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timing.code.R
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timing.code.R
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################################################
## ALL FUNCTIONS (PLOTS, TIMING, ETC.)
################################################
## 1) functions to summarise CNAs
################################################
## returns fraction LOH along the genome
getLOH.fraction <- function(cn) ## cn is a matrix from an ascat profile
{
loh <- (cn[,"Tumour.BCN"]==0)
allG <- sum(cn[,"End.Position"]/1000-cn[,"Start.Position"]/1000,na.rm=T)
LOH <- sum(cn[loh,"End.Position"]/1000-cn[loh,"Start.Position"]/1000,na.rm=T)
LOH/allG
}
## returns fraction homozygous deletion (HD) along the genome
getHD.fraction <- function(cn)
{
hd=(as.character(cn$nMin1_A)=="0" & as.character(cn$nMaj1_A)=="0")
allG <- sum(cn[,"endpos"]/1000-cn[,"startpos"]/1000,na.rm=T)
HD <- sum(cn[hd,"endpos"]/1000-cn[hd,"startpos"]/1000,na.rm=T)
HD/allG
}
## returns fraction subclonal CNAs along the genome
getSubclonal.fraction <- function(cn)
{
cond <- p.adjust(cn[,"pval"],method="fdr")<0.05
allG <- sum(cn[,"endpos"]/1000-cn[,"startpos"]/1000,na.rm=T)
SUBCLONAL <- sum(cn[cond,"endpos"]/1000-cn[cond,"startpos"]/1000,na.rm=T)
SUBCLONAL/allG
}
## returns total size of genome with HD
getHD.size <- function(cn)
{
hd=(as.character(cn$nMin1_A)=="0" & as.character(cn$nMaj1_A)=="0")
sum(cn[hd,"endpos"]-cn[hd,"startpos"])
}
## returns total size of genome with subclonal fraction around 50% (indicative of missed WGD)
getSize50CCF <- function(cn)
{
ccf50 <- cn$frac1_A<.55 & cn$frac1_A>.45
sum(cn[ccf50,"endpos"]/1000000-cn[ccf50,"startpos"]/1000000)
}
####################################
####################################
## 2) Filter SNVs by context
####################################
## get reference genome (to filter for (C>T)pG SNVs)
getRefGenome <- function(fasta=FASTA)
{
dna <- readDNAStringSet(fasta,format="fasta")
dna <- dna[1:25,]
return(dna)
}
## filter (C>T)pG SNVs in mut (vcf-like matrix)
filterCtoTatCpG <- function(mut,colPosition=3,refCol=3,altCol=4)
{
c2 <- colPosition
keepCT <- mut[,refCol]=="C" & mut[,altCol]=="T"
keepGA <- mut[,refCol]=="G" & mut[,altCol]=="A"
keepCTGA <- keepCT |keepGA
contexts <- sapply(which(keepCTGA),function(x) as(dna[[as.character(mut[x,1])]][mut[x,c2]+(-1:1)],"character"))
##contextsrev <- sapply(contexts,function(string) as(reverseComplement(as(string,"DNAString")),"character"))
keepCT <- contexts%in%paste0(c("C","G","T","A"),"CG")
keepGA <- contexts%in%paste0("CG",c("C","G","T","A"))
mut[keepCTGA,][keepCT | keepGA,]
}
####################################
####################################
## 3) Timing of WGD(s)
####################################
## dummy return of timing of WGD when no WGD is called
getT1 <- function(muts,cna)
{
return(1)
}
## return of timing and mutation counts of WGD
getT2 <- function(muts,cna)
{
cna <- cbind(cna,cna[,"Tumour.TCN"]-cna[,"Tumour.BCN"],cna[,"Tumour.BCN"],
cna[,c("Start.Position","End.Position")])
colnames(cna)[(ncol(cna)-3):ncol(cna)] <- c("nMaj1_A","nMin1_A","startpos","endpos")
muts <- cbind(muts,muts[,"no.chrs.bearing.mut"])
colnames(muts)[ncol(muts)] <- "multiplicity"
require(GenomicRanges)
## 2+0
cna. <- cna[cna[,"nMaj1_A"]==2 & cna[,"nMin1_A"]==0,]
gr1 <- GRanges(cna.[,"Chromosome"],IRanges(cna.[,"startpos"],cna.[,"endpos"]))
gr2 <- GRanges(muts[,"chr"],IRanges(muts[,"end"],muts[,"end"]))
ov <- findOverlaps(gr2,gr1)
mm <- muts[queryHits(ov),]
counts2 <- sum(mm$multiplicity==2,na.rm=T)
counts1 <- sum(mm$multiplicity==1,na.rm=T)/2
## 2+2
cna. <- cna[cna[,"nMaj1_A"]==2 & cna[,"nMin1_A"]==2,]
gr1 <- GRanges(cna.[,"Chromosome"],IRanges(cna.[,"startpos"],cna.[,"endpos"]))
gr2 <- GRanges(muts[,"chr"],IRanges(muts[,"end"],muts[,"end"]))
ov <- findOverlaps(gr2,gr1)
mm <- muts[queryHits(ov),]
counts2.22 <- sum(mm$multiplicity>=2,na.rm=T)
counts1.22 <- sum(mm$multiplicity==1,na.rm=T)/2
list(counts2=counts2,
halfcounts1=counts1,
timingWGD1=counts2/(counts1+counts2),
counts2.22=counts2.22,
halfcounts1.22=counts1.22,
timingWGD1.22=counts2.22/(counts1.22+counts2.22),
timingWGD1.tot=(counts2.22+counts2)/(counts1.22+counts2.22+counts1+counts2))
}
## return of timing and mutation counts of second WGD
getT4 <- function(muts,cna)
{
cna <- cbind(cna,cna[,"Tumour.TCN"]-cna[,"Tumour.BCN"],cna[,"Tumour.BCN"],
cna[,c("Start.Position","End.Position")])
colnames(cna)[(ncol(cna)-3):ncol(cna)] <- c("nMaj1_A","nMin1_A","startpos","endpos")
muts <- cbind(muts,muts[,"no.chrs.bearing.mut"])
colnames(muts)[ncol(muts)] <- "multiplicity"
require(GenomicRanges)
## 4+0
cna. <- cna[cna[,"nMaj1_A"]==4 & cna[,"nMin1_A"]==0,]
gr1 <- GRanges(cna.[,"Chromosome"],IRanges(cna.[,"startpos"],cna.[,"endpos"]))
gr2 <- GRanges(muts[,"chr"],IRanges(muts[,"end"],muts[,"end"]))
ov <- findOverlaps(gr2,gr1)
mm <- muts[queryHits(ov),]
counts3 <- sum(mm$multiplicity==2,na.rm=T)/2
counts4 <- sum(mm$multiplicity>=4,na.rm=T)+counts3
counts2 <- (sum(mm$multiplicity==2,na.rm=T)+counts3)/2
counts1 <- sum(mm$multiplicity==1,na.rm=T)/4
## 4+4
cna. <- cna[cna[,"nMaj1_A"]==4 & cna[,"nMin1_A"]==4,]
gr1 <- GRanges(cna.[,"Chromosome"],IRanges(cna.[,"startpos"],cna.[,"endpos"]))
gr2 <- GRanges(muts[,"chr"],IRanges(muts[,"end"],muts[,"end"]))
ov <- findOverlaps(gr2,gr1)
mm <- muts[queryHits(ov),]
counts3.44 <- sum(mm$multiplicity==3,na.rm=T)/2
counts4.44 <- sum(mm$multiplicity>=4,na.rm=T)+counts3.44
counts2.44 <- (sum(mm$multiplicity==2,na.rm=T)+counts3.44)/2
counts1.44 <- sum(mm$multiplicity==1,na.rm=T)/4
list(counts4=counts4,
halfcounts2=counts2,
quartercounts1=counts1,
timingWGD1=counts4/(counts1+counts2+counts4),
timingWGD2=(counts2+counts4)/(counts1+counts2+counts4),
counts4.44=counts4.44,
halfcounts2.44=counts2.44,
quartercounts1.44=counts1.44,
timingWGD1.44=counts4.44/(counts1.44+counts2.44+counts4.44),
timingWGD2.44=(counts2.44+counts4.44)/(counts1.44+counts2.44+counts4.44),
timingWGD1.tot=(counts4.44+counts4)/(counts1.44+counts2.44+counts4.44+counts1+counts2+counts4),
timingWGD2.tot=(counts2.44+counts4.44+counts2+counts4)/(counts1.44+counts2.44+counts4.44+counts1+counts2+counts4))
}
## get timing depending on mode of the major allele
getTiming <- function(muts,cna,mode)
{
switch(mode,
"1"=getT1(muts,cna),
"2"=getT2(muts,cna),
"3"=getT4(muts,cna),
"4"=getT4(muts,cna),
"5"=getT4(muts,cna))
}
## customised try catch
mytry <- function(x,ret=list(timingWGD1=NA),silent=T,...)
{
res <- try(x,silent=silent,...)
if(inherits(res,"try-error")) return(ret)
res
}
###############################################################################
###############################################################################
## 4) Timing of drivers SNVs and indels
###############################################################################
## Drivers
## subset SNV mutations falling in driver genes
getDriversSNV <- function(x)
{
require(GenomicRanges)
gr1 <- GRanges(as.character(x[,1]),IRanges(x[,2],x[,2]))
gr2 <- GRanges(gsub("chr","",drivers[,2]),IRanges(as.numeric(drivers[,3]),as.numeric(drivers[,4])))
ov <- findOverlaps(gr1,gr2)
ret <- x[queryHits(ov),]
ret <- cbind(ret,as.character(drivers[subjectHits(ov),1]))
}
## subset indel mutations falling in driver genes
getDriversINDEL <- function(x)
{
require(GenomicRanges)
gr1 <- GRanges(as.character(x[,1]),IRanges(x[,2],x[,2]))
gr2 <- GRanges(gsub("chr","",drivers[,2]),IRanges(as.numeric(drivers[,3]),as.numeric(drivers[,4])))
ov <- findOverlaps(gr1,gr2)
ret <- x[queryHits(ov),]
ret <- cbind(ret,as.character(drivers[subjectHits(ov),1]))
}
## (not used) subset CNA segments involving driver genes with LOH, HD or Amplification
getDriversCNA <- function(x)
{
require(GenomicRanges)
gr1 <- GRanges(as.character(x[,2]),IRanges(x[,3],x[,4]))
gr2 <- GRanges(gsub("chr","",drivers[,2]),IRanges(as.numeric(drivers[,3]),as.numeric(drivers[,4])))
ov <- findOverlaps(gr1,gr2)
ret <- x[queryHits(ov),]
ret <- cbind(ret,drivers[subjectHits(ov),1])
keepAmp <- (ret[,"Tumour.TCN"]-ret[,"Tumour.BCN"])>=5
keepHD <- (ret[,"Tumour.TCN"]-ret[,"Tumour.BCN"])==0
keepLOH <- ret[,"Tumour.BCN"]==0
ret <- cbind(ret,rep(NA,nrow(ret)))
ret[keepLOH,ncol(ret)] <- "LOH"
ret[keepHD,ncol(ret)] <- "HD"
ret[keepAmp,ncol(ret)] <- "Amplified"
ret[,]
}
## (not used) annotate mutations as clonal or subclonal and for which cluster they are assigned to (from DPCLust output)
getClusters <- function(muts)
{
clusts <- sort(tapply(1:nrow(muts),muts[,"cluster"],function(x) mean(muts[x,"ccf"],na.rm=T)),decreasing=T)
clonal <- which(clusts>=.95)
if(length(clonal)==0) clonal <- 1
subclonal <- (1:length(clusts))[-c(clonal)]
list(clonal=names(clusts)[clonal],
subclonal=names(clusts)[subclonal])
}
## time driver mutations using inferred multiplicities and cna states
## no WGD
timeDrive.1 <- function(ind)
{
snv <- mutsDrive[[ind]][[1]]
if(nrow(snv)==0) return(NA)
##clonality <- sapply(snv[,ncol(snv)-1],function(x) if(x%in%clusters[[ind]][[1]]) "clonal" else "subclonal")
cna <- mutsDrive[[ind]][[2]]
cna <- cbind(cna,cna[,"Tumour.TCN"]-cna[,"Tumour.BCN"],cna[,"Tumour.BCN"],
cna[,c("Start.Position","End.Position")])
colnames(cna)[(ncol(cna)-3):ncol(cna)] <- c("nMaj1_A","nMin1_A","startpos","endpos")
timing <- sapply(1:nrow(snv),function(x)
{
gene <- as.character(snv[x,ncol(snv)])
ww <- which(cna[,ncol(cna)-5]==gene)[1]
states <- cna[ww,c("nMaj1_A","nMin1_A")]
if(any(is.na(states))) return(NA)
if(states[1]>1)
{
if(snv[x,"no.chrs.bearing.mut"]>1) return("early")
if(states[2]==0) return("late")
}
return(NA)
})
list(SNV=cbind(snv,timing),
CNA=cna[cna[,ncol(cna)]=="HD" & !is.na(cna[,ncol(cna)]),])
}
## timing logic for multiplicity relative to WGD
timeWGD <- function(states, ## copy number states
mult) ## mutation multiplicity
{
if(states[1]>1)
{
if(mult>1) return("beforeWGD1")
if(states[2]==0 | states[2]>1) return("afterWGD1")
}
if(mult>1) return("beforeWGD1")
return(NA)
}
## timing logic for multiplicity relative to second WGD
timeWGD2 <- function(states,
mult)
{
if(states[1]>1 & states[1]<4)
{
if(mult>1) return("beforeWGD2")
if(states[2]==0 | states[2]>1) return("afterWGD2")
}
if(states[1]>=4)
{
if(states[2]==0)
{
if(mult==1) return("afterWGD2")
if(mult<4) return("afterWGD1beforeWGD2")
if(mult>=4) return("beforeWGD1")
}
if(states[2]==4)
{
if(mult==1) return("afterWGD2")
if(mult<4) return("afterWGD1beforeWGD2")
if(mult>=4) return("beforeWGD1")
}
}
if(mult>1) return("beforeWGD2")
return(NA)
}
## time driver mutations using inferred multiplicities and cna states
## 1xWGD
timeDrive.2 <- function(ind)
{
snv <- mutsDrive[[ind]][[1]]
if(nrow(snv)==0) return(NA)
##clonality <- sapply(snv[,ncol(snv)-1],function(x) if(x%in%clusters[[ind]][[1]]) "clonal" else "subclonal")
cna <- mutsDrive[[ind]][[2]]
cna <- cbind(cna,cna[,"Tumour.TCN"]-cna[,"Tumour.BCN"],cna[,"Tumour.BCN"],
cna[,c("Start.Position","End.Position")])
colnames(cna)[(ncol(cna)-3):ncol(cna)] <- c("nMaj1_A","nMin1_A","startpos","endpos")
timing <- sapply(1:nrow(snv),function(x)
{
gene <- as.character(snv[x,ncol(snv)])
ww <- which(cna[,ncol(cna)-5]==gene)[1]
states <- cna[ww,c("nMaj1_A","nMin1_A")]
if(all(!is.na(states))) return(timeWGD(states,snv[x,"no.chrs.bearing.mut"]))
NA
})
list(SNV=cbind(snv,timing),
CNA=cna[cna[,ncol(cna)]=="HD" & !is.na(cna[,ncol(cna)]),])
}
## time driver mutations using inferred multiplicities and cna states
## >1xWGD
timeDrive.4 <- function(ind)
{
snv <- mutsDrive[[ind]][[1]]
if(nrow(snv)==0) return(NA)
##clonality <- sapply(snv[,ncol(snv)-1],function(x) if(x%in%clusters[[ind]][[1]]) "clonal" else "subclonal")
cna <- mutsDrive[[ind]][[2]]
cna <- cbind(cna,cna[,"Tumour.TCN"]-cna[,"Tumour.BCN"],cna[,"Tumour.BCN"],
cna[,c("Start.Position","End.Position")])
colnames(cna)[(ncol(cna)-3):ncol(cna)] <- c("nMaj1_A","nMin1_A","startpos","endpos")
timing <- sapply(1:nrow(snv),function(x)
{
gene <- as.character(snv[x,ncol(snv)])
ww <- which(cna[,ncol(cna)-5]==gene)[1]
states <- cna[ww,c("nMaj1_A","nMin1_A")]
if(all(!is.na(states))) return(timeWGD2(states,snv[x,"no.chrs.bearing.mut"]))
NA
})
list(SNV=cbind(snv,timing),
CNA=cna[cna[,ncol(cna)]=="HD" & !is.na(cna[,ncol(cna)]),])
}
## time driver mutation depending on mode
timeDrive <- function(x)
{
mode <- modeCN[x]
return(if(mode%in%c("0","1"))
timeDrive.1(x)
else if(mode=="2")
timeDrive.2(x)
else
timeDrive.4(x))
}
###############################################################################
###############################################################################
## 5) plotting functions for timing
###############################################################################
## (not used) get confidence intervals for timing of WGDx2 using binomial distrib
getCIs.WGD2 <- function(count1,count2,count4,ci=c(0.025,.975))
{
tot <- round(count2+count4+count1)
qbinom(ci,size=tot,prob=(count4+count2)/tot)/tot
}
## (not used) get confidence intervals for timing of WGDx1 using binomial distrib
getCIs.WGD1 <- function(count1,count2,ci=c(0.025,.975),count4=0)
{
tot <- round(count1+count2+count4)
qbinom(ci,size=tot,prob=count2/tot)/tot
}
## (old version/over-written) plotting function for the timing
plotTiming <- function(timings)
{
tWGD1s <- sapply(timings,function(x)
{
if(length(x)==1) return(NA)
if("timingWGD1"%in%names(x))
{
return(x$timingWGD1.tot)
}
return(NA)
})
tWGD2s <- sapply(timings,function(x)
{
if(length(x)==1) return(NA)
if("timingWGD2"%in%names(x))
{
return(x$timingWGD2.tot)
}
return(NA)
})
CIWGD1 <- lapply(timings,function(x)
{
if(length(x)==1) return(NA)
if("timingWGD1"%in%names(x))
{
if(!"timingWGD2"%in%names(x))
return(getCIs.WGD1(x$halfcounts1+x$halfcounts1.22,x$counts2+x$counts2.22))
else
return(getCIs.WGD1(x$halfcounts2+x$halfcounts2.44,x$counts4+x$counts4.44,
count4=x$quartercounts1+x$quartercounts1.44))
}
return(NA)
})
CIWGD2 <- lapply(timings,function(x)
{
if(length(x)==1) return(NA)
if("timingWGD2"%in%names(x))
{
return(getCIs.WGD2(x$quartercounts1+x$quartercounts1.44,
x$halfcounts2+x$halfcounts2.44,
x$counts4+x$counts4.44))
}
return(NA)
})
ord <- order(tWGD1s,tWGD2s,decreasing=F)
ciwgd1.low <- sapply(CIWGD1,function(x) if(length(x)==2) x[1] else NA)
ciwgd1.high <- sapply(CIWGD1,function(x) if(length(x)==2) x[2] else NA)
ciwgd2.low <- sapply(CIWGD2,function(x) if(length(x)==2) x[1] else NA)
ciwgd2.high <- sapply(CIWGD2,function(x) if(length(x)==2) x[2] else NA)
plot(1:length(tWGD1s),tWGD1s[ord],
frame=F,
xlab="Samples",
ylab="Relative Mutational Timing",
##ylim=c(min(ciwgd1.low,na.rm=T),1),
ylim=c(-.5,1.2),
xlim=c(0,length(tWGD1s)+10),
xaxt="n",
yaxt="n",
pch=21,
col=rgb(.4,.4,.4,.4))
##axis(side=1,at=seq(0,70,10),seq(0,70,10))
text(1:length(tWGD1s)+1,rep(-.1,length(tWGD1s)),labels=samps[ord],cex=.55,srt=45,pos=2)
axis(side=2,at=seq(0,1,.2),seq(0,1,.2))
points(1:length(tWGD1s),tWGD2s[ord],
pch=19,col=rgb(.2,.2,.2,.4))
abline(h=c(0,.25,0.5,.75,1),lty=2,lwd=.5,col=rgb(.5,.5,.5,.5))
segments(1:length(tWGD1s),ciwgd1.low[ord],1:length(tWGD1s),
ciwgd1.high[ord],
lwd=.7,col=rgb(.5,.5,.5,.5))
segments(1:length(tWGD2s),ciwgd2.low[ord],1:length(tWGD2s),
ciwgd2.high[ord],lwd=.7,
col=rgb(.5,.5,.5,.5))
NN <- -5
text(length(timings)-NN,.97,"MRCA")
arrows(length(timings)-NN,.45,length(timings)-NN,.25,length=.07)
arrows(length(timings)-NN,.55,length(timings)-NN,.75,length=.07)
text(length(timings)-NN,.5,"Clonal")
text(length(timings)-NN,.03,"Early life")
##text(length(timings)-NN,1.1,"Subclonal")
list(ord=ord,
tWGD1s=tWGD1s[ord],
tWGD2s=tWGD2s[ord],
ciwgd1.low=ciwgd1.low[ord],
ciwgd1.high=ciwgd1.high[ord],
ciwgd2.low=ciwgd2.low[ord],
ciwgd2.high=ciwgd2.high[ord])
}
## adding drivers to the plot
plotPointDrive <- function(x,mD,retPlot,cols,PCH)
{
mD <- cbind(mD,rep("clonal",nrow(mD)))
colnames(mD)[ncol(mD)] <- "clonality"
COLGENE <- 17
MINIOFF <- .03
for(i in 1:nrow(mD))
{
if(mD[i,"clonality"]=="clonal")
{
if(mD[i,"timing"]=="beforeWGD1" & !is.na(mD[i,"timing"]))
{
points(x,
retPlot$ciwgd1.low[x]-MINIOFF-OFFSETS[as.character(mD[i,COLGENE])]*MINIOFF,
col=COLS[as.character(mD[i,COLGENE])],
pch=PCH)
}
if(grepl("beforeWGD2",mD[i,"timing"])& !is.na(mD[i,"timing"]))
{
points(x,
retPlot$ciwgd2.low[x]-MINIOFF-OFFSETS[as.character(mD[i,COLGENE])]*MINIOFF,
col=COLS[as.character(mD[i,COLGENE])],
pch=PCH)
}
if(mD[i,"timing"]=="afterWGD1"& !is.na(mD[i,"timing"]))
{
points(x,
retPlot$ciwgd1.high[x]+MINIOFF+OFFSETS[as.character(mD[i,16])]*MINIOFF,
col=COLS[as.character(mD[i,16])],
pch=PCH)
}
if(mD[i,"timing"]=="afterWGD2"& !is.na(mD[i,"timing"]))
{
points(x,
retPlot$ciwgd2.high[x]+MINIOFF+OFFSETS[as.character(mD[i,COLGENE])]*MINIOFF,
col=COLS[as.character(mD[i,COLGENE])],
pch=PCH)
}
}
if(mD[i,"clonality"]=="subclonal" & !grepl("before",mD[i,"timing"])& !is.na(mD[i,"timing"]))
{
points(x,
1.1+OFFSETS[as.character(mD[i,COLGENE])]*MINIOFF,
col=COLS[as.character(mD[i,COLGENE])],
pch=PCH)
}
}
}
## (not used) adding subclonal mutations
plotSubclonalMuts <- function(mutsDriveTimed,
retPlot,
cols=COLS,PCH=19)
{
mD <- lapply(retPlot$ord,function(x) mutsDriveTimed[[x]])
## plot(1:length(mD),col=rgb(0,0,0,0),xlab="",ylab="Subclonal",xaxt="n",yaxt="n",frame=F)
sapply(1:length(mD),function(x)
{
cat(".")
if(!is.null(nrow(mD[[x]][[1]])))
if(nrow(mD[[x]][[1]])>0)
{
plotPointDrive(x,mD[[x]][[1]],retPlot,cols,PCH=PCH)
}
})
}
## (not used) heatmap of presence/absence of driver mutations per sample
heatmapDrive <- function(mutsDriveTimed,retPlot)
{
mD <- lapply(retPlot$ord,function(x) mutsDriveTimed[[x]])
mat <- matrix(F,nrow(drivers),length(mD))
rownames(mat) <- drivers[,1]
for(i in 1:length(mD))
{
if(!is.null(nrow(mD[[i]][[1]])))
if(nrow(mD[[i]][[1]])>0)
{
mat[,i] <- drivers[,1]%in%as.character(mD[[i]][[1]][,17])
}
}
LIMX <- length(retPlot$tWGD1s)+10
plot(0,0,xlab="",ylab="",
xaxt="n",yaxt="n",
frame=F,
col=rgb(0,0,0,0),
xlim=c(0,LIMX),
ylim=c(0,1))
nc <- ncol(mat)
pasc <- length(retPlot$tWGD1s)/nc
nr <- nrow(mat)
pasr <- 1/nr
for(i in 1:nrow(mat))
{
for(j in 1:ncol(mat))
{
polygon(c((j-1)*pasc,(j-1)*pasc,j*pasc,j*pasc)+.5,
c((i-1)*pasr,i*pasr,i*pasr,(i-1)*pasr),
border=NA,
col=if(mat[i,j]) COLS[rownames(mat)[i]] else rgb(0,0,0,0))
}
}
}
## adding dummy plot+legend
mylegend <- function(PCH=19)
{
plot(0,0,xlab="",ylab="",
xaxt="n",yaxt="n",
frame=F,
col=rgb(0,0,0,0),
xlim=c(0,1),
ylim=c(0,1))
graphics:::legend("left",pch=PCH,col=COLS,legend=names(COLS),box.col=rgb(0,0,0,0))
}
## (not used) boxplot+points for subclonal fractions
boxpoints <- function(l,col1=rgb(0,0,0,.5),pch1=19,cex1=1,...)
{
boxplot(l,frame=F,col=rgb(0,0,0,0),...)
jitter <- list()
for(i in 1:length(l))
{
jitter[[i]] <- rnorm(length(l[[i]]),sd=0.1)
points(i+jitter[[i]],l[[i]],col=col1,pch=pch1,cex=cex1)
}
##segments(1+jitter[[1]],l[[1]],2+jitter[[2]],l[[2]],lwd=1,col=rgb(.3,.3,.3,.2))
}
## (not used) add a wrapping background for real time projection in molecular-time-based plots
addAge <- function(ages,ord,colL)
{
ages <- ages[ord]
lines <- lapply(c(20,40,60,80),function(x)
x/ages)
##lapply(lines,function(x) points(1:length(x),x,type="l",col=colL))
polygon(x=c(1,1:length(lines[[1]]),length(lines[[1]])),
y=c(0,lines[[1]],0),col=rgb(0,0,0,.02),border=NA)
polygon(x=c(1:length(lines[[1]]),length(lines[[1]]):1),
y=c(lines[[1]],lines[[2]][length(lines[[2]]):1]),
col=rgb(0,0,0,.1),border=NA)
polygon(x=c(1:length(lines[[1]]),length(lines[[1]]):1),
y=c(lines[[2]],lines[[3]][length(lines[[2]]):1]),
col=rgb(0,0,0,.15),border=NA)
polygon(x=c(1:length(lines[[1]]),length(lines[[1]]):1),
y=c(lines[[3]],lines[[4]][length(lines[[2]]):1]),
col=rgb(0,0,0,.2),border=NA)
polygon(x=c(1,1:length(lines[[1]]),length(lines[[1]])),
y=c(2,lines[[4]],2),
col=rgb(0,0,0,.3),border=NA)
}
## plot summary of molecular timing of WGD and drivers
## returns confidence intervals
plotTiming <- function(timings,groups,samps..,colL=rgb(.4,.4,.4,.1))
{
tWGD1s <- sapply(timings,function(x)
{
if(length(x)==1) return(NA)
if("timingWGD1"%in%names(x))
{
return(x$timingWGD1.tot)
}
return(NA)
})
tWGD2s <- sapply(timings,function(x)
{
if(length(x)==1) return(NA)
if("timingWGD2"%in%names(x))
{
return(x$timingWGD2.tot)
}
return(NA)
})
CIWGD1 <- lapply(timings,function(x)
{
if(length(x)==1) return(NA)
if("timingWGD1"%in%names(x))
{
if(!"timingWGD2"%in%names(x))
return(getCIs.WGD1(x$halfcounts1+x$halfcounts1.22,x$counts2+x$counts2.22))
else
return(getCIs.WGD1(x$halfcounts2+x$halfcounts2.44,x$counts4+x$counts4.44,
count4=x$quartercounts1+x$quartercounts1.44))
}
return(NA)
})
CIWGD2 <- lapply(timings,function(x)
{
if(length(x)==1) return(NA)
if("timingWGD2"%in%names(x))
{
return(getCIs.WGD2(x$quartercounts1+x$quartercounts1.44,
x$halfcounts2+x$halfcounts2.44,
x$counts4+x$counts4.44))
}
return(NA)
})
theages <- clin$Age_at_diagnosis
ord <- order(groups,tWGD1s,decreasing=F)
ciwgd1.low <- sapply(CIWGD1,function(x) if(length(x)==2) x[1] else NA)
ciwgd1.high <- sapply(CIWGD1,function(x) if(length(x)==2) x[2] else NA)
ciwgd2.low <- sapply(CIWGD2,function(x) if(length(x)==2) x[1] else NA)
ciwgd2.high <- sapply(CIWGD2,function(x) if(length(x)==2) x[2] else NA)
COOL <- rgb(t(col2rgb(RColorBrewer:::brewer.pal(12,"Paired"))/255))
##COOL <- (rgb(t(col2rgb(sample(topo.colors(10))))/255))
COLSCN <- COOL[groups[ord]]
plot(1:length(tWGD1s),tWGD1s[ord],
frame=F,
xlab="Samples",
ylab="Relative Mutational Timing",
##ylim=c(min(ciwgd1.low,na.rm=T),1),
ylim=c(-.5,1.2),
xlim=c(0,length(tWGD1s)+10),
xaxt="n",
yaxt="n",
pch=21,
col=rgb(.4,.4,.4,.4))
##axis(side=1,at=seq(0,70,10),seq(0,70,10))
text(1:length(tWGD1s)+1,rep(-.1,length(tWGD1s)),
labels=samps..[ord],
cex=.55,
srt=45,
col=COLSCN,
pos=2)
axis(side=2,at=seq(0,1,.2),seq(0,1,.2))
points(1:length(tWGD1s),tWGD2s[ord],
pch=19,col=rgb(.2,.2,.2,.4))
##abline(h=c(0,.25,0.5,.75,1),lty=2,lwd=.5,col=rgb(.5,.5,.5,.5))
addAge(ages=theages,
ord=ord,
colL=colL)
segments(1:length(tWGD1s),ciwgd1.low[ord],1:length(tWGD1s),
ciwgd1.high[ord],
lwd=.7,col=rgb(.5,.5,.5,.5))
segments(1:length(tWGD2s),ciwgd2.low[ord],1:length(tWGD2s),
ciwgd2.high[ord],lwd=.7,
col=rgb(.5,.5,.5,.5))
legend("topright",paste0("CNsig",1:7),col=COOL[1:7],pch=15)
NN <- -5
text(length(timings)-NN,.97,"MRCA")
arrows(length(timings)-NN,.45,length(timings)-NN,.25,length=.07)
arrows(length(timings)-NN,.55,length(timings)-NN,.75,length=.07)
text(length(timings)-NN,.5,"Clonal")
text(length(timings)-NN,.03,"Early life")
##text(length(timings)-NN,1.1,"Subclonal")
list(ord=ord,
tWGD1s=tWGD1s[ord],
tWGD2s=tWGD2s[ord],
ciwgd1.low=ciwgd1.low[ord],
ciwgd1.high=ciwgd1.high[ord],
ciwgd2.low=ciwgd2.low[ord],
ciwgd2.high=ciwgd2.high[ord])
}
## get confidence interval of timing using bootstrapping for second WGD
getCIs.WGD2 <- function(count1,count2,count4,ci=c(0.025,.975),Nrep=1000)
{
c2 <- round(count2+count4+.51)
c1 <- round(count1+0.51)
tot <- c2+c1
vv <- c(rep("c1",c1),rep("c2",c2))
quantile(sapply(1:Nrep,function(x)
{
vv. <- sample(vv,rep=T)
sum(vv.=="c2")/tot
}),probs=ci)
}
## get confidence interval of timing using bootstrapping for first WGD
getCIs.WGD1 <- function(count1,count2,ci=c(0.025,.975),count4=0,Nrep=1000)
{
c2 <- round(count2+.51)
c1 <- round(count1+0.51)
c4 <- round(count4+.51)
tot <- c2+c1+c4
vv <- c(rep("c1",c1+c4),rep("c2",c2))
quantile(sapply(1:Nrep,function(x)
{
vv. <- sample(vv,rep=T)
sum(vv.=="c2")/tot
}),probs=ci)
}
## boxplot+points
boxpoints <- function(l,col1=rgb(0,0,0,.5),pch1=19,cex1=1,...)
{
boxplot(l,frame=F,col=rgb(0,0,0,0),...)
for(i in 1:length(l))
{
points(i+rnorm(length(l[[i]]),sd=0.1),l[[i]],col=col1,pch=pch1,cex=cex1)
}
}
## plotting function for real-time timing of WGDs per signature group
boxpoints.segs <- function(l,
lSegslow,
lSegshigh,
lGroups,
lPch,
col1=rgb(0,0,0,.5),pch1=19,cex1=1,...)
{
ll <- sapply(l,length)
nms <- names(l)
l <- lapply(which(ll>1),function(x) l[[x]])
lSegslow <- lapply(which(ll>1),function(x) lSegslow[[x]])
lSegshigh <- lapply(which(ll>1),function(x) lSegshigh[[x]])
lGroups <- lapply(which(ll>1),function(x) lGroups[[x]])
lPch <- lapply(which(ll>1),function(x) lPch[[x]])
names(l) <- nms[ll>3]
meds <- sapply(l,median)
ord <- order(meds,decreasing=T)
nms <- names(l)
l <- lapply(ord,function(x) l[[x]])
lSegslow <- lapply(ord,function(x) lSegslow[[x]])
lSegshigh <- lapply(ord,function(x) lSegshigh[[x]])
lGroups <- lapply(ord,function(x) lGroups[[x]])
lPch <- lapply(ord,function(x) lPch[[x]])
names(l) <- nms[ord]
boxplot(l,frame=F,col=rgb(0,0,0,0),...)
jitter <- list()
lSegslow <- lapply(1:length(lSegslow),function(x) lSegslow[[x]]*as.numeric(!is.na(l[[x]])))
lSegshigh <- lapply(1:length(lSegshigh),function(x) lSegshigh[[x]]*as.numeric(!is.na(l[[x]])))
COOL <- rgb(t(col2rgb(RColorBrewer:::brewer.pal(12,"Paired"))/255))
for(i in 1:length(l))
{
jitter[[i]] <- sort(rnorm(length(l[[i]]),sd=0.1))
ord <- order(l[[i]],decreasing=F)
points(i+jitter[[i]],l[[i]][ord],pch=lPch[[i]][ord],cex=cex1,col=paste0(COOL[lGroups[[i]]][ord],"99"))
segments(i+jitter[[i]],lSegslow[[i]][ord],i+jitter[[i]],lSegshigh[[i]][ord],col=paste0(COOL[lGroups[[i]]][ord],"21"))
}
##segments(1+jitter[[1]],l[[1]],2+jitter[[2]],l[[2]],lwd=1,col=rgb(.3,.3,.3,.2))
}
########################################################
########################################################
## 6) Inferring multiplicities of SNVs and indels
########################################################
## functions for pre-formatting and inference of multiplicities using dpclust preprocessing functions
## writes a file with positions and base change for SNV
createLociFile <- function(vcfdat, outfile, chrom_col, pos_col, ref_col, alt_col)
{
loci = vcfdat[, c(chrom_col, pos_col, ref_col, alt_col)]
loci_file = "loci.txt"
write.table(loci, file=outfile, sep="\t", quote=F, row.names=F, col.names=F)
}
## writes a file with positions and base change for indels
createLociFile.indel <- function(vcfdat, outfile, chrom_col, pos_col, ref_col, alt_col)
{
loci = cbind(vcfdat[, c(chrom_col, pos_col)],rep("A",nrow(vcfdat)),rep("T",nrow(vcfdat)))
loci_file = "loci.txt"
write.table(loci, file=outfile, sep="\t", quote=F, row.names=F, col.names=F)
}
## extract allele counts from VCF (Caveman)
getCounts <- function(vec)
{
A1 <- as.numeric(gsub("(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*)",
"\\2",vec))
A2 <- as.numeric(gsub("(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*)",
"\\6",vec))
As <- A1+A2
C1 <- as.numeric(gsub("(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*)",
"\\3",vec))
C2 <- as.numeric(gsub("(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*)",
"\\7",vec))
Cs <- C1+C2
G1 <- as.numeric(gsub("(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*)",
"\\4",vec))
G2 <- as.numeric(gsub("(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*)",
"\\8",vec))
Gs <- G1+G2
T1 <- as.numeric(gsub("(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*)",
"\\5",vec))
T2 <- as.numeric(gsub("(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*):(.*)",
"\\9",vec))
Ts <- T1+T2
allcounts <- cbind(As,Cs,Gs,Ts)
colnames(allcounts) <- c("A","C","G","T")
allcounts
}
## write alle counts file for inferring multiplicities for SNV
createAlleleCountsFile <- function(vcfdat, outfile)
{
CT <- getCounts(vcfdat[,11])
mutCount <- CT[,"A"]
mutCount[vcfdat[,5]=="G"] <- CT[vcfdat[,5]=="G","G"]
mutCount[vcfdat[,5]=="C"] <- CT[vcfdat[,5]=="C","C"]
mutCount[vcfdat[,5]=="T"] <- CT[vcfdat[,5]=="T","T"]
refCount <- CT[,"A"]
refCount[vcfdat[,4]=="G"] <- CT[vcfdat[,4]=="G","G"]
refCount[vcfdat[,4]=="C"] <- CT[vcfdat[,4]=="C","C"]
refCount[vcfdat[,4]=="T"] <- CT[vcfdat[,4]=="T","T"]
counts_table <- mutwt2allelecounts(counts.alt=mutCount,
counts.ref=refCount,
allele.alt=as.character(vcfdat[,5]),
allele.ref=as.character(vcfdat[,4]))
output <- data.frame(as.character(vcfdat[,1]), vcfdat[,2],
counts_table, rowSums(counts_table))
colnames(output) <- c("#CHR","POS","Count_A",
"Count_C",
"Count_G","Count_T","Good_depth")
write.table(output, file=outfile, sep="\t", quote=F, row.names=F)
}
## write alle counts file for inferring multiplicities for indels
createAlleleCountsFile.indel <- function(vcfdat, outfile)
{
refCount <- mutCount <- rep(NA,nrow(vcfdat))
for(i in 1:nrow(vcfdat))
{
flags <- strsplit(as.character(vcfdat[i,9]),split=":")[[1]]
refC <- which(flags=="WTR")
altC <- which(flags=="MTR")
sre <- c(".*",rep(":.*",length(flags)-1))
sre[refC] <- ":(.*)"
sre <- paste0(sre,collapse="")
refCount[i] <- as.numeric(gsub(sre,"\\1",vcfdat[i,11]))
sre <- c(".*",rep(":.*",length(flags)-1))
sre[altC] <- ":(.*)"
sre <- paste0(sre,collapse="")
mutCount[i] <- as.numeric(gsub(sre,"\\1",vcfdat[i,11]))
}
counts_table <- mutwt2allelecounts(counts.alt=mutCount,
counts.ref=refCount,
allele.alt=rep("A",length(mutCount)),
allele.ref=rep("T",length(mutCount)))
output <- data.frame(as.character(vcfdat[,1]), vcfdat[,2],
counts_table, rowSums(counts_table))
colnames(output) <- c("#CHR","POS","Count_A",
"Count_C",
"Count_G","Count_T","Good_depth")
write.table(output, file=outfile, sep="\t", quote=F, row.names=F)
}
## formatting of allele counts for ref and alt
mutwt2allelecounts <- function(counts.alt, counts.ref, allele.alt, allele.ref)
{
output = array(0, c(length(allele.ref), 4))
nucleotides = c("A", "C", "G", "T")
nucleo.index = match(allele.alt, nucleotides)
for (i in 1:nrow(output)) {
output[i,nucleo.index[i]] = counts.alt[i]
}
nucleo.index = match(allele.ref, nucleotides)
for (i in 1:nrow(output)) {
output[i,nucleo.index[i]] = counts.ref[i]
}
return(output)
}
## uses dpclust3p pre-processing to derive multiplicities
## https://github.com/Wedge-Oxford/dpclust_smchet_docker
writeDPfile <- function(dpFile="dpInput.txt",
sampleName,
battenberg_rho_psi_file,
VCFFILE,
BBFILE,
GENDER)
{
vcfdat <- read.table(VCFFILE)
loci_file = "loci.txt"
createLociFile(vcfdat, loci_file, 1,2,4,5)
## Create allelecounts file
allelecounts_file = "alleleCounts.txt"
createAlleleCountsFile(vcfdat, allelecounts_file)
suppressWarnings(runGetDirichletProcessInfo(loci_file=loci_file,
allele_frequencies_file=allelecounts_file,
cellularity_file=battenberg_rho_psi_file,
subclone_file=BBFILE,
gender=GENDER,
SNP.phase.file="NA",
mut.phase.file="NA",
output_file=dpFile))
}
## uses dpclust3p pre-processing to derive multiplicities for indels
## https://github.com/Wedge-Oxford/dpclust_smchet_docker
writeDPfile.indel <- function(dpFile="dpInput.txt",
sampleName,
battenberg_rho_psi_file,
VCFFILE,
BBFILE,
GENDER)
{
vcfdat <- read.table(VCFFILE)
loci_file = "loci.indel.txt"
createLociFile.indel(vcfdat, loci_file, 1,2,4,5)
## Create allelecounts file
allelecounts_file = "alleleCounts.indel.txt"
createAlleleCountsFile.indel(vcfdat, allelecounts_file)
suppressWarnings(runGetDirichletProcessInfo(loci_file=loci_file,
allele_frequencies_file=allelecounts_file,
cellularity_file=battenberg_rho_psi_file,
subclone_file=BBFILE,
gender=GENDER,
SNP.phase.file="NA",
mut.phase.file="NA",
output_file=dpFile))
}
## dpclust3p is used to see Battenberg (BB) output files
## the following function takes an ASCAT file and format it as a BB-like file
writeBBlike.ASCAT <- function(ascat,ASCATFILE)
{
cn <- c("chr", "startpos", "endpos", "BAF", "pval",
"LogR", "ntot", "nMaj1_A", "nMin1_A", "frac1_A",
"nMaj2_A", "nMin2_A", "frac2_A", "SDfrac_A",
"SDfrac_A_BS", "frac1_A_0.025", "frac1_A_0.975", "nMaj1_B",
"nMin1_B", "frac1_B", "nMaj2_B", "nMin2_B", "frac2_B", "SDfrac_B",
"SDfrac_B_BS", "frac1_B_0.025", "frac1_B_0.975", "nMaj1_C", "nMin1_C",
"frac1_C", "nMaj2_C", "nMin2_C", "frac2_C", "SDfrac_C",
"SDfrac_C_BS", "frac1_C_0.025", "frac1_C_0.975", "nMaj1_D",
"nMin1_D", "frac1_D", "nMaj2_D", "nMin2_D", "frac2_D", "SDfrac_D",
"SDfrac_D_BS", "frac1_D_0.025", "frac1_D_0.975", "nMaj1_E",
"nMin1_E", "frac1_E", "nMaj2_E", "nMin2_E", "frac2_E", "SDfrac_E",