.noquote(paste("p value = ",p,sep=""))
return(out)
}
}else{
DNAalpha=alpha1
run=run+1
}
}else if (dim(somatic)[1]<100&dim(somatic)[1]>=20){
if (abs(alpha1-DNAalpha)<0.001){
if (length(out2)!=0){
print.noquote(paste("iteration times = ",run,sep=""))
p=PD1+PS1
print.noquote(paste("p value = ",p,sep=""))
return(out2)
}else{
print.noquote(paste("iteration times = ",run,sep=""))
p=PD1+PS1
print.noquote(paste("p value = ",p,sep=""))
return(out)
}
}else{
DNAalpha=alpha1
run=run+1
}
}else{
if (abs(alpha1-DNAalpha)<0.0001){
if (length(out2)!=0){
print.noquote(paste("iteration times = ",run,sep=""))
p=PD1+PS1
print.noquote(paste("p value = ",p,sep=""))
return(out2)
}else{
print.noquote(paste("iteration times = ",run,sep=""))
p=PD1+PS1
print.noquote(paste("p value = ",p,sep=""))
return(out)
}
}else{
DNAalpha=alpha1
run=run+1
}
}
}
if (run==cutoff){
if (length(out2)!=0){
print.noquote(paste("iteration times = ",run,sep=""))
p=PD1+PS1
print.noquote(paste("p value = ",p,sep=""))
return(out2)
}else{
print.noquote(paste("iteration times = ",run,sep=""))
p=PD1+PS1
print.noquote(paste("p value = ",p,sep=""))
return(out)
}
}
}
sequenzRun<-function(data,sample,chromosome){
outdata=data.frame(chromosome=data$chr,position=data$pos,base.ref=data$ref,depth.normal=data$Nsum,depth.tumor=data$Tsum,depth.ratio=(data$Nsum)/(data$Tsum),Af=1-data$tfrac,Bf=data$tfrac)
outdata$zygosity.normal[data$nfrac>=0.2&data$nfrac<=0.8]="het"
outdata$zygosity.normal[data$nfrac<0.2|data$nfrac>0.8]="hom"
outdata$GC.percent=50
outdata$good.reads=outdata$depth.tumor
genotype=data.frame(ref=data$ref[outdata$zygosity.normal=="het"],alt=data$alt[outdata$zygosity.normal=="het"])
genotype=t(apply(genotype,1,sort))
AB.normal=paste(genotype[,1],genotype[,2],sep="")
outdata$AB.normal[outdata$zygosity.normal=="het"]=AB.normal
genotype=data.frame(ref=data$ref[outdata$zygosity.normal=="hom"],alt=data$alt[outdata$zygosity.normal=="hom"])
subdata=data[outdata$zygosity.normal=="hom",]
if (dim(subdata)[1]>0){
subdata$genotype[subdata$nfrac<0.2]=as.character(subdata$ref[subdata$nfrac<0.2])
subdata$genotype[subdata$nfrac>0.8]=as.character(subdata$alt[subdata$nfrac>0.8])
outdata$AB.normal[outdata$zygosity.normal=="hom"]=subdata$genotype
}
AB.tumor <- rep(".", length(outdata$AB.normal))
outdata$AB.tumor=AB.tumor
strand <- AB.tumor
outdata$tumor.strand=strand
normal.pos <- outdata$zygosity.normal == "hom" & outdata$AB.tumor == "."
if (sum(normal.pos)>0){
outdata <-outdata[outdata$depth.ratio > 0 & !is.infinite(outdata$depth.ratio) & !normal.pos, ]
}
outname=paste(sample,".seqz",sep="")
write.table(outdata, outname, col.names = TRUE, row.names = FALSE, sep = "\t")
seqz.data<-read.seqz(outname,gz=FALSE)
##GC correction and normalization depth ratio
print.noquote("GC correction")
gc.stats<-gc.sample.stats(seqz.data)
#gc.stats <- gc.norm(x = seqz.data$depth.ratio,gc = seqz.data$GC.percent)
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$GC.percent)]
##extract information of sequenza input
print.noquote("extract sequenza input")
seqz.data$chr=seqz.data$chromosome
test <- sequenza.extract(seqz.data,gc.stats,chroso=intersect(chromosome,seqz.data$chromosome))
##infer tumor purity and ploidy
print.noquote("sequenza infering")
CP.example <- sequenza.fit(test,mc.cores = getOption("mc.cores", 1L),female=FALSE)
cint <- get.ci(CP.example)
alpha <- cint$max.cellularity
ploidy <- cint$max.ploidy
avg.depth.ratio <- mean(test$gc$adj[, 2])
seg.tab <- na.exclude(do.call(rbind, test$segments))
print.noquote("integrating result")
cn.alleles <- baf.bayes(Bf = seg.tab$Bf, depth.ratio = seg.tab$depth.ratio,cellularity = alpha, ploidy = ploidy,avg.depth.ratio = avg.depth.ratio)
seg.tab <- cbind(seg.tab, cn.alleles)
segment=data.frame(chr=seg.tab$chromosome,startpos=seg.tab$start.pos,endpos=seg.tab$end.pos,nMajor=seg.tab$A,nMinor=seg.tab$B)
index=which(segment$nMajor==0&segment$nMinor==0)
rowindex=setdiff(c(1:dim(segment)[1]),index)
segment=segment[rowindex,]
DNAout=list(alpha=alpha,ploidy=ploidy,segment=segment)
return(DNAout)
}
###DNArun: get final result at the DNA level
outputTitanSegments <- function(results, id, convergeParams, filename = NULL, igvfilename = NULL){
# get all possible states in this set of results
stateTable <- unique(results[, c("TITANstate", "TITANcall")])
rownames(stateTable) <- stateTable[, 1]
rleResults <- t(sapply(unique(results$Chr), function(x){
ind <- results$Chr == x
r <- rle(results$TITANstate[ind])
}))
rleLengths <- unlist(rleResults[, "lengths"])
rleValues <- unlist(rleResults[, "values"])
numSegs <- length(rleLengths)
# convert allelic ratio to symmetric ratios #
results$AllelicRatio <- apply(cbind(results$AllelicRatio, 1-results$AllelicRatio), 1, max, na.rm = TRUE)
segs <- as.data.frame(matrix(NA, ncol = 14, nrow = numSegs,
dimnames = list(c(), c("Sample", "Chromosome", "Start_Position.bp.", "End_Position.bp.",
"Length.snp.", "Median_Ratio", "Median_logR", "TITAN_state", "TITAN_call", "Copy_Number",
"MinorCN", "MajorCN", "Clonal_Cluster", "Cellular_Frequency"))))
segs$Sample <- id
colNames <- c("Chr", "Position", "TITANstate", "AllelicRatio", "LogRatio")
prevInd <- 0
for (j in 1:numSegs){
start <- prevInd + 1
end <- prevInd + rleLengths[j]
segDF <- results[start:end, ]
prevInd <- end
numR <- nrow(segDF)
segs[j, "Chromosome"] <- as.character(segDF[1, "Chr"])
segs[j, "Start_Position.bp."] <- segDF[1, "Position"]
segs[j, "TITAN_state"] <- rleValues[j]
segs[j, "TITAN_call"] <- segDF[1, "TITANcall"]#stateTable[as.character(rleValues[j]), 2]
segs[j, "Copy_Number"] <- segDF[1, "CopyNumber"]
segs[j, "Median_Ratio"] <- round(median(segDF$AllelicRatio, na.rm = TRUE), digits = 6)
segs[j, "Median_logR"] <- round(median(segDF$LogRatio, na.rm = TRUE), digits = 6)
segs[j, "MinorCN"] <- getMajorMinorCN(rleValues[j], convergeParams$symmetric)$majorCN
segs[j, "MajorCN"] <- getMajorMinorCN(rleValues[j], convergeParams$symmetric)$minorCN
segs[j, "Clonal_Cluster"] <- segDF[1, "ClonalCluster"]
segs[j, "Cellular_Frequency"] <- segDF[1, "CellularPrevalence"]
if (segDF[1, "Chr"] == segDF[numR, "Chr"]){
segs[j, "End_Position.bp."] <- segDF[numR, "Position"]
segs[j, "Length.snp."] <- numR
}else{ # segDF contains 2 different chromosomes
print(j)
}
}
if (!is.null(filename)){
# write out detailed segment file #
write.table(segs, file = filename, col.names = TRUE, row.names = FALSE, quote = FALSE, sep = "\t")
}
# write out IGV seg file #
if (!is.null(igvfilename)){
igv <- segs[, c("Sample", "Chromosome", "Start_Position.bp.",
"End_Position.bp.", "Length.snp.", "Median_logR")]
colnames(igv) <- c("sample", "chr", "start", "end", "num.snps", "median.logR")
write.table(igv, file = igvfilename, col.names = TRUE, row.names = FALSE, quote = FALSE, sep = "\t")
}
return(segs)
}
getMajorMinorCN <- function(state, symmetric = TRUE){
majorCN <- NA
minorCN <- NA
if (symmetric){
if (state==0){
majorCN = 0; minorCN = 0;
}else if (state==1){
majorCN = 0; minorCN = 1;
}else if(state==2){
majorCN = 0; minorCN = 2;
}else if (state==3){
majorCN = 1; minorCN = 1;
}else if (state==4){
majorCN = 0; minorCN = 3;
}else if (state==5){
majorCN = 1; minorCN = 2;
}else if (state==6){
majorCN = 0; minorCN = 4;
}else if (state==7){
majorCN = 1; minorCN = 3;
}else if (state==8){
majorCN = 2; minorCN = 2;
}else if (state==9){
majorCN = 0; minorCN = 5;
}else if (state==10){
majorCN = 1; minorCN = 4;
}else if (state==11){
majorCN = 2; minorCN = 3;
}else if (state==12){
majorCN = 0; minorCN = 6;
}else if (state==13){
majorCN = 1; minorCN = 5;
}else if (state==14){
majorCN = 2; minorCN = 4;
}else if (state==15){
majorCN = 3; minorCN = 3;
}else if (state==16){
majorCN = 0; minorCN = 7;
}else if (state==17){
majorCN = 1; minorCN = 6;
}else if (state==18){
majorCN = 2; minorCN = 5;
}else if (state==19){
majorCN = 3; minorCN = 4;
}else if (state==20){
majorCN = 0; minorCN = 8;
}else if (state==21){
majorCN = 1; minorCN = 7;
}else if (state==22){
majorCN = 2; minorCN = 6;
}else if (state==23){
majorCN = 3; minorCN = 5;
}else if (state==24){
majorCN = 4; minorCN = 4;
}
}else{
#stop("symmetric=FALSE not yet supported.")
}
return(list(majorCN = majorCN, minorCN = minorCN))
}
TITANout<-function(DNAinput,chromosome){
DNAinput=DNAinput[DNAinput$nfrac>=0.2&DNAinput$nfrac<=0.8,]
rlength=nchar(as.character(DNAinput$ref))
alength=nchar(as.character(DNAinput$alt))
DNAinput=DNAinput[rlength==1&alength==1,]
titandata=data.frame(chr=DNAinput$chr,pos=DNAinput$pos,ref=DNAinput$ref,refCount=DNAinput$refNumT,Nref=DNAinput$alt,NrefCount=DNAinput$altNumT)
chro=intersect(chromosome,names(table(titandata$chr))[table(titandata$chr)>1])
index=match(as.character(titandata$chr),chro)
titandata=titandata[!is.na(index),]
DNAinput=DNAinput[!is.na(index),]
write.table(titandata,"TITAN.input",col.names=TRUE,row.names=FALSE,sep="\t",quote=FALSE)
numClusters <- 2
params <- loadDefaultParameters(copyNumber=5,numberClonalClusters=numClusters)
data <- loadAlleleCounts("TITAN.input")
data$logR=log((DNAinput$refNumT+DNAinput$altNumT)/(DNAinput$refNumN+DNAinput$altNumN),base=10)
convergeParams <- runEMclonalCN(data,gParams=params$genotypeParams,nParams=params$normalParams,
pParams=params$ploidyParams,sParams=params$cellPrevParams,maxiter=20,maxiterUpdate=1500,
txnExpLen=1e15,txnZstrength=1e5,useOutlierState=FALSE,normalEstimateMethod="map",
estimateS=TRUE,estimatePloidy=TRUE)
optimalPath <- viterbiClonalCN(data,convergeParams)
results <- outputTitanResults(data,convergeParams,optimalPath,filename=NULL,posteriorProbs=F)
results$AllelicRatio=as.numeric(results$AllelicRatio)
results$LogRatio=as.numeric(results$LogRatio)
segs <- outputTitanSegments(results, id = "test", convergeParams,filename=NULL)
ploidy <- tail(convergeParams$phi, 1)
normal <- tail(convergeParams$n, 1)
#mean(as.numeric(segs$Cellular_Frequency[!is.na(segs$Cellular_Frequency)])*(1-normal))
DNAout=list(segment=segs,ploidy=ploidy,alpha=1-normal)
return(DNAout)
}
FACETSout<-function(DNAinput){
facetsinput=data.frame(chr=DNAinput$chr,pos=DNAinput$pos,Nsum=DNAinput$Nsum,NAP=DNAinput$altNumN,Tsum=DNAinput$Tsum,TAP=DNAinput$altNumT)
write.table(facetsinput,"FACETS.input",sep="\t",col.names=FALSE,row.names=FALSE,quote=FALSE)
set.seed(1234)
xx=preProcSample(file="FACETS.input")
oo=procSample(xx,cval=150)
fit<-try(emcncf(oo),silent=TRUE)
alpha=fit$purity
if (!is.na(alpha)){
ploidy=fit$ploidy
segment=data.frame(chr=fit$cncf$chrom,startpos=fit$start,endpos=fit$end,nMajor=(fit$cncf$tcn.em-fit$cncf$lcn.em),nMinor=fit$cncf$lcn.em)
segment=segment[!is.na(segment$nMajor)&!is.na(segment$nMinor)&segment$nMajor!=0&segment$nMinor!=0,]
DNAout=list(alpha=alpha,segment=segment,ploidy=ploidy)
return (DNAout)
}
}
ASCATrun<-function(DNAinput,sample,chromosome){
ASCATdata=ASCATin(data=DNAinput,sample=sample)
##run ASCAT
ASCATres<-try(ASCATout(ASCATdata=ASCATdata,sample=sample,chromosome=chromosome),silent=TRUE)
if (mode(ASCATres)=="list"){
return(ASCATres)
}
}
DNACNA<-function(DNAinput,sample,tempth,chromosome,i){
if (i==1){
print.noquote("Run germline data")
ASCATres<-try(ASCATrun(DNAinput,sample,chromosome),silent=TRUE)
return(ASCATres)
}else if (i==2){
registerDoMC(cores = 4)
TITANres<-try(TITANout(DNAinput=DNAinput,chromosome=chromosome),silent=TRUE)
return (TITANres)
}else if (i ==3){
FACETSres<-try(FACETSout(DNAinput=DNAinput),silent=TRUE)
return (FACETSres)
}else{
sequenzres<-try(sequenzRun(data=DNAinput,sample=sample,chromosome=chromosome),silent=TRUE)
return (sequenzres)
}
}
somaticCN<-function(res,i,somaticdata,method){
if (mode(res[[i]])=="list"){
if (method[i]=="TITAN" & !is.null(res[[i]])&!is.null(res[[i]]$alpha)){
DNAalpha=res[[i]]$alpha
DNAploidy=res[[i]]$ploidy
segment=res[[i]]$segment
segment=data.frame(chr=paste("chr",segment$Chromosome,sep=""),startpos=as.numeric(as.character(segment$Start_Position.bp.)),endpos=as.numeric(as.character(segment$End_Position.bp.)),nMajor=segment$MajorCN,nMinor=segment$MinorCN)
somaticres=MCN(alpha=DNAalpha,segment=segment,somatic=somaticdata)
return (somaticres)
}else if (method[i]=="FACETS" & !is.null(res[[i]])&!is.null(res[[i]]$alpha)){
DNAalpha=res[[i]]$alpha
DNAploidy=res[[i]]$ploidy
segment=res[[i]]$segment
segment=data.frame(chr=paste("chr",segment$chr,sep=""),startpos=segment$startpos,endpos=segment$endpos,nMajor=segment$nMajor,nMinor=segment$nMinor)
somaticres=MCN(alpha=DNAalpha,segment=segment,somatic=somaticdata)
return (somaticres)
}else if (!is.null(res[[i]])&!is.null(res[[i]]$alpha)){
DNAalpha=res[[i]]$alpha
DNAploidy=res[[i]]$ploidy
segment=res[[i]]$segment
somaticres=MCN(alpha=DNAalpha,segment=segment,somatic=somaticdata)
return (somaticres)
}
}
}
randomCN<-function(res,i,randomdata,somaticres,method){
if (mode(res[[i]])=="list"){
if (method[i]=="TITAN" & !is.null(res[[i]])&!is.null(res[[i]]$alpha)&!is.null(somaticres[[i]])){
if (dim(somaticres[[i]])[1]>0){
DNAalpha=res[[i]]$alpha
segment=res[[i]]$segment
segment=data.frame(chr=paste("chr",segment$Chromosome,sep=""),startpos=as.numeric(as.character(segment$Start_Position.bp.)),endpos=as.numeric(as.character(segment$End_Position.bp.)),nMajor=segment$MajorCN,nMinor=segment$MinorCN)
registerDoMC(cores = 4)
randomout=foreach (j = 1:dim(randomdata[[i]])[2], .combine=cbind) %dopar% RMCN(R=randomdata[[i]][,j],somatic=somaticres[[i]],alpha=DNAalpha,segment=segment)
#randomout=apply(randomdata[[i]],2,RMCN,somatic=somaticres[[i]],alpha=DNAalpha,segment=segment)
return(randomout)
}
}else if (method[i]=="FACETS" & !is.null(res[[i]])&!is.null(res[[i]]$alpha)&!is.null(somaticres[[i]])){
if (dim(somaticres[[i]])[1]>0){
DNAalpha=res[[i]]$alpha
segment=res[[i]]$segment
segment=data.frame(chr=paste("chr",segment$chr,sep=""),startpos=segment$startpos,endpos=segment$endpos,nMajor=segment$nMajor,nMinor=segment$nMinor)
registerDoMC(cores = 4)
randomout=foreach (j = 1:dim(randomdata[[i]])[2], .combine=cbind) %dopar% RMCN(R=randomdata[[i]][,j],somatic=somaticres[[i]],alpha=DNAalpha,segment=segment)
#randomout=apply(randomdata[[i]],2,RMCN,somatic=somaticres[[i]],alpha=DNAalpha,segment=segment)
return(randomout)
}
}else if (!is.null(res[[i]])&!is.null(res[[i]]$alpha)&!is.null(somaticres[[i]])){
if (dim(somaticres[[i]])[1]>0){
DNAalpha=res[[i]]$alpha
segment=res[[i]]$segment
registerDoMC(cores = 4)
randomout=foreach (j = 1:dim(randomdata[[i]])[2], .combine=cbind) %dopar% RMCN(R=randomdata[[i]][,j],somatic=somaticres[[i]],alpha=DNAalpha,segment=segment)
return(randomout)
}
}
}
}
randomDIF<-function(randomout,somaticres,i){
if (!is.null(randomout[[i]])){
if (dim(somaticres[[i]])>1){
randomDIF=apply(randomout[[i]],2,difCN)
aveDIF=apply(randomDIF,2,sum)/dim(somaticres[[i]])[1]
return(aveDIF)
}
}
}
realDiff<-function(somaticres,i){
if (!is.null(somaticres[[i]])){
realDIF=sum(abs(somaticres[[i]]$SMCN-somaticres[[i]]$SACN))/dim(somaticres[[i]])[1]
return(realDIF)
}
}
realSub<-function(somaticres,i){
if (!is.null(somaticres[[i]])){
realsub=sum(somaticres[[i]]$SACN==0)/dim(somaticres[[i]])[1]
return(realsub)
}
}
subcloneP<-function(x){
sub0<-function(y){
return(sum(round(y)==0))
}
if (!is.null(x)){
randomsub=apply(x,2,sub0)
return(randomsub)
}
}
DNArun<-function(SNPinput,somaticinput,sample,temppath){
DNAinput=read.csv(SNPinput,sep="\t",header=TRUE)
colnames(DNAinput)=c("chr","pos","ref","alt","refNumN","altNumN","refNumT","altNumT")
chromosome=paste("chr",c(1:22,"X","Y"),sep="")
if (length(levels(factor(DNAinput$chr)))>5){
DNAinput$Tsum=DNAinput$refNumT+DNAinput$altNumT
DNAinput$Nsum=DNAinput$refNumN+DNAinput$altNumN
DNAinput=DNAinput[DNAinput$Tsum!=0,]
DNAinput$tfrac=(DNAinput$altNumT)/(DNAinput$Tsum)
DNAinput$nfrac=(DNAinput$altNumN)/(DNAinput$Nsum)
DNAinput$TlogR=log2(DNAinput$Tsum/DNAinput$Nsum)
DNAinput$BAF[DNAinput$tfrac>=0.5]=DNAinput$tfrac[DNAinput$tfrac>=0.5]
DNAinput$BAF[DNAinput$tfrac<0.5]=1-DNAinput$tfrac[DNAinput$tfrac<0.5]
DNAinput$Ratio=(DNAinput$altNumT+DNAinput$refNumT)/(DNAinput$altNumN+DNAinput$refNumN)
DNAinput=DNAinput[DNAinput$Tsum>=10&DNAinput$Nsum>=10,]
DNAinput=DNAinput[sapply(as.character(DNAinput$chr),nchar)<=5,]
#rlength=nchar(as.character(DNAinput$ref))
#alength=nchar(as.character(DNAinput$alt))
#DNAinput=DNAinput[rlength==1&alength==1,]
setwd(temppath)
methods=c("ASCAT","TITAN","FACETS","sequenz")
somaticdata=read.csv(somaticinput,header=TRUE,sep="\t")
colnames(somaticdata)=c("chr","pos","ref","alt","refNumN","altNumN","refNumT","altNumT")
somaticdata$Tsum=somaticdata$altNumT+somaticdata$refNumT
somaticdata=somaticdata[somaticdata$Tsum>=10,]
somaticdata$tfrac=somaticdata$altNumT/somaticdata$Tsum
#rlength=nchar(as.character(somaticdata$ref))
#alength=nchar(as.character(somaticdata$alt))
#somaticdata=somaticdata[rlength==1&alength==1,]
registerDoMC(cores = 4)
times=1000
res=foreach(i = 1:4) %dopar% DNACNA(DNAinput=DNAinput,sample=sample,tempth=temppath,chromosome=chromosome,i)
print.noquote("Somatic mutation copy number")
somaticres<-foreach(i =1:length(res)) %dopar% somaticCN(res=res,i,somaticdata=somaticdata,method=methods)
somaticindex=0
for (i in 1:length(somaticres)){
if (!is.null(somaticres[[i]])){
if (dim(somaticres[[i]])[1]>0){
somaticindex=1
break
}
}
}
if (somaticindex!=0){
randomdata<-foreach(i=1:length(somaticres)) %dopar% randomSample(data=data.frame(Tsum=somaticres[[i]]$Tsum,tfrac=somaticres[[i]]$tfrac),times=times)
randomout<-foreach(i =1:length(somaticres)) %dopar% randomCN(res,i,randomdata,somaticres,method=methods)
aveDIF<-foreach(i=1:length(somaticres)) %dopar% randomDIF(randomout,somaticres,i)
realDIF<-foreach(i=1:length(somaticres)) %dopar% realDiff(somaticres,i)
realsub<-foreach(i=1:length(somaticres)) %dopar% realSub(somaticres,i)
randomsub<-foreach(i = 1:length(somaticres)) %dopar% subcloneP(x=randomout[[i]])
PDl=list()
PSl=list()
for (i in 1:length(realDIF)){
if (!is.null(realDIF[[i]])){
PDl[[i]]=1-length(aveDIF[[i]][aveDIF[[i]]>=realDIF[[i]]])/length(aveDIF[[i]])
PSl[[i]]=length(randomsub[[i]][randomsub[[i]]<sum(somaticres[[i]]$SACN==0)])/times
}
}
modelindex=c()
k=1
for (i in 1:length(PDl)){
if (!is.null(PDl[[i]])){
if(!is.na(PDl[[i]])){
modelindex[k]=i
k=k+1
}
}
}
PD=c()
PS=c()
for (k in 1:length(modelindex)){
PD[k]=PDl[[modelindex[k]]]
PS[k]=PSl[[modelindex[k]]]
}
minimum=min(PD+PS)
selmeth=methods[modelindex[which(PD+PS==minimum)]]
if ("ASCAT" %in% selmeth){
DNAout=res[[1]]
DNAalpha=res[[1]]$alpha
DNAploidy=res[[1]]$ploidy
segment=res[[1]]$segment
somaticres=somaticres[[1]]
realDIF=realDIF[[1]]
realsub=realsub[[1]]
randomdata=randomdata[[1]]
PD=PDl[[1]]
PS=PSl[[1]]
resmthod="ASCAT"
}else if ("FACETS" %in% selmeth){
DNAout=res[[3]]
DNAalpha=res[[3]]$alpha
DNAploidy=res[[3]]$ploidy
segment=res[[3]]$segment
segment=data.frame(chr=paste("chr",segment$chr,sep=""),startpos=segment$startpos,endpos=segment$endpos,nMajor=segment$nMajor,nMinor=segment$nMinor)
somaticres=somaticres[[3]]
realDIF=realDIF[[3]]
realsub=realsub[[3]]
randomdata=randomdata[[3]]
PD=PDl[[3]]
PS=PSl[[3]]
resmthod="FACETS"
}else if ("TITAN" %in% selmeth){
DNAout=res[[2]]
DNAalpha=res[[2]]$alpha
DNAploidy=res[[2]]$ploidy
segment=res[[2]]$segment
segment=data.frame(chr=paste("chr",segment$Chromosome,sep=""),startpos=as.numeric(as.character(segment$Start_Position.bp.)),endpos=as.numeric(as.character(segment$End_Position.bp.)),nMajor=segment$MajorCN,nMinor=segment$MinorCN)
somaticres=somaticres[[2]]
realDIF=realDIF[[2]]
realsub=realsub[[2]]
randomdata=randomdata[[2]]
PD=PDl[[2]]
PS=PSl[[2]]
resmthod="TITAN"
}else{
DNAout=res[[4]]
DNAalpha=res[[4]]$alpha
DNAploidy=res[[4]]$ploidy
segment=res[[4]]$segment
somaticres=somaticres[[4]]
realDIF=realDIF[[4]]
realsub=realsub[[4]]
randomdata=randomdata[[4]]
PD=PDl[[4]]
PS=PSl[[4]]
resmthod="sequenz"
}
DNAout=list()
DNAout$segment=segment
DNAout$alpha=DNAalpha
DNAout$ploidy=DNAploidy
DNAout$somatic=somaticres
DNAout$method=resmthod
if (PD >= 0.05 | PS >= 0.05){
print.noquote("Iter")
iterout=iterOPT(SNP=DNAinput,somatic=somaticres,segment=segment,realDIF=realDIF,realsub=realsub,randomdata=randomdata,times=times,PD=PD,PS=PS,DNAalpha=DNAalpha,cutoff=50)
if (length(iterout)==0){
return(DNAout)
}else{
iterout$method=resmthod
return(iterout)
}
}else{
return(DNAout)
}
}else{
DNAres=list()
k=1
for (i in 1:4){
if (mode(res[[i]])=="list"){
if (!is.null(res[[i]]$alpha)){
res[[i]]$method=methods[i]
DNAres[[k]]=res[[i]]
k=k+1
}
}
}
ll=c()
for (j in 1:length(DNAres)){
ll[j]=dim(DNAres[[j]]$segment)[1]
}
DNAout=DNAres[[which.max(ll)]]
return(DNAout)
}
}else{
print.noquote(paste(sample,":Germline data is insuccifient",sep=""))
}
}
DNArun1<-function(SNPinput,somaticinput,sample,temppath){
DNAinput=read.csv(SNPinput,sep="\t",header=TRUE)
colnames(DNAinput)=c("chr","pos","ref","alt","refNumN","altNumN","refNumT","altNumT")
chromosome=paste("chr",c(1:22,"X","Y"),sep="")