Nothing
R/haplotype.R
In TitanCNA: Subclonal copy number and LOH prediction from whole genome sequencing of tumours
Defines functions
keepChr
plotHaplotypeFraction
getPhasedAllele
getHaplotypesFromVCF
getPhasedAlleleFraction
loadHaplotypeAlleleCounts
loadReadCountsFromBed
loadBXcountsFromBEDDir
Documented in
getHaplotypesFromVCF
loadBXcountsFromBEDDir
loadHaplotypeAlleleCounts
plotHaplotypeFraction
#' author: Gavin Ha
#' Dana-Farber Cancer Institute
#' Broad Institute
#' contact: <gavinha@gmail.com> or <gavinha@broadinstitute.org>
#' date: January 11, 2018
#' @import data.table
#' @import GenomicRanges
loadBXcountsFromBEDDir <- function(bxDir, chrs = c(1:22, "X", "Y"), minReads = 2){
files <- list.files(bxDir, pattern=".bed", full.names = TRUE)
message("Loading BX counts from ", bxDir)
bxAll <- NULL
for (i in files){
message(i)
awkcmd <- paste0("awk -F \"\t\" \'{if (!$4) {print $1\"\t\"$2\"\t\"$3\"\t\"\"NA\"} else {print $1\"\t\"$2\"\t\"$3\"\t\"$4}}\' ", i)
bxChr <- fread(awkcmd, header = FALSE, na.strings = c("NA"))
colnames(bxChr) <- c("chr", "start", "end", "BX")
bxChr[, BXcounts:=sapply(BX, function(x){
if (!is.na(x)){
bxCodes <- unlist(tstrsplit(x,","))
bxReads <- as.numeric(tstrsplit(bxCodes, "_")[[2]])
return(sum(bxReads >= minReads))
}else{
return(0)
}
})]
bxChr[, BX:=NULL]
bxAll <- rbind(bxAll, bxChr)
}
bxGR <- RangedData(space = bxAll[[1]], ranges = IRanges(start = bxAll[[2]],
end = bxAll[[3]]), BX.count = bxAll[[4]])
bxGR <- keepChr(bxGR, chr = chrs)
return(bxGR)
}
loadReadCountsFromBed <- function(counts, chrs = c(1:22, "X", "Y"), gc = NULL, map = NULL, centromere = NULL, flankLength = 100000, targetedSequences = NULL, genomeStyle = "NCBI"){
names(counts) <- setGenomeStyle(names(counts), genomeStyle)
counts <- keepChr(counts, chrs)
if (!is.null(gc)){
names(gc) <- setGenomeStyle(names(gc), genomeStyle)
gc <- keepChr(gc, chrs)
counts$gc <- gc$value
}
if (!is.null(map)){
names(map) <- setGenomeStyle(names(map), genomeStyle)
map <- keepChr(map, chrs)
counts$map <- map$value
}
colnames(counts)[1] <- c("reads")
# remove centromeres
if (!is.null(centromere)){
centromere$Chr <- setGenomeStyle(centromere$Chr, genomeStyle)
counts <- excludeCentromere(counts, centromere, flankLength = flankLength)
}
# keep targeted sequences
if (!is.null(targetedSequences)){
targetedSequences[,1] <- setGenomeStyle(targetedSequences[,1], genomeStyle)
countsExons <- filterByTargetedSequences(counts, targetedSequences)
counts <- counts[countsExons$ix,]
}
## filter 0 read bins ##
#counts <- counts[counts$reads > 0, ]
return(counts)
}
loadHaplotypeAlleleCounts <- function(inCounts, cnfile, fun = "sum", haplotypeBinSize = 1e5,
minSNPsInBin = 3, chrs = c(1:22, "X"), minNormQual = 200,
genomeStyle = "NCBI", sep = "\t", header = TRUE, seqinfo = NULL,
mapWig = NULL, mapThres = 0.9, centromere = NULL, minDepth = 10, maxDepth = 1000) {
if (is.character(inCounts)){
## LOAD INPUT READ COUNT DATA
message("titan: Loading data and phasing information ", inCounts)
data <- read.delim(inCounts, header = header, stringsAsFactors = FALSE,
sep = sep)
colnames(data) <- c("chr", "posn", "refBase", "ref", "nonRefBase", "nonRef", "normQual", "genotype", "phaseSet")
if (typeof(data[,"posn"])!="integer" || typeof(data[,"ref"])!="integer" ||
typeof(data[,"nonRef"])!="integer" || is.null(data$genotype) || is.null(data$phaseSet)){
stop("loadHaplotypeAlleleCounts: Input counts file format does not match required specifications.")
}
}else if (is.data.frame(inCounts)){ #inCounts is a data.frame
data <- inCounts
}else{
stop("loadHaplotypeAlleleCounts: Must provide a filename or data.frame to inCounts")
}
if (is.null(seqinfo)){
seqinfo <- readRDS(system.file("extdata", "Seqinfo_hg19.rds", package = "TitanCNA"))
}
seqinfo <- keepStandardChromosomes(seqinfo)
seqlevelsStyle(chrs) <- genomeStyle
# convert to desired genomeStyle and only include autosomes, sex chromosomes
data[, 1] <- setGenomeStyle(data[, 1], genomeStyle)
## sort chromosomes
indChr <- orderSeqlevels(as.character(data[, "chr"]), X.is.sexchrom = TRUE)
data <- data[indChr, ]
## sort positions within each chr
for (x in unique(data[, "chr"])){
ind <- which(data[, "chr"] == x)
data[ind, ] <- data[ind[order(data[ind, "posn"], decreasing = FALSE)], ]
}
## filter data ##
data <- cbind(data, start = data$posn, end = data$posn, depth = data$ref + data$nonRef)
data <- data[data$normQual >= minNormQual, ]
# get max of allele counts
#data$refOriginal <- data$ref
#data$ref <- pmax(data$refOriginal, data$nonRef)
## use GRanges to find overlap of tiling haplotypeBinSize ##
phasedAlleles <- getPhasedAlleleFraction(data)
data$phasedAlleleFraction <- phasedAlleles$allele.fraction
data$phasedCount <- phasedAlleles$phasedCount
data$ref.symmetric <- pmax(data$ref, data$nonRef)
data.gr <- makeGRangesFromDataFrame(data, keep.extra.columns = TRUE, seqinfo = seqinfo, ignore.strand = TRUE)
tile.gr <- unlist(tileGenome(seqinfo, tilewidth = haplotypeBinSize))
data.gr <- keepSeqlevels(data.gr, chrs, pruning.mode="coarse")
tile.gr <- keepSeqlevels(tile.gr, chrs, pruning.mode="coarse")
hits <- findOverlaps(query = data.gr, subject = tile.gr)
data.gr$haplotypeBin <- subjectHits(hits)
## use data.table to process haplotype counts by blocks
data <- as(data.gr, "data.frame")
data.dt <- data.table(as(data.gr, "data.frame"))
haploBinSummary <- data.dt[, list(HaplotypeFraction = mean(phasedAlleleFraction),
HaplotypeDepth.sum = sum(phasedCount), HaplotypeBinDepth.sum = sum(depth),
HaplotypeDepth.mean = round(mean(phasedCount)), #round?
#HaplotypeDepth.mean = round(mean(ref.symmetric)),
HaplotypeBinDepth.mean = round(mean(depth)), #round?
SNPs = length(phasedAlleleFraction)), by = c("phaseSet", "haplotypeBin")]
# filter bins by number of SNPs #
haploBinSummary <- haploBinSummary[SNPs >= minSNPsInBin]
# summary bins with multiple phaseset ID such that haplotypeBin is unique
haploBinSummary.unique <- haploBinSummary[, list(SNPs = sum(SNPs),
HaplotypeFraction = sum(HaplotypeFraction * SNPs) / sum(SNPs),
HaplotypeDepth.sum = sum(HaplotypeDepth.sum),
#HaplotypeDepth.sum.symmetric = sum(HaplotypeDepth.sum.symmetric),
HaplotypeBinDepth.sum = sum(HaplotypeBinDepth.sum),
HaplotypeDepth.mean = round(sum(HaplotypeDepth.mean * SNPs) / sum(SNPs)), #round?
#HaplotypeDepth.mean.symmetric = round(sum(HaplotypeDepth.mean.symmetric * SNPs) / sum(SNPs)),
HaplotypeBinDepth.mean = round(sum(HaplotypeBinDepth.mean * SNPs) / sum(SNPs)), #round?
phaseSet = phaseSet[which.max(SNPs)]), by = haplotypeBin]
# get symmetric haplotype fraction
haploBinSummary.unique[, HaplotypeFraction.symmetric := pmax(HaplotypeFraction, 1 - HaplotypeFraction)]
haploBinSummary.unique[, HaplotypeDepth.sum.symmetric := pmax(HaplotypeDepth.sum, HaplotypeBinDepth.sum - HaplotypeDepth.sum)]
haploBinSummary.unique[, HaplotypeDepth.mean.symmetric := pmax(HaplotypeDepth.mean, HaplotypeBinDepth.mean - HaplotypeDepth.mean)]
# set bin column as key so that we can map back to original data
setkey(haploBinSummary.unique, haplotypeBin)
# add the bin summarized values back to data.dt
data.dt <- cbind(data.dt, select(haploBinSummary.unique[.(subjectHits(hits))],
haplotypeBin.aggr = haplotypeBin, HaplotypeFraction.symmetric, HaplotypeDepth.sum,
HaplotypeDepth.sum.symmetric, HaplotypeBinDepth.sum, HaplotypeDepth.mean,
HaplotypeDepth.mean.symmetric, HaplotypeBinDepth.mean,
SNPs, phaseSet.aggr = phaseSet))
data.dt <- na.omit(data.dt)
data.dt[, phasedCount.haploSymmetric := {
if (HaplotypeDepth.sum != HaplotypeDepth.sum.symmetric){
depth - phasedCount
}else{
phasedCount
}
}, by=1:nrow(data.dt)]
alleleData <- select(data.dt, chr=seqnames, posn=start,
refOriginal=ref, nonRef=nonRef, tumDepth=depth)
alleleData$chr <- as.character(alleleData$chr)
alleleData$ref = pmax(alleleData$refOriginal, alleleData$nonRef)
haplotypeData <- select(data.dt, chr=seqnames, posn=start,
phaseSet=phaseSet.aggr, refOriginal=ref, tumDepthOriginal = depth)
haplotypeData$chr <- as.character(haplotypeData$chr)
if (fun == "sum"){
haplotypeData$ref <- data.dt[, HaplotypeDepth.sum.symmetric]
haplotypeData$tumDepth <- data.dt[, HaplotypeBinDepth.sum]
haplotypeData[, HaplotypeRatio := ref / tumDepth]
#haplotypeData$haplotypeCount <- data.dt[, phasedCount]#data.dt[, HaplotypeDepth.sum]
haplotypeData$haplotypeCount <- data.dt[, phasedCount.haploSymmetric]
}else if (fun == "mean"){
haplotypeData$ref <- data.dt[, HaplotypeDepth.mean.symmetric]
haplotypeData$tumDepth <- data.dt[, HaplotypeBinDepth.mean]
haplotypeData$HaplotypeRatio <- data.dt[, HaplotypeDepth.sum.symmetric] / data.dt[, HaplotypeBinDepth.sum]
#haplotypeData[, HaplotypeRatio := ref / tumDepth]
#haplotypeData$haplotypeCount <- data.dt[, phasedCount]#data.dt[, HaplotypeDepth.mean]
haplotypeData$haplotypeCount <- data.dt[, phasedCount.haploSymmetric]
}else if (fun == "SNP"){
haplotypeData$ref <- data.dt[, phasedCount.haploSymmetric]
haplotypeData$tumDepth <- data.dt[, depth]
haplotypeData$HaplotypeRatio <- data.dt[, HaplotypeDepth.sum.symmetric] / data.dt[, HaplotypeBinDepth.sum]
haplotypeData$haplotypeCount <- data.dt[, phasedCount.haploSymmetric]
}
haplotypeData$nonRef <- haplotypeData$tumDepth - haplotypeData$ref
## filtering ##
if (!is.null(centromere)){
centromere <- read.delim(centromere,header=T,stringsAsFactors=F,sep="\t")
}
#### LOAD GC AND MAPPABILITY CORRECTED COVERAGE LOG RATIO FILE ####
message('titan: Loading GC content and mappability corrected log2 ratios...')
cnData <- fread(cnfile)
cnData$chr <- setGenomeStyle(cnData$chr, genomeStyle = genomeStyle)
#### ADD CORRECTED LOG RATIOS TO DATA OBJECT ####
message('titan: Extracting read depth...')
logR <- getPositionOverlap(haplotypeData$chr,haplotypeData$posn, cnData)
haplotypeData$logR <- log(2^logR)
rm(logR,cnData)
#### FILTER DATA FOR DEPTH, MAPPABILITY, NA, etc ####
if (!is.null(mapWig)){
mScore <- as.data.frame(wigToRangedData(mapWig))
mScore <- getPositionOverlap(haplotypeData$chr,haplotypeData$posn,mScore[,-4])
haplotypeData <- filterData(haplotypeData,chrs, minDepth=minDepth, maxDepth=maxDepth,
map=mScore,mapThres=mapThres, centromeres = centromere)
rm(mScore)
}else{
haplotypeData <- filterData(haplotypeData,chrs,minDepth=minDepth,maxDepth=maxDepth,centromeres = centromere)
}
return(list(haplotypeData=haplotypeData, alleleData=alleleData, data=data.dt))
}
getPhasedAlleleFraction <- function(x){
altInd <- as.logical(as.numeric(tstrsplit(x$genotype, "\\||\\/")[[1]]))
refInd <- !altInd
allele.fraction <- rep(NA, length(x))
allele.fraction[refInd] <- x[refInd, "ref"] / x[refInd, "depth"]
allele.fraction[altInd] <- x[altInd, "nonRef"] / x[altInd, "depth"]
phasedCount <- rep(NA, length(x))
phasedCount[refInd] <- x[refInd, "ref"]
phasedCount[altInd] <- x[altInd, "nonRef"]
return(list(allele.fraction = allele.fraction, phasedCount = phasedCount))
}
getHaplotypesFromVCF <- function(vcfFile, chrs = c(1:22, "X"), build = "hg19", genomeStyle = "NCBI",
filterFlags = c("PASS", "10X_RESCUED_MOLECULE_HIGH_DIVERSITY"),
minQUAL = 100, minDepth = 10, minVAF = 0.25, altCountField = "AD",
keepGenotypes = c("1|0", "0|1", "0/1"), snpDB = NULL){
#require(data.table)
message("Loading ", vcfFile)
vcf <- readVcf(vcfFile, genome = build)
chrName <- mapSeqlevels(seqlevels(vcf), style = genomeStyle)
rowRanges(vcf) <- renameSeqlevels(rowRanges(vcf), na.omit(chrName))
#keepGenotypes = c("1|0", "0|1", "0/1")
## filter vcf ##
message("Filtering VCF ...")
message(" by chromsomes")
# keep specified chromosomes
seqlevelsStyle(chrs) <- genomeStyle
vcf <- keepSeqlevels(vcf, chrs, pruning.mode="coarse")
# keep by filter flags
indFILTER <- rowRanges(vcf)$FILTER %in% filterFlags
# keep SNPs - ref and alt have length of 1 and only a single allele for ref/alt
indSNP <- nchar(unstrsplit(CharacterList(rowRanges(vcf)$ALT), sep=",")) == 1 &
nchar(unstrsplit(rowRanges(vcf)$REF, sep=",")) == 1
message(" by quality >=", minQUAL, ")")
indQUAL <- rowRanges(vcf)$QUAL >= minQUAL
# keep genotypes
message(" by genotypes: ", paste0(keepGenotypes, collapse=";"))
indGeno <- geno(vcf)$GT %in% keepGenotypes
ind <- indFILTER & indSNP & indQUAL & indGeno
vcf <- vcf[which(ind)]
message(" by depth (>=", minDepth, ")")
depth <- geno(vcf)$DP
indDP <- depth >= minDepth
message(" by VAF (>=", minVAF, ")")
minVAF <- min(minVAF, 1 - minVAF) # symmetric from 0-0.5
if (!is.null(geno(vcf)[[altCountField]])){
altCounts <- unlist(lapply(geno(vcf)[[altCountField]], min))
}else{
stop("Alternate read counts not in AD or AO fields.")
}
indVAR <- (altCounts / depth) >= minVAF
vcf <- vcf[which(indDP & indVAR)]
rm(depth)
if (!is.null(snpDB)){
message (" by SNP VCF file ", snpDB)
snp <- readVcf(snpDB, genome = build)
snpInd <- nchar(unstrsplit(CharacterList(rowRanges(snp)$ALT), sep=",")) == 1 &
nchar(unstrsplit(rowRanges(snp)$REF, sep=",")) == 1
snp <- snp[which(snpInd)]
hits <- findOverlaps(query = rowRanges(vcf), subject = rowRanges(snp))
indSNPDB <- queryHits(hits)
vcf <- vcf[indSNPDB]
}
##
#acounts <- do.call(rbind, geno(vcf)$AD)
# phased snps
#indPhased <- grepl(pattern = "\\|", geno(vcf)$GT)
# phase sets
#ps.rle <- rle(as.integer(geno(vcf)$PS))
geno.gr <- rowRanges(vcf)
values(geno.gr) <- cbind(values(geno.gr),
DataFrame(GT = as.character(geno(vcf)$GT), PS = as.character(geno(vcf)$PS)))
HT <- getPhasedAllele(geno.gr)
values(geno.gr) <- cbind(values(geno.gr), DataFrame(HT1 = HT$h1, HT2 = HT$h2))
geno.gr <- keepSeqlevels(geno.gr, chrs, pruning.mode="coarse")
return(list(vcf.filtered = vcf, geno = geno.gr))
}
# x GRanges object with GT, REF, ALT metadata columns
# returns reference or alternate allele in haplotype
getPhasedAllele <- function(x){
h1 <- rep(NA, length(x))
h2 <- rep(NA, length(x))
# haplotype 1 (h1) ref allele for 1|0
h1[x$GT == "1|0"] <- as.character(x$REF)[x$GT == "1|0"]
h2[x$GT == "1|0"] <- as.character(unlist(x$ALT))[x$GT == "1|0"]
# haplotype 1 (h1) alt allele for 0|1
h1[x$GT == "0|1"] <- as.character(unlist(x$ALT))[x$GT == "0|1"]
h2[x$GT == "0|1"] <- as.character(x$REF)[x$GT == "0|1"]
return(list(h1 = h1, h2 = h2))
}
plotHaplotypeFraction <- function(dataIn, chr = c(1:22), resultType = "HaplotypeRatio", colType = "Haplotypes",
phaseBlockCol = c("#9ad0f3", "#CC79A7"), geneAnnot = NULL, spacing = 4, xlim = NULL, ...) {
if (!resultType %in% c("HaplotypeRatio", "AllelicRatio")){
stop("plotHaplotypeFraction: resultType must be one of 'HaplotypeRatio' or 'AllelicRatio'.")
}
if (!colType %in% c("Haplotypes", "CopyNumber")){
stop("plotHaplotypeFraction: plotType must be one of 'Haplotypes' or 'CopyNumber'")
}
# use consistent chromosome naming convention
chr <- as.character(chr)
seqlevelsStyle(chr) <- seqlevelsStyle(as.character(dataIn$Chr))[1]
lohCol.hap <- c(`0`=phaseBlockCol[1], `1`=phaseBlockCol[2])
lohCol.titan <- c("#00FF00", "#006400", "#0000FF", "#8B0000",
"#006400", "#BEBEBE", "#FF0000", "#BEBEBE",
"#FF0000")
names(lohCol.titan) <- c("HOMD", "DLOH", "NLOH", "GAIN",
"ALOH", "HET", "ASCNA", "BCNA", "UBCNA")
dataIn <- copy(dataIn)
colnames(dataIn)[1:2] <- c("Chr", "Position")
#dataIn$AllelicRatio <- pmax(dataIn$refOriginal, dataIn$tumDepthOriginal - dataIn$refOriginal) / dataIn$tumDepthOriginal
## set alternating 0 and 1 for phaseSets
psRuns <- rle(dataIn$PhaseSet)
ps.id <- 1:length(psRuns$lengths) %% 2
dataIn$phaseSet.id <- rep(ps.id, psRuns$lengths)
#dataIn$HaplotypeCount[as.logical(dataIn$phaseSet.id)] <- dataIn$HaplotypeDepth[as.logical(dataIn$phaseSet.id)] - dataIn$HaplotypeCount[as.logical(dataIn$phaseSet.id)]
#dataIn$HaplotypeRatio[as.logical(dataIn$phaseSet.id)] <- 1 - dataIn$HaplotypeRatio[as.logical(dataIn$phaseSet.id)]
#dataIn$AllelicRatio <- dataIn$refOriginal / dataIn$tumDepthOriginal
dataIn[, HaplotypeRatio.1 := HaplotypeRatio]#dataIn$HaplotypeCount / dataIn$HaplotypeDepth
dataIn[, HaplotypeRatio.2 := 1 - HaplotypeRatio]#(dataIn$HaplotypeDepth - dataIn$HaplotypeCount) / dataIn$HaplotypeDepth
if (!is.null(chr) && length(chr) == 1) {
for (i in chr) {
dataByChr <- dataIn[Chr == i, ]
#dataByChr <- dataByChr[dataByChr[, "TITANcall"] != "OUT", ]
# plot the data if (outfile!=''){
# pdf(outfile,width=10,height=6) }
if (colType == "Haplotypes" || !"TITANcall" %in% colnames(dataByChr)){
colors.1 <- lohCol.hap[as.character(dataByChr$phaseSet.id)]
colors.2 <- lohCol.hap[as.character(as.numeric(!dataByChr$phaseSet.id))]
}else{
colors.1 <- lohCol.titan[dataByChr[, TITANcall]]
colors.2 <- colors.1
}
par(mar = c(spacing, 8, 2, 2))
# par(xpd=NA)
if (missing(xlim)) {
xlim <- as.numeric(c(1, dataByChr[nrow(dataByChr), Position]))
}
if (resultType == "HaplotypeRatio"){
plot(dataByChr[, Position], dataByChr[, HaplotypeRatio.1],
col = colors.1,
pch = 16, xaxt = "n", las = 1, ylab = "Haplotype Fraction", xlim = xlim,
...)
points(dataByChr[, Position], dataByChr[, HaplotypeRatio.2], col = colors.1, pch=16, ...)
}else if (resultType == "AllelicRatio"){
plot(dataByChr[, Position], dataByChr[, AllelicRatio],
col = colors.1,
pch = 16, xaxt = "n", las = 1, ylab = "Allelic Fraction", xlim = xlim,
...)
}else{
stop("Need to specify \"resultType\": HaplotypeRatio or AllelicRatio")
}
lines(as.numeric(c(1, dataByChr[nrow(dataByChr), Position])), rep(0.5, 2), type = "l",
col = "grey", lwd = 3)
if (!is.null(geneAnnot)) {
plotGeneAnnotation(geneAnnot, i)
}
}
} else {
if (colType == "Haplotypes" || !"TITANcall" %in% colnames(dataIn)){
colors.1 <- lohCol.hap[as.character(dataIn$phaseSet.id)]
colors.2 <- lohCol.hap[as.character(as.numeric(!dataIn$phaseSet.id))]
}else{
colors.1 <- lohCol.titan[dataIn[, TITANcall]]
colors.2 <- colors.1
}
# plot for all chromosomes specified
dataIn <- dataIn[Chr %in% chr]
coord <- getGenomeWidePositions(dataIn[, Chr], dataIn[, Position])
if (resultType == "HaplotypeRatio"){
plot(coord$posns, as.numeric(dataIn[, HaplotypeRatio.1]),
col = colors.1, pch = 16,
xaxt = "n", bty = "n", las = 1, ylab = "Haplotype Fraction", ...)
points(coord$posns, dataIn[, HaplotypeRatio.2], col = colors.1, pch=16, ...)
}else if (resultType == "AllelicRatio"){
plot(coord$posns, as.numeric(dataIn[, AllelicRatio]),
col = colors.2, pch = 16,
xaxt = "n", bty = "n", las = 1, ylab = "Allelic Fraction", ...)
}else{
stop("Need to specify \"resultType\": HaplotypeRatio or AllelicRatio")
}
lines(as.numeric(c(1, coord$posns[length(coord$posns)])),
rep(0.5, 2), type = "l", col = "grey",
lwd = 3)
plotChrLines(unique(dataIn[, Chr]), coord$chrBkpt,
c(-0.1, 1.1))
}
}
keepChr <- function(tumour_reads, chr = c(1:22,"X","Y")){
tumour_reads <- tumour_reads[space(tumour_reads) %in% chr, ]
tumour_reads <- as.data.frame(tumour_reads)
tumour_reads$space <- droplevels(tumour_reads$space)
tumour_reads$space <- factor(tumour_reads$space,levels=chr)
tumour_reads <- as(tumour_reads,"RangedData")
return(tumour_reads)
}
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Defines functions keepChr plotHaplotypeFraction getPhasedAllele getHaplotypesFromVCF getPhasedAlleleFraction loadHaplotypeAlleleCounts loadReadCountsFromBed loadBXcountsFromBEDDir
Documented in getHaplotypesFromVCF loadBXcountsFromBEDDir loadHaplotypeAlleleCounts plotHaplotypeFraction
#' author: Gavin Ha
#' Dana-Farber Cancer Institute
#' Broad Institute
#' contact: <gavinha@gmail.com> or <gavinha@broadinstitute.org>
#' date: January 11, 2018
#' @import data.table
#' @import GenomicRanges
loadBXcountsFromBEDDir <- function(bxDir, chrs = c(1:22, "X", "Y"), minReads = 2){
files <- list.files(bxDir, pattern=".bed", full.names = TRUE)
message("Loading BX counts from ", bxDir)
bxAll <- NULL
for (i in files){
message(i)
awkcmd <- paste0("awk -F \"\t\" \'{if (!$4) {print $1\"\t\"$2\"\t\"$3\"\t\"\"NA\"} else {print $1\"\t\"$2\"\t\"$3\"\t\"$4}}\' ", i)
bxChr <- fread(awkcmd, header = FALSE, na.strings = c("NA"))
colnames(bxChr) <- c("chr", "start", "end", "BX")
bxChr[, BXcounts:=sapply(BX, function(x){
if (!is.na(x)){
bxCodes <- unlist(tstrsplit(x,","))
bxReads <- as.numeric(tstrsplit(bxCodes, "_")[[2]])
return(sum(bxReads >= minReads))
}else{
return(0)
}
})]
bxChr[, BX:=NULL]
bxAll <- rbind(bxAll, bxChr)
}
bxGR <- RangedData(space = bxAll[[1]], ranges = IRanges(start = bxAll[[2]],
end = bxAll[[3]]), BX.count = bxAll[[4]])
bxGR <- keepChr(bxGR, chr = chrs)
return(bxGR)
}
loadReadCountsFromBed <- function(counts, chrs = c(1:22, "X", "Y"), gc = NULL, map = NULL, centromere = NULL, flankLength = 100000, targetedSequences = NULL, genomeStyle = "NCBI"){
names(counts) <- setGenomeStyle(names(counts), genomeStyle)
counts <- keepChr(counts, chrs)
if (!is.null(gc)){
names(gc) <- setGenomeStyle(names(gc), genomeStyle)
gc <- keepChr(gc, chrs)
counts$gc <- gc$value
}
if (!is.null(map)){
names(map) <- setGenomeStyle(names(map), genomeStyle)
map <- keepChr(map, chrs)
counts$map <- map$value
}
colnames(counts)[1] <- c("reads")
# remove centromeres
if (!is.null(centromere)){
centromere$Chr <- setGenomeStyle(centromere$Chr, genomeStyle)
counts <- excludeCentromere(counts, centromere, flankLength = flankLength)
}
# keep targeted sequences
if (!is.null(targetedSequences)){
targetedSequences[,1] <- setGenomeStyle(targetedSequences[,1], genomeStyle)
countsExons <- filterByTargetedSequences(counts, targetedSequences)
counts <- counts[countsExons$ix,]
}
## filter 0 read bins ##
#counts <- counts[counts$reads > 0, ]
return(counts)
}
loadHaplotypeAlleleCounts <- function(inCounts, cnfile, fun = "sum", haplotypeBinSize = 1e5,
minSNPsInBin = 3, chrs = c(1:22, "X"), minNormQual = 200,
genomeStyle = "NCBI", sep = "\t", header = TRUE, seqinfo = NULL,
mapWig = NULL, mapThres = 0.9, centromere = NULL, minDepth = 10, maxDepth = 1000) {
if (is.character(inCounts)){
## LOAD INPUT READ COUNT DATA
message("titan: Loading data and phasing information ", inCounts)
data <- read.delim(inCounts, header = header, stringsAsFactors = FALSE,
sep = sep)
colnames(data) <- c("chr", "posn", "refBase", "ref", "nonRefBase", "nonRef", "normQual", "genotype", "phaseSet")
if (typeof(data[,"posn"])!="integer" || typeof(data[,"ref"])!="integer" ||
typeof(data[,"nonRef"])!="integer" || is.null(data$genotype) || is.null(data$phaseSet)){
stop("loadHaplotypeAlleleCounts: Input counts file format does not match required specifications.")
}
}else if (is.data.frame(inCounts)){ #inCounts is a data.frame
data <- inCounts
}else{
stop("loadHaplotypeAlleleCounts: Must provide a filename or data.frame to inCounts")
}
if (is.null(seqinfo)){
seqinfo <- readRDS(system.file("extdata", "Seqinfo_hg19.rds", package = "TitanCNA"))
}
seqinfo <- keepStandardChromosomes(seqinfo)
seqlevelsStyle(chrs) <- genomeStyle
# convert to desired genomeStyle and only include autosomes, sex chromosomes
data[, 1] <- setGenomeStyle(data[, 1], genomeStyle)
## sort chromosomes
indChr <- orderSeqlevels(as.character(data[, "chr"]), X.is.sexchrom = TRUE)
data <- data[indChr, ]
## sort positions within each chr
for (x in unique(data[, "chr"])){
ind <- which(data[, "chr"] == x)
data[ind, ] <- data[ind[order(data[ind, "posn"], decreasing = FALSE)], ]
}
## filter data ##
data <- cbind(data, start = data$posn, end = data$posn, depth = data$ref + data$nonRef)
data <- data[data$normQual >= minNormQual, ]
# get max of allele counts
#data$refOriginal <- data$ref
#data$ref <- pmax(data$refOriginal, data$nonRef)
## use GRanges to find overlap of tiling haplotypeBinSize ##
phasedAlleles <- getPhasedAlleleFraction(data)
data$phasedAlleleFraction <- phasedAlleles$allele.fraction
data$phasedCount <- phasedAlleles$phasedCount
data$ref.symmetric <- pmax(data$ref, data$nonRef)
data.gr <- makeGRangesFromDataFrame(data, keep.extra.columns = TRUE, seqinfo = seqinfo, ignore.strand = TRUE)
tile.gr <- unlist(tileGenome(seqinfo, tilewidth = haplotypeBinSize))
data.gr <- keepSeqlevels(data.gr, chrs, pruning.mode="coarse")
tile.gr <- keepSeqlevels(tile.gr, chrs, pruning.mode="coarse")
hits <- findOverlaps(query = data.gr, subject = tile.gr)
data.gr$haplotypeBin <- subjectHits(hits)
## use data.table to process haplotype counts by blocks
data <- as(data.gr, "data.frame")
data.dt <- data.table(as(data.gr, "data.frame"))
haploBinSummary <- data.dt[, list(HaplotypeFraction = mean(phasedAlleleFraction),
HaplotypeDepth.sum = sum(phasedCount), HaplotypeBinDepth.sum = sum(depth),
HaplotypeDepth.mean = round(mean(phasedCount)), #round?
#HaplotypeDepth.mean = round(mean(ref.symmetric)),
HaplotypeBinDepth.mean = round(mean(depth)), #round?
SNPs = length(phasedAlleleFraction)), by = c("phaseSet", "haplotypeBin")]
# filter bins by number of SNPs #
haploBinSummary <- haploBinSummary[SNPs >= minSNPsInBin]
# summary bins with multiple phaseset ID such that haplotypeBin is unique
haploBinSummary.unique <- haploBinSummary[, list(SNPs = sum(SNPs),
HaplotypeFraction = sum(HaplotypeFraction * SNPs) / sum(SNPs),
HaplotypeDepth.sum = sum(HaplotypeDepth.sum),
#HaplotypeDepth.sum.symmetric = sum(HaplotypeDepth.sum.symmetric),
HaplotypeBinDepth.sum = sum(HaplotypeBinDepth.sum),
HaplotypeDepth.mean = round(sum(HaplotypeDepth.mean * SNPs) / sum(SNPs)), #round?
#HaplotypeDepth.mean.symmetric = round(sum(HaplotypeDepth.mean.symmetric * SNPs) / sum(SNPs)),
HaplotypeBinDepth.mean = round(sum(HaplotypeBinDepth.mean * SNPs) / sum(SNPs)), #round?
phaseSet = phaseSet[which.max(SNPs)]), by = haplotypeBin]
# get symmetric haplotype fraction
haploBinSummary.unique[, HaplotypeFraction.symmetric := pmax(HaplotypeFraction, 1 - HaplotypeFraction)]
haploBinSummary.unique[, HaplotypeDepth.sum.symmetric := pmax(HaplotypeDepth.sum, HaplotypeBinDepth.sum - HaplotypeDepth.sum)]
haploBinSummary.unique[, HaplotypeDepth.mean.symmetric := pmax(HaplotypeDepth.mean, HaplotypeBinDepth.mean - HaplotypeDepth.mean)]
# set bin column as key so that we can map back to original data
setkey(haploBinSummary.unique, haplotypeBin)
# add the bin summarized values back to data.dt
data.dt <- cbind(data.dt, select(haploBinSummary.unique[.(subjectHits(hits))],
haplotypeBin.aggr = haplotypeBin, HaplotypeFraction.symmetric, HaplotypeDepth.sum,
HaplotypeDepth.sum.symmetric, HaplotypeBinDepth.sum, HaplotypeDepth.mean,
HaplotypeDepth.mean.symmetric, HaplotypeBinDepth.mean,
SNPs, phaseSet.aggr = phaseSet))
data.dt <- na.omit(data.dt)
data.dt[, phasedCount.haploSymmetric := {
if (HaplotypeDepth.sum != HaplotypeDepth.sum.symmetric){
depth - phasedCount
}else{
phasedCount
}
}, by=1:nrow(data.dt)]
alleleData <- select(data.dt, chr=seqnames, posn=start,
refOriginal=ref, nonRef=nonRef, tumDepth=depth)
alleleData$chr <- as.character(alleleData$chr)
alleleData$ref = pmax(alleleData$refOriginal, alleleData$nonRef)
haplotypeData <- select(data.dt, chr=seqnames, posn=start,
phaseSet=phaseSet.aggr, refOriginal=ref, tumDepthOriginal = depth)
haplotypeData$chr <- as.character(haplotypeData$chr)
if (fun == "sum"){
haplotypeData$ref <- data.dt[, HaplotypeDepth.sum.symmetric]
haplotypeData$tumDepth <- data.dt[, HaplotypeBinDepth.sum]
haplotypeData[, HaplotypeRatio := ref / tumDepth]
#haplotypeData$haplotypeCount <- data.dt[, phasedCount]#data.dt[, HaplotypeDepth.sum]
haplotypeData$haplotypeCount <- data.dt[, phasedCount.haploSymmetric]
}else if (fun == "mean"){
haplotypeData$ref <- data.dt[, HaplotypeDepth.mean.symmetric]
haplotypeData$tumDepth <- data.dt[, HaplotypeBinDepth.mean]
haplotypeData$HaplotypeRatio <- data.dt[, HaplotypeDepth.sum.symmetric] / data.dt[, HaplotypeBinDepth.sum]
#haplotypeData[, HaplotypeRatio := ref / tumDepth]
#haplotypeData$haplotypeCount <- data.dt[, phasedCount]#data.dt[, HaplotypeDepth.mean]
haplotypeData$haplotypeCount <- data.dt[, phasedCount.haploSymmetric]
}else if (fun == "SNP"){
haplotypeData$ref <- data.dt[, phasedCount.haploSymmetric]
haplotypeData$tumDepth <- data.dt[, depth]
haplotypeData$HaplotypeRatio <- data.dt[, HaplotypeDepth.sum.symmetric] / data.dt[, HaplotypeBinDepth.sum]
haplotypeData$haplotypeCount <- data.dt[, phasedCount.haploSymmetric]
}
haplotypeData$nonRef <- haplotypeData$tumDepth - haplotypeData$ref
## filtering ##
if (!is.null(centromere)){
centromere <- read.delim(centromere,header=T,stringsAsFactors=F,sep="\t")
}
#### LOAD GC AND MAPPABILITY CORRECTED COVERAGE LOG RATIO FILE ####
message('titan: Loading GC content and mappability corrected log2 ratios...')
cnData <- fread(cnfile)
cnData$chr <- setGenomeStyle(cnData$chr, genomeStyle = genomeStyle)
#### ADD CORRECTED LOG RATIOS TO DATA OBJECT ####
message('titan: Extracting read depth...')
logR <- getPositionOverlap(haplotypeData$chr,haplotypeData$posn, cnData)
haplotypeData$logR <- log(2^logR)
rm(logR,cnData)
#### FILTER DATA FOR DEPTH, MAPPABILITY, NA, etc ####
if (!is.null(mapWig)){
mScore <- as.data.frame(wigToRangedData(mapWig))
mScore <- getPositionOverlap(haplotypeData$chr,haplotypeData$posn,mScore[,-4])
haplotypeData <- filterData(haplotypeData,chrs, minDepth=minDepth, maxDepth=maxDepth,
map=mScore,mapThres=mapThres, centromeres = centromere)
rm(mScore)
}else{
haplotypeData <- filterData(haplotypeData,chrs,minDepth=minDepth,maxDepth=maxDepth,centromeres = centromere)
}
return(list(haplotypeData=haplotypeData, alleleData=alleleData, data=data.dt))
}
getPhasedAlleleFraction <- function(x){
altInd <- as.logical(as.numeric(tstrsplit(x$genotype, "\\||\\/")[[1]]))
refInd <- !altInd
allele.fraction <- rep(NA, length(x))
allele.fraction[refInd] <- x[refInd, "ref"] / x[refInd, "depth"]
allele.fraction[altInd] <- x[altInd, "nonRef"] / x[altInd, "depth"]
phasedCount <- rep(NA, length(x))
phasedCount[refInd] <- x[refInd, "ref"]
phasedCount[altInd] <- x[altInd, "nonRef"]
return(list(allele.fraction = allele.fraction, phasedCount = phasedCount))
}
getHaplotypesFromVCF <- function(vcfFile, chrs = c(1:22, "X"), build = "hg19", genomeStyle = "NCBI",
filterFlags = c("PASS", "10X_RESCUED_MOLECULE_HIGH_DIVERSITY"),
minQUAL = 100, minDepth = 10, minVAF = 0.25, altCountField = "AD",
keepGenotypes = c("1|0", "0|1", "0/1"), snpDB = NULL){
#require(data.table)
message("Loading ", vcfFile)
vcf <- readVcf(vcfFile, genome = build)
chrName <- mapSeqlevels(seqlevels(vcf), style = genomeStyle)
rowRanges(vcf) <- renameSeqlevels(rowRanges(vcf), na.omit(chrName))
#keepGenotypes = c("1|0", "0|1", "0/1")
## filter vcf ##
message("Filtering VCF ...")
message(" by chromsomes")
# keep specified chromosomes
seqlevelsStyle(chrs) <- genomeStyle
vcf <- keepSeqlevels(vcf, chrs, pruning.mode="coarse")
# keep by filter flags
indFILTER <- rowRanges(vcf)$FILTER %in% filterFlags
# keep SNPs - ref and alt have length of 1 and only a single allele for ref/alt
indSNP <- nchar(unstrsplit(CharacterList(rowRanges(vcf)$ALT), sep=",")) == 1 &
nchar(unstrsplit(rowRanges(vcf)$REF, sep=",")) == 1
message(" by quality >=", minQUAL, ")")
indQUAL <- rowRanges(vcf)$QUAL >= minQUAL
# keep genotypes
message(" by genotypes: ", paste0(keepGenotypes, collapse=";"))
indGeno <- geno(vcf)$GT %in% keepGenotypes
ind <- indFILTER & indSNP & indQUAL & indGeno
vcf <- vcf[which(ind)]
message(" by depth (>=", minDepth, ")")
depth <- geno(vcf)$DP
indDP <- depth >= minDepth
message(" by VAF (>=", minVAF, ")")
minVAF <- min(minVAF, 1 - minVAF) # symmetric from 0-0.5
if (!is.null(geno(vcf)[[altCountField]])){
altCounts <- unlist(lapply(geno(vcf)[[altCountField]], min))
}else{
stop("Alternate read counts not in AD or AO fields.")
}
indVAR <- (altCounts / depth) >= minVAF
vcf <- vcf[which(indDP & indVAR)]
rm(depth)
if (!is.null(snpDB)){
message (" by SNP VCF file ", snpDB)
snp <- readVcf(snpDB, genome = build)
snpInd <- nchar(unstrsplit(CharacterList(rowRanges(snp)$ALT), sep=",")) == 1 &
nchar(unstrsplit(rowRanges(snp)$REF, sep=",")) == 1
snp <- snp[which(snpInd)]
hits <- findOverlaps(query = rowRanges(vcf), subject = rowRanges(snp))
indSNPDB <- queryHits(hits)
vcf <- vcf[indSNPDB]
}
##
#acounts <- do.call(rbind, geno(vcf)$AD)
# phased snps
#indPhased <- grepl(pattern = "\\|", geno(vcf)$GT)
# phase sets
#ps.rle <- rle(as.integer(geno(vcf)$PS))
geno.gr <- rowRanges(vcf)
values(geno.gr) <- cbind(values(geno.gr),
DataFrame(GT = as.character(geno(vcf)$GT), PS = as.character(geno(vcf)$PS)))
HT <- getPhasedAllele(geno.gr)
values(geno.gr) <- cbind(values(geno.gr), DataFrame(HT1 = HT$h1, HT2 = HT$h2))
geno.gr <- keepSeqlevels(geno.gr, chrs, pruning.mode="coarse")
return(list(vcf.filtered = vcf, geno = geno.gr))
}
# x GRanges object with GT, REF, ALT metadata columns
# returns reference or alternate allele in haplotype
getPhasedAllele <- function(x){
h1 <- rep(NA, length(x))
h2 <- rep(NA, length(x))
# haplotype 1 (h1) ref allele for 1|0
h1[x$GT == "1|0"] <- as.character(x$REF)[x$GT == "1|0"]
h2[x$GT == "1|0"] <- as.character(unlist(x$ALT))[x$GT == "1|0"]
# haplotype 1 (h1) alt allele for 0|1
h1[x$GT == "0|1"] <- as.character(unlist(x$ALT))[x$GT == "0|1"]
h2[x$GT == "0|1"] <- as.character(x$REF)[x$GT == "0|1"]
return(list(h1 = h1, h2 = h2))
}
plotHaplotypeFraction <- function(dataIn, chr = c(1:22), resultType = "HaplotypeRatio", colType = "Haplotypes",
phaseBlockCol = c("#9ad0f3", "#CC79A7"), geneAnnot = NULL, spacing = 4, xlim = NULL, ...) {
if (!resultType %in% c("HaplotypeRatio", "AllelicRatio")){
stop("plotHaplotypeFraction: resultType must be one of 'HaplotypeRatio' or 'AllelicRatio'.")
}
if (!colType %in% c("Haplotypes", "CopyNumber")){
stop("plotHaplotypeFraction: plotType must be one of 'Haplotypes' or 'CopyNumber'")
}
# use consistent chromosome naming convention
chr <- as.character(chr)
seqlevelsStyle(chr) <- seqlevelsStyle(as.character(dataIn$Chr))[1]
lohCol.hap <- c(`0`=phaseBlockCol[1], `1`=phaseBlockCol[2])
lohCol.titan <- c("#00FF00", "#006400", "#0000FF", "#8B0000",
"#006400", "#BEBEBE", "#FF0000", "#BEBEBE",
"#FF0000")
names(lohCol.titan) <- c("HOMD", "DLOH", "NLOH", "GAIN",
"ALOH", "HET", "ASCNA", "BCNA", "UBCNA")
dataIn <- copy(dataIn)
colnames(dataIn)[1:2] <- c("Chr", "Position")
#dataIn$AllelicRatio <- pmax(dataIn$refOriginal, dataIn$tumDepthOriginal - dataIn$refOriginal) / dataIn$tumDepthOriginal
## set alternating 0 and 1 for phaseSets
psRuns <- rle(dataIn$PhaseSet)
ps.id <- 1:length(psRuns$lengths) %% 2
dataIn$phaseSet.id <- rep(ps.id, psRuns$lengths)
#dataIn$HaplotypeCount[as.logical(dataIn$phaseSet.id)] <- dataIn$HaplotypeDepth[as.logical(dataIn$phaseSet.id)] - dataIn$HaplotypeCount[as.logical(dataIn$phaseSet.id)]
#dataIn$HaplotypeRatio[as.logical(dataIn$phaseSet.id)] <- 1 - dataIn$HaplotypeRatio[as.logical(dataIn$phaseSet.id)]
#dataIn$AllelicRatio <- dataIn$refOriginal / dataIn$tumDepthOriginal
dataIn[, HaplotypeRatio.1 := HaplotypeRatio]#dataIn$HaplotypeCount / dataIn$HaplotypeDepth
dataIn[, HaplotypeRatio.2 := 1 - HaplotypeRatio]#(dataIn$HaplotypeDepth - dataIn$HaplotypeCount) / dataIn$HaplotypeDepth
if (!is.null(chr) && length(chr) == 1) {
for (i in chr) {
dataByChr <- dataIn[Chr == i, ]
#dataByChr <- dataByChr[dataByChr[, "TITANcall"] != "OUT", ]
# plot the data if (outfile!=''){
# pdf(outfile,width=10,height=6) }
if (colType == "Haplotypes" || !"TITANcall" %in% colnames(dataByChr)){
colors.1 <- lohCol.hap[as.character(dataByChr$phaseSet.id)]
colors.2 <- lohCol.hap[as.character(as.numeric(!dataByChr$phaseSet.id))]
}else{
colors.1 <- lohCol.titan[dataByChr[, TITANcall]]
colors.2 <- colors.1
}
par(mar = c(spacing, 8, 2, 2))
# par(xpd=NA)
if (missing(xlim)) {
xlim <- as.numeric(c(1, dataByChr[nrow(dataByChr), Position]))
}
if (resultType == "HaplotypeRatio"){
plot(dataByChr[, Position], dataByChr[, HaplotypeRatio.1],
col = colors.1,
pch = 16, xaxt = "n", las = 1, ylab = "Haplotype Fraction", xlim = xlim,
...)
points(dataByChr[, Position], dataByChr[, HaplotypeRatio.2], col = colors.1, pch=16, ...)
}else if (resultType == "AllelicRatio"){
plot(dataByChr[, Position], dataByChr[, AllelicRatio],
col = colors.1,
pch = 16, xaxt = "n", las = 1, ylab = "Allelic Fraction", xlim = xlim,
...)
}else{
stop("Need to specify \"resultType\": HaplotypeRatio or AllelicRatio")
}
lines(as.numeric(c(1, dataByChr[nrow(dataByChr), Position])), rep(0.5, 2), type = "l",
col = "grey", lwd = 3)
if (!is.null(geneAnnot)) {
plotGeneAnnotation(geneAnnot, i)
}
}
} else {
if (colType == "Haplotypes" || !"TITANcall" %in% colnames(dataIn)){
colors.1 <- lohCol.hap[as.character(dataIn$phaseSet.id)]
colors.2 <- lohCol.hap[as.character(as.numeric(!dataIn$phaseSet.id))]
}else{
colors.1 <- lohCol.titan[dataIn[, TITANcall]]
colors.2 <- colors.1
}
# plot for all chromosomes specified
dataIn <- dataIn[Chr %in% chr]
coord <- getGenomeWidePositions(dataIn[, Chr], dataIn[, Position])
if (resultType == "HaplotypeRatio"){
plot(coord$posns, as.numeric(dataIn[, HaplotypeRatio.1]),
col = colors.1, pch = 16,
xaxt = "n", bty = "n", las = 1, ylab = "Haplotype Fraction", ...)
points(coord$posns, dataIn[, HaplotypeRatio.2], col = colors.1, pch=16, ...)
}else if (resultType == "AllelicRatio"){
plot(coord$posns, as.numeric(dataIn[, AllelicRatio]),
col = colors.2, pch = 16,
xaxt = "n", bty = "n", las = 1, ylab = "Allelic Fraction", ...)
}else{
stop("Need to specify \"resultType\": HaplotypeRatio or AllelicRatio")
}
lines(as.numeric(c(1, coord$posns[length(coord$posns)])),
rep(0.5, 2), type = "l", col = "grey",
lwd = 3)
plotChrLines(unique(dataIn[, Chr]), coord$chrBkpt,
c(-0.1, 1.1))
}
}
keepChr <- function(tumour_reads, chr = c(1:22,"X","Y")){
tumour_reads <- tumour_reads[space(tumour_reads) %in% chr, ]
tumour_reads <- as.data.frame(tumour_reads)
tumour_reads$space <- droplevels(tumour_reads$space)
tumour_reads$space <- factor(tumour_reads$space,levels=chr)
tumour_reads <- as(tumour_reads,"RangedData")
return(tumour_reads)
}
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