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fit-hmm.R
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fit-hmm.R
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#!/usr/bin/env Rscript
## options(warn=2,error=recover);
options(error=quote(q("yes")))
args <- commandArgs()
dollar0 <- substring(args[grep("^--file=", args)], 8)
source(sprintf("%s/ded.R", dirname(dollar0)))
source(sprintf("%s/hmmlib.R", dirname(dollar0)))
library("R.methodsS3", lib.loc = dirname(dollar0))
library("R.oo", lib.loc = dirname(dollar0))
opts <- getopts()
indivs <- unlist(strsplit(opts$i,split=","))
sex <- opts$s
dir <- opts$d
outdir <- opts$o
deltapar1 <- as.numeric(opts$p)
deltapar2 <- as.numeric(opts$q)
rfac <- as.numeric(opts$r)
priors <- unlist(strsplit(opts$z,split=","))
theta <- as.numeric(opts$t)
stopifnot(!is.null(indivs), !is.null(dir), !is.null(outdir), length(indivs) == 1)
one.site.per.read <- TRUE
minCoverage <- 0;
contigLengths <- read.csv("msg.chrLengths",header=T,sep=",",as.is=T);
contigs <- sort(as.vector(contigLengths$chr))
rownames(contigLengths) <- contigLengths$chr
main.contigs <- unlist(strsplit(opts$c,split=","))
plot.contigs <- unlist(strsplit(opts$y,split=","))
if(opts$c == "all") main.contigs <- contigs;
if(opts$y == "all") plot.contigs <- contigs;
sex.chroms <- unlist(strsplit(opts$x,split=","))
aveSpace <- sum(as.numeric(contigLengths[contigLengths$chr %in% plot.contigs,]$length)) / length(plot.contigs)
plotPadding <- 10^(ceiling(log10(aveSpace))-2)
alleles <- c("A","C","G","T")
for(indiv in indivs) {
cat(indiv, "\n")
## if(opts$c == "all")
## all.contigs <- system(sprintf("ls %s/%s/refs/par1 | grep -F '-ref.alleles' | perl -pe 's/-ref.alleles//'", dir, indiv), intern=TRUE)
## else all.contigs <- contigs
dataa <- list()
hmmdata.file <- file.path(outdir, indiv, paste(indiv, "hmmprob.RData", sep="-"))
if(file.exists(hmmdata.file)) {
cat("HMM fit for indiv", indiv, "already exists\n")
dataa <- read.object(hmmdata.file)
## next
}
else {
for(contig in main.contigs) {
if(sex == "male" && contig %in% sex.chroms) {
ploidy <- 1
ancestries <- c("par1","par2")
phi <- rep(1/length(ancestries), length(ancestries))
}
else {
ploidy <- 2
ancestries <- c("par1/par1","par1/par2","par2/par2")
phi <- priors
}
cat("\t", contig, sex, ploidy, "\n")
if (!file.exists(sprintf("%s/%s/%s-%s.hmmdata", dir, indiv, indiv, contig))) {
cat("MISSING file for CONTIG ", contig, " INDIV ", indiv, "\n")
cat(sprintf("%s/%s/%s-%s.hmmdata", dir, indiv, indiv, contig),"\n")
next
}
data <- read.data(dir, indiv, contig)
data$read <- factor.contiguous(data$pos)
total_sites <- length(unique(data$read));
cat("\tRound 2: Total number of markers", total_sites, "\n")
ok <- !is.na(data$bad) | !is.na(data$par1ref) & !is.na(data$par2ref) & !is.na(data$cons)
cat("\tRound 2: Removing", sum(!ok), "sites at which par1/par2/cons allele unknown\n")
data$bad[!ok] <- "par1/par2/cons unknown"
ok <- data$A + data$C + data$G + data$T > 0
cat("\tRound 2: Removing", sum(!ok), "sites at which cons allele is known but reads are unknown\n")
data$bad[!ok] <- "reads unknown"
ok <- !is.na(data$bad) | data$par1ref %in% alleles & data$par2ref %in% alleles
data$bad[!ok] <- "par1/par2 not in ACGT"
#data$bad[!ok] <- "par1/par2/cons not in ACGT"
cat("\tRound 2: Removing", sum(!ok), "sites at which par1/par2 ref not %in% {", paste(alleles, collapse=", "), "}\n")
## data <- data[ok,,drop=F]
badpos <- data$pos[!is.na(data$bad)]
data <- data[is.na(data$bad),,drop=F]
ok <- data$par1ref != data$par2ref
cat("\tRemoving", sum(!ok), "sites at which par1 == par2\n")
data <- data[ok,,drop=F]
data$count <- data$A + data$C + data$G + data$T #+ data$N
ok <- data$count >= minCoverage
cat("\tRemoving", sum(!ok), "sites at where coverage is < ",minCoverage,"\n")
data <- data[ok,,drop=F]
if (nrow(data)==0) next
if(one.site.per.read) {
## Sample one site per read
data$read <- factor(data$read)
## ok <- 1:nrow(data) %in% sapply(levels(data$read), function(x) sample(which(data$read == x), 1))
ok <- !duplicated(data$read)
cat("\tRemoving", sum(!ok), "sites from same reads\n")
data <- data[ok,,drop=F]
cat("\tNumber of informative markers:", nrow(data), "\n")
}
cat("\tFinal total of", nrow(data), "sites at which par1 != par2\n")
#if (nrow(data)<20) next
if (nrow(data)==0) next
L <- nrow(data)
K <- length(ancestries)
## Transition probabilities
if(contig %in% main.contigs) {
r <- 1 / contigLengths[contig,"length"]
} else {
cat("\tContig ", contig, " not found in main.contigs - defaulting to contig length of ", contigLengths[1,"chr"], "\n")
r <- 1 / contigLengths[1,"length"] ## Arbitrarily use the first contig for unassembled contigs
}
d <- c(NA, diff(data$pos))
p <- 1 - exp(-r*d*rfac)
Pi <- array(dim=c(L,K,K), dimnames=list(NULL, ancestries, ancestries))
if(ploidy == 2) {
Pi[,"par1/par1","par1/par1"] <- Pi[,"par1/par2","par1/par2"] <- Pi[,"par2/par2","par2/par2"] <- 1-p
Pi[,"par1/par1","par1/par2"] <- Pi[,"par1/par2","par1/par1"] <- Pi[,"par1/par2","par2/par2"] <- Pi[,"par2/par2","par1/par2"] <- p
Pi[,"par1/par1","par2/par2"] <- Pi[,"par2/par2","par1/par1"] <- 0
} else {
Pi[,"par1","par1"] <- Pi[,"par2","par2"] <- 1-p
Pi[,"par1","par2"] <- Pi[,"par2","par1"] <- p
}
Pi[1,,] <- NA
## Allele frequencies in parental backgrounds
ppar1 <- ppar2 <- matrix(NA, nrow=4, ncol=4, dimnames=list(alleles, alleles))
ppar1[] <- deltapar1/3
diag(ppar1) <- 1-deltapar1
ppar2[] <- deltapar2/3
diag(ppar2) <- 1-deltapar2
p1 <- ppar1[data$par1ref,,drop=F]
p2 <- ppar2[data$par2ref,,drop=F]
p12 <- array(c(p1,p2), dim=c(dim(p1),2))
dimnames(p12) <- list(NULL, alleles, NULL)
## Take all (<=50) reads for each site
N<-min(max(data$A+data$C+data$G+data$T+data$N),50) ## Total number of reads
#theta <-1 ## quality value correction - defined by passed option
eps<-paste('eps',seq(1,N,by=1),sep='')
read<-paste('read',seq(1,N,by=1),sep='')
y <- data[,c(alleles,"reads","quals","par1ref"),drop=F]
y$selected_allele <- NA
y[,eps]<-rep(0,N)
y[,read]<-rep(5,N)
fun<-function (x) {
if (x=='A') return (0)
if (x=='C') return (1)
if (x=='G') return (2)
if (x=='T') return (3)
if (x=='N') return (5)
}
for(i in 1:nrow(y)) {
total.reads <- unlist(strsplit(cleanupReadPileup(y[i,"reads"],y[i,"par1ref"]),''))
y[i,"selected_allele"] <- total.reads[sample(length(total.reads),1)] ## Sample one read for plotting
qual<-NULL
qual_corrected<-NULL
for (s in 1:min(length(total.reads),N)){
y[i,read[s]] <-lapply(total.reads[s],fun)
qual<-c(qual,(charToInt(unlist(strsplit(y[i,"quals"],''))[s])-33))
}
for (g in 1:length(qual)) {
qual_corrected[g]<-qual[g]*(theta^(order(qual)[g]-1)) ## quality value correction
y[i,eps[g]] <- 10^(-(qual_corrected[g])/10)
}
}
data$read_allele <- as.vector(y[,"selected_allele"])
## Emission probabilities
prob = Pr.y.given.z(y=y[,read,drop=F], p=p12, n=N, eps=y[,eps,drop=F], ploidy=ploidy, C=TRUE, dir=dirname(dollar0), chrom=contig, id=indiv)
colnames(prob) <- paste("Pr(y|", ancestries, ")")
data <- cbind(data, prob)
data$est <- apply(prob, 1, which.max)
## Posterior probability
hmm <- forwardback.ded(Pi=Pi, delta=phi, prob=prob)
#hmm <- forwardback.ded(Pi=Pi, delta=rep(1/K, K), prob=prob)
Pr.z.given.y <- exp(hmm$logalpha + hmm$logbeta - hmm$LL)
colnames(Pr.z.given.y) <- paste("Pr(", ancestries, "|y)")
data <- cbind(data, Pr.z.given.y)
attr(data, "badpos") <- badpos
dataa[[contig]] <- data
}
cat("Saving data...")
save(dataa, file=hmmdata.file)
cat("OK\n")
}
contigLengths <- contigLengths[plot.contigs,]
## Track the width of breakpoints
breakpoints <- {};
matchMismatch <- {}
cat("Plotting...")
plotfile <- file.path(outdir, indiv, paste(indiv, "hmmprob.pdf", sep="-"))
if(file.exists(plotfile)) { cat("plot already exists\n") ; next }
pdf(file=plotfile, width=7, height=1.5)
par(mar=c(2,2.5,0.5,0.5),bg="transparent",cex.main=.68,cex.lab=.8,font.lab=2,cex.axis=.38,mgp=c(1,.000001,0),xaxs="i")
plot(0, 0, xlab="", ylab="", col="transparent", xlim=c(1,sum(as.numeric(contigLengths$length)) + plotPadding*(length(plot.contigs)+1)), ylim=c(-1.01,1.01), axes=F)
axis(side=2,at=c(-1,0,1),labels=c("","",""),col="gray38");
mtext(c("par2","par1"),side=2,line=.68,at=c(-1,1),font=2,cex=.8,col=c("blue","red"),las=2);
box(col="gray68");
current_start <- plotPadding;
for(contig in plot.contigs) {
mtext(side=1,at=current_start,contig,font=2,cex=.8,line=1,xpd=T,adj=0)
current_end <- current_start + contigLengths[contigLengths$chr == contig,"length"] - 1;
if(sex == "male" && contig == "X") {
ploidy <- 1
ancestries <- c("par1","par2")
par1homo_col <- 1;
par2homo_col <- 2;
}
else {
ploidy <- 2
ancestries <- c("par1/par1","par1/par2","par2/par2")
par1homo_col <- 1;
par2homo_col <- 3;
}
if (sum(names(dataa) %in% contig)!=0) {
contig_data <- dataa[[contig]];
x <- contig_data$pos
y <- contig_data[,paste("Pr(", ancestries, "|y)")]
### divvy up homozygous and heterozygous blocks
byBlocks <- breakpoint.width(x, y[,par1homo_col], y[,par2homo_col], indiv=indiv, contig=contig, conf1=.05 ,conf2=.95);
if (is.null(byBlocks[["bps"]])==F) { breakpoints <- rbind(breakpoints,byBlocks[["bps"]]); }
### plot
like.par1 <- contig_data[contig_data$read_allele==contig_data$par1ref,]$pos;
like.par2 <- contig_data[contig_data$read_allele==contig_data$par2ref,]$pos;
plot.posterior(x+current_start, y, ancestries, like.par1+current_start, like.par2+current_start, bounds=c(1,contigLengths[contigLengths$chr==contig,]$length)+current_start-1, subtract=current_start, tickwidth=5*10^7)
### report mismatch fraction for homozygous regions
if (nrow(byBlocks[["blocks"]])>0) {
## plot fraction of par1/(par1+par2) among informative markers (between -1 and 1)
matchMismatch <- rbind(matchMismatch, reportCounts(contig_data, as.vector(byBlocks[["blocks"]][,"V1"]), as.vector(byBlocks[["blocks"]][,"V4"]), as.numeric(as.vector(byBlocks[["blocks"]][,"V7"])), as.numeric(as.vector(byBlocks[["blocks"]][,"V8"]))))
}
}
current_start <- current_start + contigLengths[contigLengths$chr == contig,"length"] + plotPadding;
if (contig != plot.contigs[length(plot.contigs)]) {
abline(v=current_start-(plotPadding/2),col="gray68",lwd=1)
}
}
etc <- ""
main <- sprintf("%s (%s): delta=(%.0e, %.0e)", indiv, sex, deltapar1, deltapar2)
dev.off()
cat("OK\n")
if (is.null(breakpoints)==F) {
write.table(breakpoints,file=file.path(outdir, indiv, paste(indiv, "breakpoints.csv", sep="-")),append=F,quote=F,na="NA",row.names=F,col.names=F,sep=",");
}
if (is.null(matchMismatch)==F) {
write.table(as.data.frame(matchMismatch),file=file.path(outdir, indiv, paste(indiv, "matchMismatch.csv", sep="-")),
append=F,quote=F,na="NA",row.names=F,col.names=T,sep=",");
}
}