Nothing
R/GOTHiC.R
In GOTHiC: Binomial test for Hi-C data analysis
Defines functions
GOTHiChicup
GOTHiC
.binomialHiC
.binomialHiChicup
.mapHicupToRestrictionFragment
.getHindIIIsitesFromHicup
.importHicup
.fixChromosomeNames
.binInteractions
.countDuplicates
mapReadsToRestrictionSites
.exportCoverage
.findOverlaps.parallel
.strandAsSignedNumber
.findOverlaps.circle
.putRangesOnFirstCircle
.getRestrictionSitesFromBSgenome
pairReads
.filterOutDuplicates
.countDuplicatesOfRaw
.onlyPairing
Documented in
GOTHiC
GOTHiChicup
mapReadsToRestrictionSites
pairReads
#Copyright: Bori Gerle (gerle@ebi.ac.uk), EMBL-EBI, 2011
#set global variables
globalVariables(c("BSgenome.Hsapiens.UCSC.hg19", "V1", "chr1", "chr2", "filtered", "frequencies", "int1", "int2", "interactingLoci", "locus1", "locus2", "pvalue", "resGR"))
.onlyPairing <- function(fileName1, fileName2, sampleName, fileType)
{
#DUPLICATETHRESHOLD: maximum amount of duplicated paired-end reads allowed (over that value it is expected to be PCR bias)
if(fileType=="Table"){
reads1=read.table(fileName1, header=F, sep='\t', colClasses=c(rep('character', times=3), 'numeric', rep("NULL", times=4)))
reads1_1=GRanges(seqnames=reads1$V3, ranges=IRanges(start=reads1$V4, end=reads1$V4), strand=reads1$V2, id=sapply(strsplit(reads1$V1, ' '), '[[', 2))
reads2=read.table(fileName2, header=F, sep='\t', colClasses=c(rep('character', times=3), 'numeric', rep("NULL", times=4)))
reads2_1=GRanges(seqnames=reads2$V3, ranges=IRanges(start=reads2$V4, end=reads2$V4), strand=reads2$V2, id=sapply(strsplit(reads2$V1, ' '), '[[', 2))
id1=as.character(elementMetadata(reads1_1)$id)
id2=as.character(elementMetadata(reads2_1)$id)
ids1=id1
ids2=id2
}
if(fileType%in%c("Bowtie", "BAM")){
if(fileType=="Bowtie"){
reads1=readAligned(fileName1,type=fileType) ## returns identifier, strand, chromosome, position read, quality
reads1_1=as(reads1,"GRanges") ## Grange object containing 4 slots, seqnames, ranges, strand, elementMetadata (data.frame)
reads2=readAligned(fileName2,type=fileType)
reads2_1=as(reads2,"GRanges")
#match IDs to find paired reads where both ends were mapped
id1=as.character(elementMetadata(reads1_1)$id)
id2=as.character(elementMetadata(reads2_1)$id)
}
if(fileType=='BAM'){
what=c("qname","rname","pos","qwidth", "strand")
param=ScanBamParam(what=what)
reads1=scanBam(fileName1, param=param)
reads2=scanBam(fileName2, param=param)
id1=reads1[[1]]$qname
id2=reads2[[1]]$qname
reads1_1=GRanges(seqnames=reads1[[1]]$rname, ranges=IRanges(start=reads1[[1]]$pos, end=reads1[[1]]$pos+reads1[[1]]$qwidth), strand=reads1[[1]]$strand)
reads2_1=GRanges(seqnames=reads2[[1]]$rname, ranges=IRanges(start=reads2[[1]]$pos, end=reads2[[1]]$pos+reads2[[1]]$qwidth), strand=reads2[[1]]$strand)
if(length(grep('SRR',id1[1]))==1|length(grep('ERR',id1[1]))==1){
ids1=id1
ids2=id2
elementMetadata(reads1_1)$id = id1
elementMetadata(reads2_1)$id = id2
}else
{
ids1 = sapply(strsplit(id1, '\\-'), '[[', 2)
ids2 = sapply(strsplit(id2, '\\-'), '[[', 2)
elementMetadata(reads1_1)$id = ids1
elementMetadata(reads2_1)$id = ids2}
}else
{
#old fastq files use /1 and /2 to differentiate read pairs, CASAVA 1.8 separates members of a pair with spaces
firsttwo=sapply(strsplit(id1[1:2], '[/ ]'), '[[', 1)
if(firsttwo[1]==firsttwo[2]){
ids1 = sapply(strsplit(id1, ' '), '[[', 1)
ids2 = sapply(strsplit(id2, ' '), '[[', 1)
}else
{
ids1 = sapply(strsplit(id1, '[/ ]'), '[[', 1)
ids2 = sapply(strsplit(id2, '[/ ]'), '[[', 1)
}
}
}
rm(id1,id2)
s1=which(ids1%in%ids2)
s2=which(ids2%in%ids1)
paired_reads_1=reads1_1[s1]
rm(reads1_1)
paired_reads_1=paired_reads_1[order(elementMetadata(paired_reads_1)$id)]
paired_reads_2=reads2_1[s2]
rm(reads2_1)
paired_reads_2=paired_reads_2[order(elementMetadata(paired_reads_2)$id)]
paired_df=cbind(as.character(seqnames(paired_reads_1)),as.character(start(ranges(paired_reads_1))),as.character(end(ranges(paired_reads_1))),as.character(seqnames(paired_reads_2)),as.character(start(ranges(paired_reads_2))),as.character(end(ranges(paired_reads_2))))
colnames(paired_df)=c("chr1","start1","end1", "chr2","start2","end2")
#save paired reads in GRanges objects and data.frame
paired_df=data.frame(paired_df,frequencies=rep(1,times=nrow(paired_df)),stringsAsFactors=FALSE)
return(list(paired_reads_1=paired_reads_1, paired_reads_2=paired_reads_2, paired_df=paired_df,s1=s1,s2=s2))
}
.countDuplicatesOfRaw <- function(paired_df, sampleName)
{
#count how many times we have the exact same read pairs to be able to remove PCR duplicates
colus=c("chr1","start1","end1", "chr2","start2","end2")
vec <- do.call("paste",c(paired_df[colus],sep="_"))
names(vec) <- c(1:length(vec))
dup <- duplicated(vec)
uniq <-vec[!dup]
uniqfreq <- rep(1, times=length(uniq))
uniq=cbind(uniq,uniqfreq)
colnames(uniq)=c("interaction","freqs")
vec1 <- vec[dup]
vec1 <- sort(vec1)
freq <- cbind(vec1,as.numeric(sequence(rle(vec1)$lengths))+1)
colnames(freq)=c("interaction","freqs")
vec=rbind(uniq, freq)
vec <- vec[order(as.numeric(rownames(vec))),]
paired_df$frequencies <- as.numeric(vec[,2])
freqs <- as.numeric(vec[,2])
freqstable <- table(freqs)
l=c()
for(i in 1:length(freqstable-1)){
l[i]=freqstable[i]==freqstable[i+1]
}
p=freqstable[l]
p=p[1]
ths=as.numeric(names(p))
return(list(paired_df=paired_df,freqs=freqs,ths=ths))
}
.filterOutDuplicates <- function(paired_df, paired_reads_1, paired_reads_2, sampleName, DUPLICATETHRESHOLD)
{
#DUPLICATETHRESHOLD: maximum amount of duplicated paired-end reads allowed (over that value it is expected to be PCR bias)
#remove readpairs that are present in more replicates than the DUPLICATETHRESHOLD - keep as many of the reads as the threshold
dth <- which(paired_df$frequencies<(DUPLICATETHRESHOLD+1))
paired_reads_1<-paired_reads_1[dth]
paired_reads_2<-paired_reads_2[dth]
return(list(paired_reads_1=paired_reads_1,paired_reads_2=paired_reads_2))
}
pairReads <- function(fileName1, fileName2, sampleName, DUPLICATETHRESHOLD=1, fileType='BAM')
{
oP=.onlyPairing(fileName1, fileName2, sampleName, fileType)
paired_reads_1=oP$paired_reads_1
paired_reads_2=oP$paired_reads_2
paired_df=oP$paired_df
s1=oP$s1
s2=oP$s2
cDoR=.countDuplicatesOfRaw(paired_df, sampleName)
paired_df=cDoR$paired_df
freqs=cDoR$freqs
ths=cDoR$ths
filtered=.filterOutDuplicates(paired_df, paired_reads_1, paired_reads_2, sampleName, DUPLICATETHRESHOLD)
save(filtered,file=paste(sampleName,"paired_filtered",sep="_"))
return(filtered)
}
.getRestrictionSitesFromBSgenome <- function(BSgenomeName, genome, restrictionSite, enzyme)
{
#check if the genome needed is already installed and install from BioConductor if not
iG=installed.genomes()
if(!BSgenomeName%in%iG){
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install(BSgenomeName)
}
library(BSgenomeName,character.only=TRUE)
chrEnds <- seqlengths(genome)
#find out how many base pairs from the 5 end of the restriction site the enzyme cuts
restRemain <- regexpr("\\^",restrictionSite)[1]-1
#restriction site as regular expression
rSite <- sub('\\^', '', restrictionSite)
#find locations where restriction enzyme cuts for all chromosomes
params <- new("BSParams",X=genome, FUN=matchPattern,simplify=TRUE)
resSite <- bsapply(params, pattern = rSite)
starts <- lapply(resSite, function(x){c(1,(start(x)+restRemain))})
lengths <- lapply(resSite, function(x){length(x)+1})
chrs <- lapply(1:length(names(resSite)),function(i){rep(names(resSite)[i],times=lengths[[i]])})
ends <- lapply(1:length(names(resSite)), function(i){c(start(resSite[[i]])+restRemain-1,chrEnds[i])})
starts <- unlist(starts)
chrs <- unlist(chrs)
ends <- unlist(ends)
#save restriction sites as GRanges
resGR <- GRanges(seqnames=chrs, ranges=IRanges(start=starts, end=ends))
save(resGR,file=paste("Digest",BSgenomeName,enzyme,sep="_"))
return(resGR)
}
#functions for parallel version of findOverlaps
.putRangesOnFirstCircle <- function(x, circle.length)
{
x_start0 <- start(x) - 1L # 0-based start
x_shift0 <- x_start0 %% circle.length - x_start0
shift(x, x_shift0)
}
.findOverlaps.circle <- function(circle.length, query, subject,
maxgap, minoverlap, type)
{
if (is.na(circle.length))
return(findOverlaps(query, subject,
maxgap=maxgap, minoverlap=minoverlap,
type=type, select="all"))
if (type != "any")
stop("overlap type \"", type, "\" is not yet supported ",
"for circular sequence ", names(circle.length))
subject0 <- .putRangesOnFirstCircle(subject, circle.length)
inttree0 <- NCList(subject0)
query0 <- .putRangesOnFirstCircle(query, circle.length)
hits00 <- findOverlaps(query0, inttree0,
maxgap=maxgap, minoverlap=minoverlap,
type=type, select="all")
query1 <- shift(query0, circle.length)
hits10 <- findOverlaps(query1, inttree0,
maxgap=maxgap, minoverlap=minoverlap,
type=type, select="all")
subject1 <- shift(subject0, circle.length)
hits01 <- findOverlaps(query0, subject1,
maxgap=maxgap, minoverlap=minoverlap,
type=type, select="all")
## Merge 'hits00', 'hits10' and 'hits01'.
union(union(hits00, hits10), hits01)
}
.strandAsSignedNumber <- function(x)
{
tmp <- as.integer(runValue(x))
idx <- tmp >= 2L
tmp[idx] <- tmp[idx] - 3L
runValue(x) <- tmp
as.vector(x)
}
.findOverlaps.parallel <- function(query, subject, maxgap=0L, minoverlap=1L,
type=c("any", "start", "end", "within", "equal"),
select=c("all", "first", "last", "arbitrary"),
ignore.strand=TRUE, mc.cores=1, mc.preschedule = TRUE)
{
if (!isSingleNumber(maxgap) || maxgap < 0L)
stop("'maxgap' must be a non-negative integer")
type <- match.arg(type)
select <- match.arg(select)
## merge() also checks that 'query' and 'subject' are based on the
## same reference genome.
seqinfo <- merge(seqinfo(query), seqinfo(subject))
q_len <- length(query)
s_len <- length(subject)
q_seqnames <- seqnames(query)
s_seqnames <- seqnames(subject)
q_seqlevels <- levels(q_seqnames)
s_seqlevels <- levels(s_seqnames)
q_splitranges <- splitRanges(q_seqnames)
s_splitranges <- splitRanges(s_seqnames)
q_ranges <- unname(ranges(query))
s_ranges <- unname(ranges(subject))
if (ignore.strand) {
q_strand <- rep.int(1L, q_len)
s_strand <- rep.int(1L, s_len)
} else {
q_strand <- .strandAsSignedNumber(strand(query))
s_strand <- .strandAsSignedNumber(strand(subject))
}
common_seqlevels <- intersect(q_seqlevels, s_seqlevels)
results <- parallel::mclapply(common_seqlevels,
function(seqlevel)
{
if (isCircular(seqinfo)[seqlevel] %in% TRUE) {
circle.length <- seqlengths(seqinfo)[seqlevel]
} else {
circle.length <- NA
}
q_idx <- q_splitranges[[seqlevel]]
s_idx <- s_splitranges[[seqlevel]]
hits <- .findOverlaps.circle(circle.length,
q_ranges[q_idx],
s_ranges[s_idx],
maxgap, minoverlap, type)
q_hits <- queryHits(hits)
s_hits <- subjectHits(hits)
compatible_strand <- q_strand[q_idx][q_hits] *
s_strand[s_idx][s_hits] != -1L
hits <- hits[compatible_strand]
remapHits(hits, Lnodes.remapping=as.integer(q_idx),
new.nLnode=q_len,
Rnodes.remapping=as.integer(s_idx),
new.nRnode=s_len)
}, mc.cores=mc.cores, mc.preschedule=mc.preschedule)
## Combine the results.
q_hits <- unlist(lapply(results, queryHits))
if (is.null(q_hits))
q_hits <- integer(0)
s_hits <- unlist(lapply(results, subjectHits))
if (is.null(s_hits))
s_hits <- integer(0)
if (select == "arbitrary") {
ans <- rep.int(NA_integer_, q_len)
ans[q_hits] <- s_hits
return(ans)
}
if (select == "first") {
ans <- rep.int(NA_integer_, q_len)
oo <- S4Vectors::orderIntegerPairs(q_hits, s_hits, decreasing=TRUE)
ans[q_hits[oo]] <- s_hits[oo]
return(ans)
}
oo <- S4Vectors::orderIntegerPairs(q_hits, s_hits)
q_hits <- q_hits[oo]
s_hits <- s_hits[oo]
if (select == "last") {
ans <- rep.int(NA_integer_, q_len)
ans[q_hits] <- s_hits
return(ans)
}
new2("Hits", queryHits=q_hits, subjectHits=s_hits,
queryLength=q_len, subjectLength=s_len,
check=FALSE)
}
.exportCoverage <- function(reads, sampleName)
{
coverage_reads = coverage(reads)
export.bedGraph(as(coverage_reads,"GRanges"), paste("coverage_", sampleName, ".bed", sep =""))
}
mapReadsToRestrictionSites <- function(pairedReadsFile, sampleName,BSgenomeName,genome,restrictionSite,enzyme,parallel=F, cores=1)
{
paired_reads_1 <- pairedReadsFile$paired_reads_1
paired_reads_2 <- pairedReadsFile$paired_reads_2
#calculate coverage
all_reads <- c(paired_reads_1, paired_reads_2)
.exportCoverage(all_reads, sampleName)
#take restriction sites
digestFile <- paste("Digest",BSgenomeName,enzyme,sep="_")
if(file.exists(digestFile))
{
load(paste("Digest",BSgenomeName,enzyme,sep="_"))
hindIIIRanges <- resGR
}else
{
hindIIIRanges <- .getRestrictionSitesFromBSgenome(BSgenomeName, genome,restrictionSite,enzyme)
}
#find HindIII sites
if(parallel)
{
requireNamespace("parallel")
hindIII_1 <- .findOverlaps.parallel(paired_reads_1, hindIIIRanges, mc.cores=cores, select="first")
hindIII_2 <- .findOverlaps.parallel(paired_reads_2, hindIIIRanges, mc.cores=cores, select="first")
}else
{
hindIII_1 <- findOverlaps(paired_reads_1, hindIIIRanges, select="first")
hindIII_2 <- findOverlaps(paired_reads_2, hindIIIRanges, select="first")
}
rm(paired_reads_1,paired_reads_2)
#take out reads where we cant assign hindIII site
validInteractions=!is.na(hindIII_1) & !is.na(hindIII_2)
hindIII_1=hindIII_1[validInteractions]
hindIII_2=hindIII_2[validInteractions]
hindIII1=pmin(hindIII_1, hindIII_2)
hindIII2=pmax(hindIII_1, hindIII_2)
#assign hindIII restriction sites to reads
loci1 <- hindIIIRanges[hindIII1]
loci2 <- hindIIIRanges[hindIII2]
interactingLoci <- GRangesList(locus1 = loci1, locus2 = loci2)
outputfilename <- paste("interactingLoci", sampleName, sep = "_")
save(interactingLoci, file=outputfilename)
return(interactingLoci)
}
#Copyright: Robert Sugar (robert.sugar@ebi.ac.uk), EMBL-EBI, 2011
#counts duplicate rows of a data frame
#returns a data frame with unique rows and the frequencies in the new row "frequencies"
#warning: data frame should be ordered!
.countDuplicates <- function(df, frequencyCol = c("frequencies", "frequency"), considerExistingFrequencies = any(frequencyCol %in% names(df)), ordered=FALSE)
{
if(considerExistingFrequencies)
{
frequencyCol <- frequencyCol[frequencyCol %in% names(df)][1]
if(is.null(df[[frequencyCol]]))
stop("frequencies column missing")
} else if(!considerExistingFrequencies)
{
frequencyCol <- frequencyCol[1]
df[, frequencyCol] <- 1 #one for all
}
#order
chrLocusColumns <- c("chr1", "locus1", "chr2", "locus2")
coverageColumns <- c("chr", "locus")
startEndColumns <- c("seqnames1", "start1", "end1", "strand1", "seqnames2", "start2", "end2", "strand2")
if(all(chrLocusColumns %in% names(df)))
{
df <- df[order(df$chr1, df$locus1, df$chr2, df$locus2), ]
} else
if(all(coverageColumns %in% names(df)))
{
df <- df[order(df$chr, df$locus), ]
} else
if(all(startEndColumns %in% names(df)))
{
df <- df[order(df$seqnames1, df$start1, df$end1, df$strand1, df$seqnames2, df$start2, df$end2, df$strand2), ]
} else #use all columns except for frequency
{
df <- df[do.call(order, df[, names(df)[-which(names(df) == frequencyCol)]]), ]
}
#add up the frequencies for the original and all duplicates of a row
duplicates <- c(duplicated(df[, -which(names(df) == frequencyCol)])) #look for duplicates (excluding frequencies column)
cumulative <- c(0, cumsum(df[, frequencyCol])) #the 0 is needed to consider not the first unique but the last duplicate
beforeFirstUnique <- cumulative[!c(duplicates, FALSE)] #the extra FALSE value is needed for the last group
dfUnique <- df[!duplicates, ]
dfUnique[, frequencyCol] = diff(beforeFirstUnique)
return(dfUnique)
}
#####bin interactions into desired bin size e.g. 1Mb########
.binInteractions <- function(interactions, resolution, frequencyCol = "frequencies", considerExistingFrequencies = frequencyCol %in% names(interactions))
{
if(!identical(colnames(interactions)[1:4], c("chr1", "locus1", "chr2", "locus2")))
stop("expecting columns chr1, locus1, chr2, locus2")
if(considerExistingFrequencies && sum(names(interactions) == "frequencies") == 0)
stop(paste("expecting column", frequencyCol))
#if resolution is given, bins will be calculated from interactions using the resolution
interactions$locus1 <- (interactions$locus1 %/% resolution) * resolution
interactions$locus2 <- (interactions$locus2 %/% resolution) * resolution
#put smaller locus first to make sure a-b and b-a interactions look the same
firstSmaller <- subset(interactions, (as.character(chr1) < as.character(chr2)) | (as.character(chr1) == (as.character(chr2)) & locus1 <= locus2))
secondSmaller <- subset(interactions, !((as.character(chr1) < as.character(chr2)) | (as.character(chr1) == (as.character(chr2)) & locus1 <= locus2)))
#flip first and second if second is smaller
cnames <- colnames(interactions)
cnames[1:4] <- c("chr2", "locus2", "chr1", "locus1")
setnames(secondSmaller,colnames(secondSmaller),cnames)
#stich them back together
interactions <- rbind(firstSmaller, secondSmaller)
# --------- count the number of interactions between bins -------
interactions <- interactions[order(interactions$chr1, interactions$locus1, interactions$chr2, interactions$locus2), ]
#bin it
interactions <- .countDuplicates(interactions, frequencyCol, considerExistingFrequencies)
return(interactions)
}
#############functions to import HiCUP filtered reads##########
.fixChromosomeNames <- function(chrnames)
{
#capital to small
chrnames <- paste0("chr", chrnames)
chrnames <- sub("CHR", "chr", chrnames)
chrnames <- sub("chrchr", "chr", chrnames)
}
.importHicup <- function(fileName, checkConsistency=TRUE, fileType=ifelse(grepl("\\.bam$", fileName)|grepl("\\.sam$", fileName), "bam", "table"), compressed=ifelse(grepl("\\.gz$", fileName), TRUE, FALSE))
{
#the output of hicup is a sam file, that looks like uniques_ORIGINALFILE_trunc.sam
#this has to be converted using the hicupToTable tool
if(compressed){
tbl <- read.table(gzfile(fileName))
#in the sam file the two ends of the interactions are consecutive rows
odd <- tbl[seq(1,nrow(tbl),2), ]
names(odd) <- c("id1", "flag1", "chr1", "locus1")
levels(odd$chr1) <- .fixChromosomeNames(levels(odd$chr1))
even <- tbl[seq(2,nrow(tbl),2), ]
names(even) <- c("id2", "flag2", "chr2", "locus2")
levels(even$chr2) <- .fixChromosomeNames(levels(even$chr2))
}else{
if(fileType=="table")
{
tbl <- read.table(fileName)
#in the sam file the two ends of the interactions are consecutive rows
odd <- tbl[seq(1,nrow(tbl),2), ]
names(odd) <- c("id1", "flag1", "chr1", "locus1")
levels(odd$chr1) <- .fixChromosomeNames(levels(odd$chr1))
even <- tbl[seq(2,nrow(tbl),2), ]
names(even) <- c("id2", "flag2", "chr2", "locus2")
levels(even$chr2) <- .fixChromosomeNames(levels(even$chr2))
} else if(fileType=="bam")
{
stop('imporHicup: HiCUP output BAM/SAM file needs to be converted into a table')
}
}
joined <- cbind(odd, even)
if(nrow(odd) != nrow(even)) stop('importHicup: reads must be paired in consecutive rows')
if(checkConsistency)
{
if(!all(joined$id1==joined$id2)) stop('importHicup: reads must be paired in consecutive rows')
}
return(joined[, c("chr1", "locus1", "chr2", "locus2")])
}
.getHindIIIsitesFromHicup <- function(fileName, asRanges = TRUE)
{
sites = read.table(fileName, stringsAsFactors=FALSE, header=TRUE, skip=1)
sites$chr <- sub("^", "chr", sites$Chromosome)
sites$chr <- sub("chrCHR", "chr", sites$chr) #sometimes there is CHR
sites$chr <- sub("chrchr", "chr", sites$chr) #sometimes there is CHR
sites$start <- sites$Fragment_Start_Position
sites$locus <- sites$Fragment_Start_Position
sites$end <- sites$Fragment_End_Position
sites <- sites[, c("chr", "locus", "start", "end")]
if(asRanges)
{
return(GRanges(seqnames=sites$chr, ranges=IRanges(start=sites$start, end=sites$end)))
}
}
.mapHicupToRestrictionFragment <- function(interactions, fragments)
{
source = GRanges(seqnames = Rle(interactions$chr1), ranges = IRanges(interactions$locus1, width = 1),strand = Rle("*", nrow(interactions)))
target = GRanges(seqnames = Rle(interactions$chr2), ranges = IRanges(interactions$locus2, width = 1),strand = Rle("*", nrow(interactions)))
if(is.character(fragments))
{
fragments <- .getHindIIIsitesFromHicup(fragments)
}
firstFragment <- findOverlaps(source, fragments, select="first")
secondFragment <- findOverlaps(target, fragments, select="first")
#filter out non-mapping loci
validInteractions <- !is.na(firstFragment) & !is.na(secondFragment)
firstFragment <- firstFragment[validInteractions]
secondFragment <- secondFragment[validInteractions]
#make sure A-B and B-A are treated together -> put fragment with smaller ID in front
sources <- pmin(firstFragment, secondFragment)
targets <- pmax(firstFragment, secondFragment)
interactions <- as.data.frame(cbind(as.character(seqnames(fragments[sources])), start(ranges(fragments[sources])), as.character(seqnames(fragments[targets])), start(ranges(fragments[targets])), rep(1, times=length(sources))), stringsAsFactors=FALSE)
colnames(interactions) <- c('chr1', 'locus1', 'chr2', 'locus2', 'frequencies')
interactions$locus1=as.numeric(interactions$locus1)
interactions$locus2=as.numeric(interactions$locus2)
interactions$frequencies=as.numeric(interactions$frequencies)
return(interactions)
}
.binomialHiChicup=function(hicupinteraction, restrictionFile, sampleName, cistrans='all', parallel=FALSE, cores=8, removeDiagonal=TRUE)
{
hindGR <- .getHindIIIsitesFromHicup(restrictionFile)
if(parallel)
{
requireNamespace("parallel")
print("running garbage collector before parallel fork")
gc()
}
binned_df_filtered <-hicupinteraction
binned_df_filtered$int1 <-paste(binned_df_filtered$chr1,binned_df_filtered$locus1,sep='_')
binned_df_filtered$int2 <-paste(binned_df_filtered$chr2,binned_df_filtered$locus2,sep='_')
#diagonal removal
if(removeDiagonal)
{
binned_df_filtered <- binned_df_filtered[binned_df_filtered$int1!=binned_df_filtered$int2,]
}
if(cistrans=='cis'){
binned_df_filtered <- binned_df_filtered[binned_df_filtered$chr1==binned_df_filtered$chr2,]
}
if(cistrans=='trans'){
binned_df_filtered <- binned_df_filtered[binned_df_filtered$chr1!=binned_df_filtered$chr2,]
}
#all read pairs used in binomial
numberOfReadPairs <- sum(binned_df_filtered$frequencies)
#calculate coverage
all_bins <- unique(c(unique(binned_df_filtered$int1), unique(binned_df_filtered$int2)))
all_bins <- sort(all_bins)
if(nrow(binned_df_filtered)>1e8)
{
t <- ceiling(nrow(binned_df_filtered)/1e8)
dfList <- list()
dfList[[1]] <- binned_df_filtered[1:1e8,]
for(i in 2:t){
dfList[[i]] <- binned_df_filtered[(((i-1)*1e8)+1):min((i*1e8),nrow(binned_df_filtered)),]
}
dtList <- lapply(dfList, data.table)
covAs <- lapply(dtList, function(x) x[,sum(frequencies), by=int1])
covBs <- lapply(dtList, function(x) x[,sum(frequencies), by=int2])
covAm <- do.call(rbind, covAs)
covBm <- do.call(rbind, covBs)
covA <- covAm[,sum(V1),by=int1]
covB <- covBm[,sum(V1),by=int2]
}else{
binned_dt=data.table(binned_df_filtered)
covA <- binned_dt[,sum(frequencies),by=int1]
covB <- binned_dt[,sum(frequencies),by=int2]
}
covA <- setkey(covA,key='int1')
setnames(covB, 1,'int1')
covB <- setkey(covB,key='int1')
cov=merge(covA,covB,all.x=TRUE,all.y=TRUE,by='int1')
cov$V1.x[is.na(cov$V1.x)]=0
cov$V1.y[is.na(cov$V1.y)]=0
cov$coverage=cov$V1.x+cov$V1.y
coverage=cov$coverage
names(coverage)=cov$int1
sumcov <- sum(coverage)
relative_coverage <- coverage/sumcov
names(relative_coverage)=names(coverage)
binned_df_filtered$coverage_source <- relative_coverage[binned_df_filtered$int1]
binned_df_filtered$coverage_target <- relative_coverage[binned_df_filtered$int2]
#probability correction assuming on average equal probabilities for all interactions
numberOfAllInteractions <- length(all_bins)^2
upperhalfBinNumber <- (length(all_bins)^2-length(all_bins))/2
if(cistrans!='all'){
chromos <- unique(binned_df_filtered$chr1)
chrlens <- c()
for(cr in chromos){
chrlens[cr] <- length(unique(c(unique(binned_df_filtered$locus1[binned_df_filtered$chr1==cr]),unique(binned_df_filtered$locus2[binned_df_filtered$chr2==cr]))))
}
cisBinNumber <-(sum(chrlens^2)-length(all_bins))/2
transBinNumber <- upperhalfBinNumber-cisBinNumber
}
diagonalProb <- sum(relative_coverage^2)
if(cistrans=='all'){
probabilityCorrection <- if(removeDiagonal){1/(1-diagonalProb)}else{1}
}
if(cistrans=='cis'){
probabilityCorrection <- upperhalfBinNumber/cisBinNumber
}
if(cistrans=='trans'){
probabilityCorrection <- upperhalfBinNumber/transBinNumber
}
binned_df_filtered$probabilityOfInteraction <- binned_df_filtered$coverage_source*binned_df_filtered$coverage_target*2*probabilityCorrection
binned_df_filtered$predicted <- binned_df_filtered$probabilityOfInteraction * numberOfReadPairs
if(parallel)
{
requireNamespace("parallel")
if(nrow(binned_df_filtered)>1e8)
{
t <- ceiling(nrow(binned_df_filtered)/1e8)
dfList <- list()
dfList[[1]] <- binned_df_filtered[1:1e8,]
for(i in 2:t){
dfList[[i]] <- binned_df_filtered[(((i-1)*1e8)+1):min((i*1e8),nrow(binned_df_filtered)),]
}
dtList <- lapply(dfList, function(x) as.data.frame(t(cbind(as.numeric(x[["frequencies"]]),
as.numeric(x[["probabilityOfInteraction"]])))))
pvalues=list()
for(i in 1:length(dtList)){
pvalues[[i]] <-unlist(parallel::mclapply(dtList[[i]], function(x)
{
binom.test(x[1], numberOfReadPairs, x[2], alternative = "greater")$p.value
},
mc.cores=cores))
}
pvals=unlist(pvalues)
binned_df_filtered$pvalue <- pvals
}else{
binomParams <- as.data.frame(t(cbind(
as.numeric(binned_df_filtered[["frequencies"]]),
as.numeric(binned_df_filtered[["probabilityOfInteraction"]]
))))
binned_df_filtered$pvalue <- unlist(parallel::mclapply(binomParams, function(x)
{
binom.test(x[1], numberOfReadPairs, x[2], alternative = "greater")$p.value
},
mc.cores=cores))
}
}else{
if(nrow(binned_df_filtered)>1e8)
{
t <- ceiling(nrow(binned_df_filtered)/1e8)
dfList <- list()
dfList[[1]] <- binned_df_filtered[1:1e8,]
for(i in 2:t){
dfList[[i]] <- binned_df_filtered[(((i-1)*1e8)+1):min((i*1e8),nrow(binned_df_filtered)),]
}
pvalues=list()
for(i in 1:length(dfList)){
pvalues[[i]] <-apply(dfList[[i]], 1, function(x)
{
binom.test(as.numeric(x[["frequencies"]]), numberOfReadPairs, as.numeric(x[["probabilityOfInteraction"]]), alternative = "greater")$p.value
}
)
}
pvals=unlist(pvalues)
binned_df_filtered$pvalue <- pvals
}else{
binned_df_filtered$pvalue <- apply(binned_df_filtered, 1, function(x)
{
binom.test(as.numeric(x[["frequencies"]]), numberOfReadPairs, as.numeric(x[["probabilityOfInteraction"]]), alternative = "greater")$p.value
}
)
}
}
#observed over expected log ratio
binned_df_filtered$logFoldChange <- log2(binned_df_filtered$frequencies/binned_df_filtered$predicted)
#multiple testing correction separately for matrices with all interactions/only cis/only transs
if(cistrans=='all'){
binned_df_filtered$qvalue <- if(removeDiagonal){p.adjust(binned_df_filtered$pvalue, method = "BH", n=upperhalfBinNumber)}else{p.adjust(binned_df_filtered$pvalue, method = "BH", n=upperhalfBinNumber+length(all_bins))}
}
if(cistrans=='cis'){
binned_df_filtered$qvalue <- if(removeDiagonal){p.adjust(binned_df_filtered$pvalue, method = "BH", n=cisBinNumber)}else{p.adjust(binned_df_filtered$pvalue, method = "BH", n=cisBinNumber+length(all_bins))}
}
if(cistrans=='trans'){
binned_df_filtered$qvalue <- p.adjust(binned_df_filtered$pvalue, method = "BH", n=transBinNumber)
}
test <- data.frame(binned_df_filtered)
test[,"pvalue"] <- test$pvalue
pval.plot <- ggplot(test,aes(x=pvalue))
tryCatch(
{
x11()
print(pval.plot + geom_density())
},
error=function(cond) {
message("No interactive plot, try saving image")
message(cond)
return(tryCatch(
{
pdf(file=paste(sampleName,"pvalue_distribution.pdf",sep="_"))
print(pval.plot + geom_density())
dev.off()
},
error=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
},
warning=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
}
))
},
warning=function(cond) {
message("No interactive plot, try saving image")
message(cond)
return(tryCatch(
{
pdf(file=paste(sampleName,"pvalue_distribution.pdf",sep="_"))
print(pval.plot + geom_density())
dev.off()
},
error=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
},
warning=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
}
))
})
binned_df_filtered=binned_df_filtered[,c('chr1','locus1','chr2','locus2','coverage_source','coverage_target','probabilityOfInteraction', 'predicted','frequencies', 'pvalue','qvalue','logFoldChange')]
colnames(binned_df_filtered)=c('chr1','locus1','chr2','locus2','relCoverage1','relCoverage2','probability', 'expected','readCount', 'pvalue','qvalue','logObservedOverExpected')
return(binned_df_filtered)
}
# Author: borbalagerle
###############################################################################
# take GenomicRangesList with interactions in locus1 and locus2
.binomialHiC=function(interactingLoci, res,sampleName,BSgenomeName, genome, restrictionSite, enzyme, parallel=F, cores=NULL,removeDiagonal=TRUE,cistrans='all',filterdist=10000)
{
#take restriction sites
digestFile <- paste("Digest",BSgenomeName,enzyme,sep="_")
if(file.exists(digestFile))
{
load(paste("Digest",BSgenomeName,enzyme,sep="_"))
hindIIIRanges <- resGR
}else
{
hindIIIRanges <- .getRestrictionSitesFromBSgenome(BSgenomeName, genome, restrictionSite, enzyme)
}
elementMetadata(hindIIIRanges)$score=1:length(hindIIIRanges)
#remove self-ligations and interactions between adjacent bins by assigning the fragment number to reads and remove those where the difference is smaller or equal to 1
x=findOverlaps(interactingLoci[[1]],hindIIIRanges,select='first')
y=findOverlaps(interactingLoci[[2]],hindIIIRanges,select='first')
interactingLoci1 <- interactingLoci[[1]]
interactingLoci2 <- interactingLoci[[2]]
interactingLoci1$score=hindIIIRanges$score[x]
interactingLoci2$score=hindIIIRanges$score[y]
interactingLoci <- GRangesList(locus1=interactingLoci1,locus2=interactingLoci2)
score=interactingLoci[[2]]$score-interactingLoci[[1]]$score
interactingLoci[[1]]=interactingLoci[[1]][abs(score)>1]
interactingLoci[[2]]=interactingLoci[[2]][abs(score)>1]
#filter for reads that are closer than 10kb to get rid of incomplete digest products
# df_int <- data.frame(as.vector(seqnames(interactingLoci[[1]])), start(ranges(interactingLoci[[1]])), as.vector(seqnames(interactingLoci[[2]])), start(ranges(interactingLoci[[2]])))
df_int <- data.frame(as.character(seqnames(interactingLoci[[1]])), start(ranges(interactingLoci[[1]])), as.character(seqnames(interactingLoci[[2]])), start(ranges(interactingLoci[[2]])), stringsAsFactors=FALSE)
colnames(df_int) <- c("chr1", "locus1", "chr2", "locus2")
df_filtered <- df_int
df_filtered$dist <-as.vector(ifelse(df_filtered$chr1 == df_filtered$chr2, abs(df_filtered$locus1 - df_filtered$locus2), Inf))
df_filtered <-df_filtered[df_filtered$dist>filterdist,]
df_filtered <-df_filtered[,1:4]
#bin interactions according to resolution
binned_df_filtered <- .binInteractions(df_filtered, res)
binned_df_filtered$int1 <-paste(binned_df_filtered$chr1,binned_df_filtered$locus1,sep='_')
binned_df_filtered$int2 <-paste(binned_df_filtered$chr2,binned_df_filtered$locus2,sep='_')
#diagonal removal - interactions within the same bin
if(removeDiagonal)
{
subs <-which(binned_df_filtered$int1!=binned_df_filtered$int2)
binned_df_filtered <- binned_df_filtered[subs,]
}
if(cistrans=='cis'){
subs <-which(binned_df_filtered$chr1==binned_df_filtered$chr2)
binned_df_filtered <- binned_df_filtered[subs,]
}
if(cistrans=='trans'){
subs <-which(binned_df_filtered$chr1!=binned_df_filtered$chr2)
binned_df_filtered <- binned_df_filtered[subs,]
}
#all read pairs used in binomial
numberOfReadPairs <- sum(binned_df_filtered$frequencies)
#calculate coverage
all_bins <- unique(c(unique(binned_df_filtered$int1), unique(binned_df_filtered$int2)))
all_bins <- sort(all_bins)
if(nrow(binned_df_filtered)>1e8)
{
t <- ceiling(nrow(binned_df_filtered)/1e8)
dfList <- list()
dfList[[1]] <- binned_df_filtered[1:1e8,]
for(i in 2:t){
dfList[[i]] <- binned_df_filtered[(((i-1)*1e8)+1):min((i*1e8),nrow(binned_df_filtered)),]
}
dtList <- lapply(dfList, data.table)
covAs <- lapply(dtList, function(x) x[,sum(frequencies), by=int1])
covBs <- lapply(dtList, function(x) x[,sum(frequencies), by=int2])
covAm <- do.call(rbind, covAs)
covBm <- do.call(rbind, covBs)
covA <- covAm[,sum(V1),by=int1]
covB <- covBm[,sum(V1),by=int2]
}else{
binned_dt=data.table(binned_df_filtered)
covA <- binned_dt[,sum(frequencies),by=int1]
covB <- binned_dt[,sum(frequencies),by=int2]
}
covA <- setkey(covA,key='int1')
setnames(covB, 1,'int1')
covB <- setkey(covB,key='int1')
cov=merge(covA,covB,all.x=TRUE,all.y=TRUE,by='int1')
cov$V1.x[is.na(cov$V1.x)]=0
cov$V1.y[is.na(cov$V1.y)]=0
cov$coverage=cov$V1.x+cov$V1.y
coverage=cov$coverage
names(coverage)=cov$int1
sumcov <- sum(coverage)
relative_coverage <- coverage/sumcov
names(relative_coverage)=names(coverage)
binned_df_filtered$cov1 <- relative_coverage[binned_df_filtered$int1]
binned_df_filtered$cov2 <- relative_coverage[binned_df_filtered$int2]
#probability correction assuming on average equal probabilities for all interactions
numberOfAllInteractions <- length(all_bins)^2
upperhalfBinNumber <- (length(all_bins)^2-length(all_bins))/2
chromos <- unique(binned_df_filtered$chr1)
chrlens <- c()
for(cr in chromos){
chrlens[cr] <- length(unique(c(unique(binned_df_filtered$locus1[binned_df_filtered$chr1==cr]),unique(binned_df_filtered$locus2[binned_df_filtered$chr2==cr]))))
}
cisBinNumber <-(sum(chrlens^2)-length(all_bins))/2
transBinNumber <- upperhalfBinNumber-cisBinNumber
diagonalProb <- sum(relative_coverage^2)
if(cistrans=='all'){
probabilityCorrection <- if(removeDiagonal){1/(1-diagonalProb)}else{1}
}
if(cistrans=='cis'){
probabilityCorrection <- upperhalfBinNumber/cisBinNumber
}
if(cistrans=='trans'){
probabilityCorrection <- upperhalfBinNumber/transBinNumber
}
#calculate probability of random interaction as a product of the relative coverages of individual bins*2
binned_df_filtered$probability <- binned_df_filtered$cov1*binned_df_filtered$cov2*2*probabilityCorrection
#number of expected reads by chance
binned_df_filtered$predicted <- binned_df_filtered$probability * numberOfReadPairs
#calculate cumulative binomial test for observed number of reads given the probability of seeing a random read
if(parallel)
{
requireNamespace("parallel")
if(nrow(binned_df_filtered)>1e8)
{
t <- ceiling(nrow(binned_df_filtered)/1e8)
dfList <- list()
dfList[[1]] <- binned_df_filtered[1:1e8,]
for(i in 2:t){
dfList[[i]] <- binned_df_filtered[(((i-1)*1e8)+1):min((i*1e8),nrow(binned_df_filtered)),]
}
dtList <- lapply(dfList, function(x) as.data.frame(t(cbind(as.numeric(x[["frequencies"]]),
as.numeric(x[["probability"]])))))
pvalues=list()
for(i in 1:length(dtList)){
pvalues[[i]] <-unlist(parallel::mclapply(dtList[[i]], function(x)
{
binom.test(x[1], numberOfReadPairs, x[2], alternative = "greater")$p.value
},
mc.cores=cores))
}
pvals=unlist(pvalues)
binned_df_filtered$pvalue <- pvals
}else{
binomParams <- as.data.frame(t(cbind(
as.numeric(binned_df_filtered[["frequencies"]]),
as.numeric(binned_df_filtered[["probability"]]
))))
binned_df_filtered$pvalue <- unlist(parallel::mclapply(binomParams, function(x)
{
binom.test(x[1], numberOfReadPairs, x[2], alternative = "greater")$p.value
},
mc.cores=cores))
}
}else{
if(nrow(binned_df_filtered)>1e8)
{
t <- ceiling(nrow(binned_df_filtered)/1e8)
dfList <- list()
dfList[[1]] <- binned_df_filtered[1:1e8,]
for(i in 2:t){
dfList[[i]] <- binned_df_filtered[(((i-1)*1e8)+1):min((i*1e8),nrow(binned_df_filtered)),]
}
pvalues=list()
for(i in 1:length(dfList)){
pvalues[[i]] <-apply(dfList[[i]], 1, function(x)
{
binom.test(as.numeric(x[["frequencies"]]), numberOfReadPairs, as.numeric(x[["probability"]]), alternative = "greater")$p.value
}
)
}
pvals=unlist(pvalues)
binned_df_filtered$pvalue <- pvals
}else{
binned_df_filtered$pvalue <- apply(binned_df_filtered, 1, function(x)
{
binom.test(as.numeric(x[["frequencies"]]), numberOfReadPairs, as.numeric(x[["probability"]]), alternative = "greater")$p.value
}
)
}
}
#observed over expected log ratio
binned_df_filtered$logFoldChange <- log2(binned_df_filtered$frequencies/binned_df_filtered$predicted)
#multiple testing correction separately for matrices with all interactions/only cis/only transs
if(cistrans=='all'){
binned_df_filtered$qvalue <- if(removeDiagonal){p.adjust(binned_df_filtered$pvalue, method = "BH", n=upperhalfBinNumber)}else{p.adjust(binned_df_filtered$pvalue, method = "BH", n=upperhalfBinNumber+length(all_bins))}
}
if(cistrans=='cis'){
binned_df_filtered$qvalue <- if(removeDiagonal){p.adjust(binned_df_filtered$pvalue, method = "BH", n=cisBinNumber)}else{p.adjust(binned_df_filtered$pvalue, method = "BH", n=cisBinNumber+length(all_bins))}
}
if(cistrans=='trans'){
binned_df_filtered$qvalue <- p.adjust(binned_df_filtered$pvalue, method = "BH", n=transBinNumber)
}
test <- data.frame(binned_df_filtered)
test[,"pvalue"] <- test$pvalue
pval.plot <- ggplot(test,aes(x=pvalue))
tryCatch(
{
x11()
print(pval.plot + geom_density())
},
error=function(cond) {
message("No interactive plot, try saving image")
message(cond)
return(tryCatch(
{
pdf(file=paste(sampleName,"pvalue_distribution.pdf",sep="_"))
print(pval.plot + geom_density())
dev.off()
},
error=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
},
warning=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
}
))
},
warning=function(cond) {
message("No interactive plot, try saving image")
message(cond)
return(tryCatch(
{
pdf(file=paste(sampleName,"pvalue_distribution.pdf",sep="_"))
print(pval.plot + geom_density())
dev.off()
},
error=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
},
warning=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
}
))
})
binned_df_filtered=binned_df_filtered[,c('chr1','locus1','chr2','locus2','cov1','cov2','probability', 'predicted','frequencies', 'pvalue','qvalue','logFoldChange')]
colnames(binned_df_filtered)=c('chr1','locus1','chr2','locus2','relCoverage1','relCoverage2','probability', 'expected','readCount', 'pvalue','qvalue','logObservedOverExpected')
return(binned_df_filtered)
}
####################################################################################################################################
####################################################################################################################################
## run GOTHiC
GOTHiC <- function(fileName1, fileName2, sampleName, res, BSgenomeName='BSgenome.Hsapiens.UCSC.hg19', genome=BSgenome.Hsapiens.UCSC.hg19, restrictionSite='A^AGCTT', enzyme='HindIII',cistrans='all',filterdist=10000, DUPLICATETHRESHOLD=1, fileType='BAM', parallel=FALSE, cores=NULL){
if(file.exists(paste(sampleName,"paired_filtered",sep="_"))){
message("Loading paired reads file ...")
load(paste(sampleName,"paired_filtered",sep="_"))
pairedReadsFile <- filtered
}else{
message("Pairing reads")
pairedReadsFile <- pairReads(fileName1, fileName2, sampleName, DUPLICATETHRESHOLD=1, fileType=fileType)
}
if (file.exists(paste("interactingLoci",sampleName,sep="_"))){
message("Loading mapped reads file ...")
load(paste("interactingLoci",sampleName,sep="_"))
interactions <- interactingLoci
}else{
message("Mapping reads to restriction sites")
interactions <- mapReadsToRestrictionSites(pairedReadsFile, sampleName, BSgenomeName, genome, restrictionSite, enzyme, parallel, cores)
}
message("Computing binomial ...")
binom <- .binomialHiC(interactions, res,sampleName, BSgenomeName, genome, restrictionSite, enzyme, parallel, cores,cistrans=cistrans,filterdist=filterdist)
return(binom)
}
GOTHiChicup <-function(fileName, sampleName, res, restrictionFile, cistrans='all', parallel=FALSE, cores=NULL)
{
interactions <- .importHicup(fileName, checkConsistency=TRUE, fileType=ifelse(grepl("\\.bam$", fileName)|grepl("\\.sam$", fileName), "bam", "table"))
interactions <- .mapHicupToRestrictionFragment(interactions, restrictionFile)
interactions <- .binInteractions(interactions, res)
binom <- .binomialHiChicup(interactions, restrictionFile, sampleName, cistrans, parallel, cores)
return(binom)
}
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GOTHiC documentation built on Nov. 8, 2020, 8:25 p.m.
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Defines functions GOTHiChicup GOTHiC .binomialHiC .binomialHiChicup .mapHicupToRestrictionFragment .getHindIIIsitesFromHicup .importHicup .fixChromosomeNames .binInteractions .countDuplicates mapReadsToRestrictionSites .exportCoverage .findOverlaps.parallel .strandAsSignedNumber .findOverlaps.circle .putRangesOnFirstCircle .getRestrictionSitesFromBSgenome pairReads .filterOutDuplicates .countDuplicatesOfRaw .onlyPairing
Documented in GOTHiC GOTHiChicup mapReadsToRestrictionSites pairReads
#Copyright: Bori Gerle (gerle@ebi.ac.uk), EMBL-EBI, 2011
#set global variables
globalVariables(c("BSgenome.Hsapiens.UCSC.hg19", "V1", "chr1", "chr2", "filtered", "frequencies", "int1", "int2", "interactingLoci", "locus1", "locus2", "pvalue", "resGR"))
.onlyPairing <- function(fileName1, fileName2, sampleName, fileType)
{
#DUPLICATETHRESHOLD: maximum amount of duplicated paired-end reads allowed (over that value it is expected to be PCR bias)
if(fileType=="Table"){
reads1=read.table(fileName1, header=F, sep='\t', colClasses=c(rep('character', times=3), 'numeric', rep("NULL", times=4)))
reads1_1=GRanges(seqnames=reads1$V3, ranges=IRanges(start=reads1$V4, end=reads1$V4), strand=reads1$V2, id=sapply(strsplit(reads1$V1, ' '), '[[', 2))
reads2=read.table(fileName2, header=F, sep='\t', colClasses=c(rep('character', times=3), 'numeric', rep("NULL", times=4)))
reads2_1=GRanges(seqnames=reads2$V3, ranges=IRanges(start=reads2$V4, end=reads2$V4), strand=reads2$V2, id=sapply(strsplit(reads2$V1, ' '), '[[', 2))
id1=as.character(elementMetadata(reads1_1)$id)
id2=as.character(elementMetadata(reads2_1)$id)
ids1=id1
ids2=id2
}
if(fileType%in%c("Bowtie", "BAM")){
if(fileType=="Bowtie"){
reads1=readAligned(fileName1,type=fileType) ## returns identifier, strand, chromosome, position read, quality
reads1_1=as(reads1,"GRanges") ## Grange object containing 4 slots, seqnames, ranges, strand, elementMetadata (data.frame)
reads2=readAligned(fileName2,type=fileType)
reads2_1=as(reads2,"GRanges")
#match IDs to find paired reads where both ends were mapped
id1=as.character(elementMetadata(reads1_1)$id)
id2=as.character(elementMetadata(reads2_1)$id)
}
if(fileType=='BAM'){
what=c("qname","rname","pos","qwidth", "strand")
param=ScanBamParam(what=what)
reads1=scanBam(fileName1, param=param)
reads2=scanBam(fileName2, param=param)
id1=reads1[[1]]$qname
id2=reads2[[1]]$qname
reads1_1=GRanges(seqnames=reads1[[1]]$rname, ranges=IRanges(start=reads1[[1]]$pos, end=reads1[[1]]$pos+reads1[[1]]$qwidth), strand=reads1[[1]]$strand)
reads2_1=GRanges(seqnames=reads2[[1]]$rname, ranges=IRanges(start=reads2[[1]]$pos, end=reads2[[1]]$pos+reads2[[1]]$qwidth), strand=reads2[[1]]$strand)
if(length(grep('SRR',id1[1]))==1|length(grep('ERR',id1[1]))==1){
ids1=id1
ids2=id2
elementMetadata(reads1_1)$id = id1
elementMetadata(reads2_1)$id = id2
}else
{
ids1 = sapply(strsplit(id1, '\\-'), '[[', 2)
ids2 = sapply(strsplit(id2, '\\-'), '[[', 2)
elementMetadata(reads1_1)$id = ids1
elementMetadata(reads2_1)$id = ids2}
}else
{
#old fastq files use /1 and /2 to differentiate read pairs, CASAVA 1.8 separates members of a pair with spaces
firsttwo=sapply(strsplit(id1[1:2], '[/ ]'), '[[', 1)
if(firsttwo[1]==firsttwo[2]){
ids1 = sapply(strsplit(id1, ' '), '[[', 1)
ids2 = sapply(strsplit(id2, ' '), '[[', 1)
}else
{
ids1 = sapply(strsplit(id1, '[/ ]'), '[[', 1)
ids2 = sapply(strsplit(id2, '[/ ]'), '[[', 1)
}
}
}
rm(id1,id2)
s1=which(ids1%in%ids2)
s2=which(ids2%in%ids1)
paired_reads_1=reads1_1[s1]
rm(reads1_1)
paired_reads_1=paired_reads_1[order(elementMetadata(paired_reads_1)$id)]
paired_reads_2=reads2_1[s2]
rm(reads2_1)
paired_reads_2=paired_reads_2[order(elementMetadata(paired_reads_2)$id)]
paired_df=cbind(as.character(seqnames(paired_reads_1)),as.character(start(ranges(paired_reads_1))),as.character(end(ranges(paired_reads_1))),as.character(seqnames(paired_reads_2)),as.character(start(ranges(paired_reads_2))),as.character(end(ranges(paired_reads_2))))
colnames(paired_df)=c("chr1","start1","end1", "chr2","start2","end2")
#save paired reads in GRanges objects and data.frame
paired_df=data.frame(paired_df,frequencies=rep(1,times=nrow(paired_df)),stringsAsFactors=FALSE)
return(list(paired_reads_1=paired_reads_1, paired_reads_2=paired_reads_2, paired_df=paired_df,s1=s1,s2=s2))
}
.countDuplicatesOfRaw <- function(paired_df, sampleName)
{
#count how many times we have the exact same read pairs to be able to remove PCR duplicates
colus=c("chr1","start1","end1", "chr2","start2","end2")
vec <- do.call("paste",c(paired_df[colus],sep="_"))
names(vec) <- c(1:length(vec))
dup <- duplicated(vec)
uniq <-vec[!dup]
uniqfreq <- rep(1, times=length(uniq))
uniq=cbind(uniq,uniqfreq)
colnames(uniq)=c("interaction","freqs")
vec1 <- vec[dup]
vec1 <- sort(vec1)
freq <- cbind(vec1,as.numeric(sequence(rle(vec1)$lengths))+1)
colnames(freq)=c("interaction","freqs")
vec=rbind(uniq, freq)
vec <- vec[order(as.numeric(rownames(vec))),]
paired_df$frequencies <- as.numeric(vec[,2])
freqs <- as.numeric(vec[,2])
freqstable <- table(freqs)
l=c()
for(i in 1:length(freqstable-1)){
l[i]=freqstable[i]==freqstable[i+1]
}
p=freqstable[l]
p=p[1]
ths=as.numeric(names(p))
return(list(paired_df=paired_df,freqs=freqs,ths=ths))
}
.filterOutDuplicates <- function(paired_df, paired_reads_1, paired_reads_2, sampleName, DUPLICATETHRESHOLD)
{
#DUPLICATETHRESHOLD: maximum amount of duplicated paired-end reads allowed (over that value it is expected to be PCR bias)
#remove readpairs that are present in more replicates than the DUPLICATETHRESHOLD - keep as many of the reads as the threshold
dth <- which(paired_df$frequencies<(DUPLICATETHRESHOLD+1))
paired_reads_1<-paired_reads_1[dth]
paired_reads_2<-paired_reads_2[dth]
return(list(paired_reads_1=paired_reads_1,paired_reads_2=paired_reads_2))
}
pairReads <- function(fileName1, fileName2, sampleName, DUPLICATETHRESHOLD=1, fileType='BAM')
{
oP=.onlyPairing(fileName1, fileName2, sampleName, fileType)
paired_reads_1=oP$paired_reads_1
paired_reads_2=oP$paired_reads_2
paired_df=oP$paired_df
s1=oP$s1
s2=oP$s2
cDoR=.countDuplicatesOfRaw(paired_df, sampleName)
paired_df=cDoR$paired_df
freqs=cDoR$freqs
ths=cDoR$ths
filtered=.filterOutDuplicates(paired_df, paired_reads_1, paired_reads_2, sampleName, DUPLICATETHRESHOLD)
save(filtered,file=paste(sampleName,"paired_filtered",sep="_"))
return(filtered)
}
.getRestrictionSitesFromBSgenome <- function(BSgenomeName, genome, restrictionSite, enzyme)
{
#check if the genome needed is already installed and install from BioConductor if not
iG=installed.genomes()
if(!BSgenomeName%in%iG){
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install(BSgenomeName)
}
library(BSgenomeName,character.only=TRUE)
chrEnds <- seqlengths(genome)
#find out how many base pairs from the 5 end of the restriction site the enzyme cuts
restRemain <- regexpr("\\^",restrictionSite)[1]-1
#restriction site as regular expression
rSite <- sub('\\^', '', restrictionSite)
#find locations where restriction enzyme cuts for all chromosomes
params <- new("BSParams",X=genome, FUN=matchPattern,simplify=TRUE)
resSite <- bsapply(params, pattern = rSite)
starts <- lapply(resSite, function(x){c(1,(start(x)+restRemain))})
lengths <- lapply(resSite, function(x){length(x)+1})
chrs <- lapply(1:length(names(resSite)),function(i){rep(names(resSite)[i],times=lengths[[i]])})
ends <- lapply(1:length(names(resSite)), function(i){c(start(resSite[[i]])+restRemain-1,chrEnds[i])})
starts <- unlist(starts)
chrs <- unlist(chrs)
ends <- unlist(ends)
#save restriction sites as GRanges
resGR <- GRanges(seqnames=chrs, ranges=IRanges(start=starts, end=ends))
save(resGR,file=paste("Digest",BSgenomeName,enzyme,sep="_"))
return(resGR)
}
#functions for parallel version of findOverlaps
.putRangesOnFirstCircle <- function(x, circle.length)
{
x_start0 <- start(x) - 1L # 0-based start
x_shift0 <- x_start0 %% circle.length - x_start0
shift(x, x_shift0)
}
.findOverlaps.circle <- function(circle.length, query, subject,
maxgap, minoverlap, type)
{
if (is.na(circle.length))
return(findOverlaps(query, subject,
maxgap=maxgap, minoverlap=minoverlap,
type=type, select="all"))
if (type != "any")
stop("overlap type \"", type, "\" is not yet supported ",
"for circular sequence ", names(circle.length))
subject0 <- .putRangesOnFirstCircle(subject, circle.length)
inttree0 <- NCList(subject0)
query0 <- .putRangesOnFirstCircle(query, circle.length)
hits00 <- findOverlaps(query0, inttree0,
maxgap=maxgap, minoverlap=minoverlap,
type=type, select="all")
query1 <- shift(query0, circle.length)
hits10 <- findOverlaps(query1, inttree0,
maxgap=maxgap, minoverlap=minoverlap,
type=type, select="all")
subject1 <- shift(subject0, circle.length)
hits01 <- findOverlaps(query0, subject1,
maxgap=maxgap, minoverlap=minoverlap,
type=type, select="all")
## Merge 'hits00', 'hits10' and 'hits01'.
union(union(hits00, hits10), hits01)
}
.strandAsSignedNumber <- function(x)
{
tmp <- as.integer(runValue(x))
idx <- tmp >= 2L
tmp[idx] <- tmp[idx] - 3L
runValue(x) <- tmp
as.vector(x)
}
.findOverlaps.parallel <- function(query, subject, maxgap=0L, minoverlap=1L,
type=c("any", "start", "end", "within", "equal"),
select=c("all", "first", "last", "arbitrary"),
ignore.strand=TRUE, mc.cores=1, mc.preschedule = TRUE)
{
if (!isSingleNumber(maxgap) || maxgap < 0L)
stop("'maxgap' must be a non-negative integer")
type <- match.arg(type)
select <- match.arg(select)
## merge() also checks that 'query' and 'subject' are based on the
## same reference genome.
seqinfo <- merge(seqinfo(query), seqinfo(subject))
q_len <- length(query)
s_len <- length(subject)
q_seqnames <- seqnames(query)
s_seqnames <- seqnames(subject)
q_seqlevels <- levels(q_seqnames)
s_seqlevels <- levels(s_seqnames)
q_splitranges <- splitRanges(q_seqnames)
s_splitranges <- splitRanges(s_seqnames)
q_ranges <- unname(ranges(query))
s_ranges <- unname(ranges(subject))
if (ignore.strand) {
q_strand <- rep.int(1L, q_len)
s_strand <- rep.int(1L, s_len)
} else {
q_strand <- .strandAsSignedNumber(strand(query))
s_strand <- .strandAsSignedNumber(strand(subject))
}
common_seqlevels <- intersect(q_seqlevels, s_seqlevels)
results <- parallel::mclapply(common_seqlevels,
function(seqlevel)
{
if (isCircular(seqinfo)[seqlevel] %in% TRUE) {
circle.length <- seqlengths(seqinfo)[seqlevel]
} else {
circle.length <- NA
}
q_idx <- q_splitranges[[seqlevel]]
s_idx <- s_splitranges[[seqlevel]]
hits <- .findOverlaps.circle(circle.length,
q_ranges[q_idx],
s_ranges[s_idx],
maxgap, minoverlap, type)
q_hits <- queryHits(hits)
s_hits <- subjectHits(hits)
compatible_strand <- q_strand[q_idx][q_hits] *
s_strand[s_idx][s_hits] != -1L
hits <- hits[compatible_strand]
remapHits(hits, Lnodes.remapping=as.integer(q_idx),
new.nLnode=q_len,
Rnodes.remapping=as.integer(s_idx),
new.nRnode=s_len)
}, mc.cores=mc.cores, mc.preschedule=mc.preschedule)
## Combine the results.
q_hits <- unlist(lapply(results, queryHits))
if (is.null(q_hits))
q_hits <- integer(0)
s_hits <- unlist(lapply(results, subjectHits))
if (is.null(s_hits))
s_hits <- integer(0)
if (select == "arbitrary") {
ans <- rep.int(NA_integer_, q_len)
ans[q_hits] <- s_hits
return(ans)
}
if (select == "first") {
ans <- rep.int(NA_integer_, q_len)
oo <- S4Vectors::orderIntegerPairs(q_hits, s_hits, decreasing=TRUE)
ans[q_hits[oo]] <- s_hits[oo]
return(ans)
}
oo <- S4Vectors::orderIntegerPairs(q_hits, s_hits)
q_hits <- q_hits[oo]
s_hits <- s_hits[oo]
if (select == "last") {
ans <- rep.int(NA_integer_, q_len)
ans[q_hits] <- s_hits
return(ans)
}
new2("Hits", queryHits=q_hits, subjectHits=s_hits,
queryLength=q_len, subjectLength=s_len,
check=FALSE)
}
.exportCoverage <- function(reads, sampleName)
{
coverage_reads = coverage(reads)
export.bedGraph(as(coverage_reads,"GRanges"), paste("coverage_", sampleName, ".bed", sep =""))
}
mapReadsToRestrictionSites <- function(pairedReadsFile, sampleName,BSgenomeName,genome,restrictionSite,enzyme,parallel=F, cores=1)
{
paired_reads_1 <- pairedReadsFile$paired_reads_1
paired_reads_2 <- pairedReadsFile$paired_reads_2
#calculate coverage
all_reads <- c(paired_reads_1, paired_reads_2)
.exportCoverage(all_reads, sampleName)
#take restriction sites
digestFile <- paste("Digest",BSgenomeName,enzyme,sep="_")
if(file.exists(digestFile))
{
load(paste("Digest",BSgenomeName,enzyme,sep="_"))
hindIIIRanges <- resGR
}else
{
hindIIIRanges <- .getRestrictionSitesFromBSgenome(BSgenomeName, genome,restrictionSite,enzyme)
}
#find HindIII sites
if(parallel)
{
requireNamespace("parallel")
hindIII_1 <- .findOverlaps.parallel(paired_reads_1, hindIIIRanges, mc.cores=cores, select="first")
hindIII_2 <- .findOverlaps.parallel(paired_reads_2, hindIIIRanges, mc.cores=cores, select="first")
}else
{
hindIII_1 <- findOverlaps(paired_reads_1, hindIIIRanges, select="first")
hindIII_2 <- findOverlaps(paired_reads_2, hindIIIRanges, select="first")
}
rm(paired_reads_1,paired_reads_2)
#take out reads where we cant assign hindIII site
validInteractions=!is.na(hindIII_1) & !is.na(hindIII_2)
hindIII_1=hindIII_1[validInteractions]
hindIII_2=hindIII_2[validInteractions]
hindIII1=pmin(hindIII_1, hindIII_2)
hindIII2=pmax(hindIII_1, hindIII_2)
#assign hindIII restriction sites to reads
loci1 <- hindIIIRanges[hindIII1]
loci2 <- hindIIIRanges[hindIII2]
interactingLoci <- GRangesList(locus1 = loci1, locus2 = loci2)
outputfilename <- paste("interactingLoci", sampleName, sep = "_")
save(interactingLoci, file=outputfilename)
return(interactingLoci)
}
#Copyright: Robert Sugar (robert.sugar@ebi.ac.uk), EMBL-EBI, 2011
#counts duplicate rows of a data frame
#returns a data frame with unique rows and the frequencies in the new row "frequencies"
#warning: data frame should be ordered!
.countDuplicates <- function(df, frequencyCol = c("frequencies", "frequency"), considerExistingFrequencies = any(frequencyCol %in% names(df)), ordered=FALSE)
{
if(considerExistingFrequencies)
{
frequencyCol <- frequencyCol[frequencyCol %in% names(df)][1]
if(is.null(df[[frequencyCol]]))
stop("frequencies column missing")
} else if(!considerExistingFrequencies)
{
frequencyCol <- frequencyCol[1]
df[, frequencyCol] <- 1 #one for all
}
#order
chrLocusColumns <- c("chr1", "locus1", "chr2", "locus2")
coverageColumns <- c("chr", "locus")
startEndColumns <- c("seqnames1", "start1", "end1", "strand1", "seqnames2", "start2", "end2", "strand2")
if(all(chrLocusColumns %in% names(df)))
{
df <- df[order(df$chr1, df$locus1, df$chr2, df$locus2), ]
} else
if(all(coverageColumns %in% names(df)))
{
df <- df[order(df$chr, df$locus), ]
} else
if(all(startEndColumns %in% names(df)))
{
df <- df[order(df$seqnames1, df$start1, df$end1, df$strand1, df$seqnames2, df$start2, df$end2, df$strand2), ]
} else #use all columns except for frequency
{
df <- df[do.call(order, df[, names(df)[-which(names(df) == frequencyCol)]]), ]
}
#add up the frequencies for the original and all duplicates of a row
duplicates <- c(duplicated(df[, -which(names(df) == frequencyCol)])) #look for duplicates (excluding frequencies column)
cumulative <- c(0, cumsum(df[, frequencyCol])) #the 0 is needed to consider not the first unique but the last duplicate
beforeFirstUnique <- cumulative[!c(duplicates, FALSE)] #the extra FALSE value is needed for the last group
dfUnique <- df[!duplicates, ]
dfUnique[, frequencyCol] = diff(beforeFirstUnique)
return(dfUnique)
}
#####bin interactions into desired bin size e.g. 1Mb########
.binInteractions <- function(interactions, resolution, frequencyCol = "frequencies", considerExistingFrequencies = frequencyCol %in% names(interactions))
{
if(!identical(colnames(interactions)[1:4], c("chr1", "locus1", "chr2", "locus2")))
stop("expecting columns chr1, locus1, chr2, locus2")
if(considerExistingFrequencies && sum(names(interactions) == "frequencies") == 0)
stop(paste("expecting column", frequencyCol))
#if resolution is given, bins will be calculated from interactions using the resolution
interactions$locus1 <- (interactions$locus1 %/% resolution) * resolution
interactions$locus2 <- (interactions$locus2 %/% resolution) * resolution
#put smaller locus first to make sure a-b and b-a interactions look the same
firstSmaller <- subset(interactions, (as.character(chr1) < as.character(chr2)) | (as.character(chr1) == (as.character(chr2)) & locus1 <= locus2))
secondSmaller <- subset(interactions, !((as.character(chr1) < as.character(chr2)) | (as.character(chr1) == (as.character(chr2)) & locus1 <= locus2)))
#flip first and second if second is smaller
cnames <- colnames(interactions)
cnames[1:4] <- c("chr2", "locus2", "chr1", "locus1")
setnames(secondSmaller,colnames(secondSmaller),cnames)
#stich them back together
interactions <- rbind(firstSmaller, secondSmaller)
# --------- count the number of interactions between bins -------
interactions <- interactions[order(interactions$chr1, interactions$locus1, interactions$chr2, interactions$locus2), ]
#bin it
interactions <- .countDuplicates(interactions, frequencyCol, considerExistingFrequencies)
return(interactions)
}
#############functions to import HiCUP filtered reads##########
.fixChromosomeNames <- function(chrnames)
{
#capital to small
chrnames <- paste0("chr", chrnames)
chrnames <- sub("CHR", "chr", chrnames)
chrnames <- sub("chrchr", "chr", chrnames)
}
.importHicup <- function(fileName, checkConsistency=TRUE, fileType=ifelse(grepl("\\.bam$", fileName)|grepl("\\.sam$", fileName), "bam", "table"), compressed=ifelse(grepl("\\.gz$", fileName), TRUE, FALSE))
{
#the output of hicup is a sam file, that looks like uniques_ORIGINALFILE_trunc.sam
#this has to be converted using the hicupToTable tool
if(compressed){
tbl <- read.table(gzfile(fileName))
#in the sam file the two ends of the interactions are consecutive rows
odd <- tbl[seq(1,nrow(tbl),2), ]
names(odd) <- c("id1", "flag1", "chr1", "locus1")
levels(odd$chr1) <- .fixChromosomeNames(levels(odd$chr1))
even <- tbl[seq(2,nrow(tbl),2), ]
names(even) <- c("id2", "flag2", "chr2", "locus2")
levels(even$chr2) <- .fixChromosomeNames(levels(even$chr2))
}else{
if(fileType=="table")
{
tbl <- read.table(fileName)
#in the sam file the two ends of the interactions are consecutive rows
odd <- tbl[seq(1,nrow(tbl),2), ]
names(odd) <- c("id1", "flag1", "chr1", "locus1")
levels(odd$chr1) <- .fixChromosomeNames(levels(odd$chr1))
even <- tbl[seq(2,nrow(tbl),2), ]
names(even) <- c("id2", "flag2", "chr2", "locus2")
levels(even$chr2) <- .fixChromosomeNames(levels(even$chr2))
} else if(fileType=="bam")
{
stop('imporHicup: HiCUP output BAM/SAM file needs to be converted into a table')
}
}
joined <- cbind(odd, even)
if(nrow(odd) != nrow(even)) stop('importHicup: reads must be paired in consecutive rows')
if(checkConsistency)
{
if(!all(joined$id1==joined$id2)) stop('importHicup: reads must be paired in consecutive rows')
}
return(joined[, c("chr1", "locus1", "chr2", "locus2")])
}
.getHindIIIsitesFromHicup <- function(fileName, asRanges = TRUE)
{
sites = read.table(fileName, stringsAsFactors=FALSE, header=TRUE, skip=1)
sites$chr <- sub("^", "chr", sites$Chromosome)
sites$chr <- sub("chrCHR", "chr", sites$chr) #sometimes there is CHR
sites$chr <- sub("chrchr", "chr", sites$chr) #sometimes there is CHR
sites$start <- sites$Fragment_Start_Position
sites$locus <- sites$Fragment_Start_Position
sites$end <- sites$Fragment_End_Position
sites <- sites[, c("chr", "locus", "start", "end")]
if(asRanges)
{
return(GRanges(seqnames=sites$chr, ranges=IRanges(start=sites$start, end=sites$end)))
}
}
.mapHicupToRestrictionFragment <- function(interactions, fragments)
{
source = GRanges(seqnames = Rle(interactions$chr1), ranges = IRanges(interactions$locus1, width = 1),strand = Rle("*", nrow(interactions)))
target = GRanges(seqnames = Rle(interactions$chr2), ranges = IRanges(interactions$locus2, width = 1),strand = Rle("*", nrow(interactions)))
if(is.character(fragments))
{
fragments <- .getHindIIIsitesFromHicup(fragments)
}
firstFragment <- findOverlaps(source, fragments, select="first")
secondFragment <- findOverlaps(target, fragments, select="first")
#filter out non-mapping loci
validInteractions <- !is.na(firstFragment) & !is.na(secondFragment)
firstFragment <- firstFragment[validInteractions]
secondFragment <- secondFragment[validInteractions]
#make sure A-B and B-A are treated together -> put fragment with smaller ID in front
sources <- pmin(firstFragment, secondFragment)
targets <- pmax(firstFragment, secondFragment)
interactions <- as.data.frame(cbind(as.character(seqnames(fragments[sources])), start(ranges(fragments[sources])), as.character(seqnames(fragments[targets])), start(ranges(fragments[targets])), rep(1, times=length(sources))), stringsAsFactors=FALSE)
colnames(interactions) <- c('chr1', 'locus1', 'chr2', 'locus2', 'frequencies')
interactions$locus1=as.numeric(interactions$locus1)
interactions$locus2=as.numeric(interactions$locus2)
interactions$frequencies=as.numeric(interactions$frequencies)
return(interactions)
}
.binomialHiChicup=function(hicupinteraction, restrictionFile, sampleName, cistrans='all', parallel=FALSE, cores=8, removeDiagonal=TRUE)
{
hindGR <- .getHindIIIsitesFromHicup(restrictionFile)
if(parallel)
{
requireNamespace("parallel")
print("running garbage collector before parallel fork")
gc()
}
binned_df_filtered <-hicupinteraction
binned_df_filtered$int1 <-paste(binned_df_filtered$chr1,binned_df_filtered$locus1,sep='_')
binned_df_filtered$int2 <-paste(binned_df_filtered$chr2,binned_df_filtered$locus2,sep='_')
#diagonal removal
if(removeDiagonal)
{
binned_df_filtered <- binned_df_filtered[binned_df_filtered$int1!=binned_df_filtered$int2,]
}
if(cistrans=='cis'){
binned_df_filtered <- binned_df_filtered[binned_df_filtered$chr1==binned_df_filtered$chr2,]
}
if(cistrans=='trans'){
binned_df_filtered <- binned_df_filtered[binned_df_filtered$chr1!=binned_df_filtered$chr2,]
}
#all read pairs used in binomial
numberOfReadPairs <- sum(binned_df_filtered$frequencies)
#calculate coverage
all_bins <- unique(c(unique(binned_df_filtered$int1), unique(binned_df_filtered$int2)))
all_bins <- sort(all_bins)
if(nrow(binned_df_filtered)>1e8)
{
t <- ceiling(nrow(binned_df_filtered)/1e8)
dfList <- list()
dfList[[1]] <- binned_df_filtered[1:1e8,]
for(i in 2:t){
dfList[[i]] <- binned_df_filtered[(((i-1)*1e8)+1):min((i*1e8),nrow(binned_df_filtered)),]
}
dtList <- lapply(dfList, data.table)
covAs <- lapply(dtList, function(x) x[,sum(frequencies), by=int1])
covBs <- lapply(dtList, function(x) x[,sum(frequencies), by=int2])
covAm <- do.call(rbind, covAs)
covBm <- do.call(rbind, covBs)
covA <- covAm[,sum(V1),by=int1]
covB <- covBm[,sum(V1),by=int2]
}else{
binned_dt=data.table(binned_df_filtered)
covA <- binned_dt[,sum(frequencies),by=int1]
covB <- binned_dt[,sum(frequencies),by=int2]
}
covA <- setkey(covA,key='int1')
setnames(covB, 1,'int1')
covB <- setkey(covB,key='int1')
cov=merge(covA,covB,all.x=TRUE,all.y=TRUE,by='int1')
cov$V1.x[is.na(cov$V1.x)]=0
cov$V1.y[is.na(cov$V1.y)]=0
cov$coverage=cov$V1.x+cov$V1.y
coverage=cov$coverage
names(coverage)=cov$int1
sumcov <- sum(coverage)
relative_coverage <- coverage/sumcov
names(relative_coverage)=names(coverage)
binned_df_filtered$coverage_source <- relative_coverage[binned_df_filtered$int1]
binned_df_filtered$coverage_target <- relative_coverage[binned_df_filtered$int2]
#probability correction assuming on average equal probabilities for all interactions
numberOfAllInteractions <- length(all_bins)^2
upperhalfBinNumber <- (length(all_bins)^2-length(all_bins))/2
if(cistrans!='all'){
chromos <- unique(binned_df_filtered$chr1)
chrlens <- c()
for(cr in chromos){
chrlens[cr] <- length(unique(c(unique(binned_df_filtered$locus1[binned_df_filtered$chr1==cr]),unique(binned_df_filtered$locus2[binned_df_filtered$chr2==cr]))))
}
cisBinNumber <-(sum(chrlens^2)-length(all_bins))/2
transBinNumber <- upperhalfBinNumber-cisBinNumber
}
diagonalProb <- sum(relative_coverage^2)
if(cistrans=='all'){
probabilityCorrection <- if(removeDiagonal){1/(1-diagonalProb)}else{1}
}
if(cistrans=='cis'){
probabilityCorrection <- upperhalfBinNumber/cisBinNumber
}
if(cistrans=='trans'){
probabilityCorrection <- upperhalfBinNumber/transBinNumber
}
binned_df_filtered$probabilityOfInteraction <- binned_df_filtered$coverage_source*binned_df_filtered$coverage_target*2*probabilityCorrection
binned_df_filtered$predicted <- binned_df_filtered$probabilityOfInteraction * numberOfReadPairs
if(parallel)
{
requireNamespace("parallel")
if(nrow(binned_df_filtered)>1e8)
{
t <- ceiling(nrow(binned_df_filtered)/1e8)
dfList <- list()
dfList[[1]] <- binned_df_filtered[1:1e8,]
for(i in 2:t){
dfList[[i]] <- binned_df_filtered[(((i-1)*1e8)+1):min((i*1e8),nrow(binned_df_filtered)),]
}
dtList <- lapply(dfList, function(x) as.data.frame(t(cbind(as.numeric(x[["frequencies"]]),
as.numeric(x[["probabilityOfInteraction"]])))))
pvalues=list()
for(i in 1:length(dtList)){
pvalues[[i]] <-unlist(parallel::mclapply(dtList[[i]], function(x)
{
binom.test(x[1], numberOfReadPairs, x[2], alternative = "greater")$p.value
},
mc.cores=cores))
}
pvals=unlist(pvalues)
binned_df_filtered$pvalue <- pvals
}else{
binomParams <- as.data.frame(t(cbind(
as.numeric(binned_df_filtered[["frequencies"]]),
as.numeric(binned_df_filtered[["probabilityOfInteraction"]]
))))
binned_df_filtered$pvalue <- unlist(parallel::mclapply(binomParams, function(x)
{
binom.test(x[1], numberOfReadPairs, x[2], alternative = "greater")$p.value
},
mc.cores=cores))
}
}else{
if(nrow(binned_df_filtered)>1e8)
{
t <- ceiling(nrow(binned_df_filtered)/1e8)
dfList <- list()
dfList[[1]] <- binned_df_filtered[1:1e8,]
for(i in 2:t){
dfList[[i]] <- binned_df_filtered[(((i-1)*1e8)+1):min((i*1e8),nrow(binned_df_filtered)),]
}
pvalues=list()
for(i in 1:length(dfList)){
pvalues[[i]] <-apply(dfList[[i]], 1, function(x)
{
binom.test(as.numeric(x[["frequencies"]]), numberOfReadPairs, as.numeric(x[["probabilityOfInteraction"]]), alternative = "greater")$p.value
}
)
}
pvals=unlist(pvalues)
binned_df_filtered$pvalue <- pvals
}else{
binned_df_filtered$pvalue <- apply(binned_df_filtered, 1, function(x)
{
binom.test(as.numeric(x[["frequencies"]]), numberOfReadPairs, as.numeric(x[["probabilityOfInteraction"]]), alternative = "greater")$p.value
}
)
}
}
#observed over expected log ratio
binned_df_filtered$logFoldChange <- log2(binned_df_filtered$frequencies/binned_df_filtered$predicted)
#multiple testing correction separately for matrices with all interactions/only cis/only transs
if(cistrans=='all'){
binned_df_filtered$qvalue <- if(removeDiagonal){p.adjust(binned_df_filtered$pvalue, method = "BH", n=upperhalfBinNumber)}else{p.adjust(binned_df_filtered$pvalue, method = "BH", n=upperhalfBinNumber+length(all_bins))}
}
if(cistrans=='cis'){
binned_df_filtered$qvalue <- if(removeDiagonal){p.adjust(binned_df_filtered$pvalue, method = "BH", n=cisBinNumber)}else{p.adjust(binned_df_filtered$pvalue, method = "BH", n=cisBinNumber+length(all_bins))}
}
if(cistrans=='trans'){
binned_df_filtered$qvalue <- p.adjust(binned_df_filtered$pvalue, method = "BH", n=transBinNumber)
}
test <- data.frame(binned_df_filtered)
test[,"pvalue"] <- test$pvalue
pval.plot <- ggplot(test,aes(x=pvalue))
tryCatch(
{
x11()
print(pval.plot + geom_density())
},
error=function(cond) {
message("No interactive plot, try saving image")
message(cond)
return(tryCatch(
{
pdf(file=paste(sampleName,"pvalue_distribution.pdf",sep="_"))
print(pval.plot + geom_density())
dev.off()
},
error=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
},
warning=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
}
))
},
warning=function(cond) {
message("No interactive plot, try saving image")
message(cond)
return(tryCatch(
{
pdf(file=paste(sampleName,"pvalue_distribution.pdf",sep="_"))
print(pval.plot + geom_density())
dev.off()
},
error=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
},
warning=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
}
))
})
binned_df_filtered=binned_df_filtered[,c('chr1','locus1','chr2','locus2','coverage_source','coverage_target','probabilityOfInteraction', 'predicted','frequencies', 'pvalue','qvalue','logFoldChange')]
colnames(binned_df_filtered)=c('chr1','locus1','chr2','locus2','relCoverage1','relCoverage2','probability', 'expected','readCount', 'pvalue','qvalue','logObservedOverExpected')
return(binned_df_filtered)
}
# Author: borbalagerle
###############################################################################
# take GenomicRangesList with interactions in locus1 and locus2
.binomialHiC=function(interactingLoci, res,sampleName,BSgenomeName, genome, restrictionSite, enzyme, parallel=F, cores=NULL,removeDiagonal=TRUE,cistrans='all',filterdist=10000)
{
#take restriction sites
digestFile <- paste("Digest",BSgenomeName,enzyme,sep="_")
if(file.exists(digestFile))
{
load(paste("Digest",BSgenomeName,enzyme,sep="_"))
hindIIIRanges <- resGR
}else
{
hindIIIRanges <- .getRestrictionSitesFromBSgenome(BSgenomeName, genome, restrictionSite, enzyme)
}
elementMetadata(hindIIIRanges)$score=1:length(hindIIIRanges)
#remove self-ligations and interactions between adjacent bins by assigning the fragment number to reads and remove those where the difference is smaller or equal to 1
x=findOverlaps(interactingLoci[[1]],hindIIIRanges,select='first')
y=findOverlaps(interactingLoci[[2]],hindIIIRanges,select='first')
interactingLoci1 <- interactingLoci[[1]]
interactingLoci2 <- interactingLoci[[2]]
interactingLoci1$score=hindIIIRanges$score[x]
interactingLoci2$score=hindIIIRanges$score[y]
interactingLoci <- GRangesList(locus1=interactingLoci1,locus2=interactingLoci2)
score=interactingLoci[[2]]$score-interactingLoci[[1]]$score
interactingLoci[[1]]=interactingLoci[[1]][abs(score)>1]
interactingLoci[[2]]=interactingLoci[[2]][abs(score)>1]
#filter for reads that are closer than 10kb to get rid of incomplete digest products
# df_int <- data.frame(as.vector(seqnames(interactingLoci[[1]])), start(ranges(interactingLoci[[1]])), as.vector(seqnames(interactingLoci[[2]])), start(ranges(interactingLoci[[2]])))
df_int <- data.frame(as.character(seqnames(interactingLoci[[1]])), start(ranges(interactingLoci[[1]])), as.character(seqnames(interactingLoci[[2]])), start(ranges(interactingLoci[[2]])), stringsAsFactors=FALSE)
colnames(df_int) <- c("chr1", "locus1", "chr2", "locus2")
df_filtered <- df_int
df_filtered$dist <-as.vector(ifelse(df_filtered$chr1 == df_filtered$chr2, abs(df_filtered$locus1 - df_filtered$locus2), Inf))
df_filtered <-df_filtered[df_filtered$dist>filterdist,]
df_filtered <-df_filtered[,1:4]
#bin interactions according to resolution
binned_df_filtered <- .binInteractions(df_filtered, res)
binned_df_filtered$int1 <-paste(binned_df_filtered$chr1,binned_df_filtered$locus1,sep='_')
binned_df_filtered$int2 <-paste(binned_df_filtered$chr2,binned_df_filtered$locus2,sep='_')
#diagonal removal - interactions within the same bin
if(removeDiagonal)
{
subs <-which(binned_df_filtered$int1!=binned_df_filtered$int2)
binned_df_filtered <- binned_df_filtered[subs,]
}
if(cistrans=='cis'){
subs <-which(binned_df_filtered$chr1==binned_df_filtered$chr2)
binned_df_filtered <- binned_df_filtered[subs,]
}
if(cistrans=='trans'){
subs <-which(binned_df_filtered$chr1!=binned_df_filtered$chr2)
binned_df_filtered <- binned_df_filtered[subs,]
}
#all read pairs used in binomial
numberOfReadPairs <- sum(binned_df_filtered$frequencies)
#calculate coverage
all_bins <- unique(c(unique(binned_df_filtered$int1), unique(binned_df_filtered$int2)))
all_bins <- sort(all_bins)
if(nrow(binned_df_filtered)>1e8)
{
t <- ceiling(nrow(binned_df_filtered)/1e8)
dfList <- list()
dfList[[1]] <- binned_df_filtered[1:1e8,]
for(i in 2:t){
dfList[[i]] <- binned_df_filtered[(((i-1)*1e8)+1):min((i*1e8),nrow(binned_df_filtered)),]
}
dtList <- lapply(dfList, data.table)
covAs <- lapply(dtList, function(x) x[,sum(frequencies), by=int1])
covBs <- lapply(dtList, function(x) x[,sum(frequencies), by=int2])
covAm <- do.call(rbind, covAs)
covBm <- do.call(rbind, covBs)
covA <- covAm[,sum(V1),by=int1]
covB <- covBm[,sum(V1),by=int2]
}else{
binned_dt=data.table(binned_df_filtered)
covA <- binned_dt[,sum(frequencies),by=int1]
covB <- binned_dt[,sum(frequencies),by=int2]
}
covA <- setkey(covA,key='int1')
setnames(covB, 1,'int1')
covB <- setkey(covB,key='int1')
cov=merge(covA,covB,all.x=TRUE,all.y=TRUE,by='int1')
cov$V1.x[is.na(cov$V1.x)]=0
cov$V1.y[is.na(cov$V1.y)]=0
cov$coverage=cov$V1.x+cov$V1.y
coverage=cov$coverage
names(coverage)=cov$int1
sumcov <- sum(coverage)
relative_coverage <- coverage/sumcov
names(relative_coverage)=names(coverage)
binned_df_filtered$cov1 <- relative_coverage[binned_df_filtered$int1]
binned_df_filtered$cov2 <- relative_coverage[binned_df_filtered$int2]
#probability correction assuming on average equal probabilities for all interactions
numberOfAllInteractions <- length(all_bins)^2
upperhalfBinNumber <- (length(all_bins)^2-length(all_bins))/2
chromos <- unique(binned_df_filtered$chr1)
chrlens <- c()
for(cr in chromos){
chrlens[cr] <- length(unique(c(unique(binned_df_filtered$locus1[binned_df_filtered$chr1==cr]),unique(binned_df_filtered$locus2[binned_df_filtered$chr2==cr]))))
}
cisBinNumber <-(sum(chrlens^2)-length(all_bins))/2
transBinNumber <- upperhalfBinNumber-cisBinNumber
diagonalProb <- sum(relative_coverage^2)
if(cistrans=='all'){
probabilityCorrection <- if(removeDiagonal){1/(1-diagonalProb)}else{1}
}
if(cistrans=='cis'){
probabilityCorrection <- upperhalfBinNumber/cisBinNumber
}
if(cistrans=='trans'){
probabilityCorrection <- upperhalfBinNumber/transBinNumber
}
#calculate probability of random interaction as a product of the relative coverages of individual bins*2
binned_df_filtered$probability <- binned_df_filtered$cov1*binned_df_filtered$cov2*2*probabilityCorrection
#number of expected reads by chance
binned_df_filtered$predicted <- binned_df_filtered$probability * numberOfReadPairs
#calculate cumulative binomial test for observed number of reads given the probability of seeing a random read
if(parallel)
{
requireNamespace("parallel")
if(nrow(binned_df_filtered)>1e8)
{
t <- ceiling(nrow(binned_df_filtered)/1e8)
dfList <- list()
dfList[[1]] <- binned_df_filtered[1:1e8,]
for(i in 2:t){
dfList[[i]] <- binned_df_filtered[(((i-1)*1e8)+1):min((i*1e8),nrow(binned_df_filtered)),]
}
dtList <- lapply(dfList, function(x) as.data.frame(t(cbind(as.numeric(x[["frequencies"]]),
as.numeric(x[["probability"]])))))
pvalues=list()
for(i in 1:length(dtList)){
pvalues[[i]] <-unlist(parallel::mclapply(dtList[[i]], function(x)
{
binom.test(x[1], numberOfReadPairs, x[2], alternative = "greater")$p.value
},
mc.cores=cores))
}
pvals=unlist(pvalues)
binned_df_filtered$pvalue <- pvals
}else{
binomParams <- as.data.frame(t(cbind(
as.numeric(binned_df_filtered[["frequencies"]]),
as.numeric(binned_df_filtered[["probability"]]
))))
binned_df_filtered$pvalue <- unlist(parallel::mclapply(binomParams, function(x)
{
binom.test(x[1], numberOfReadPairs, x[2], alternative = "greater")$p.value
},
mc.cores=cores))
}
}else{
if(nrow(binned_df_filtered)>1e8)
{
t <- ceiling(nrow(binned_df_filtered)/1e8)
dfList <- list()
dfList[[1]] <- binned_df_filtered[1:1e8,]
for(i in 2:t){
dfList[[i]] <- binned_df_filtered[(((i-1)*1e8)+1):min((i*1e8),nrow(binned_df_filtered)),]
}
pvalues=list()
for(i in 1:length(dfList)){
pvalues[[i]] <-apply(dfList[[i]], 1, function(x)
{
binom.test(as.numeric(x[["frequencies"]]), numberOfReadPairs, as.numeric(x[["probability"]]), alternative = "greater")$p.value
}
)
}
pvals=unlist(pvalues)
binned_df_filtered$pvalue <- pvals
}else{
binned_df_filtered$pvalue <- apply(binned_df_filtered, 1, function(x)
{
binom.test(as.numeric(x[["frequencies"]]), numberOfReadPairs, as.numeric(x[["probability"]]), alternative = "greater")$p.value
}
)
}
}
#observed over expected log ratio
binned_df_filtered$logFoldChange <- log2(binned_df_filtered$frequencies/binned_df_filtered$predicted)
#multiple testing correction separately for matrices with all interactions/only cis/only transs
if(cistrans=='all'){
binned_df_filtered$qvalue <- if(removeDiagonal){p.adjust(binned_df_filtered$pvalue, method = "BH", n=upperhalfBinNumber)}else{p.adjust(binned_df_filtered$pvalue, method = "BH", n=upperhalfBinNumber+length(all_bins))}
}
if(cistrans=='cis'){
binned_df_filtered$qvalue <- if(removeDiagonal){p.adjust(binned_df_filtered$pvalue, method = "BH", n=cisBinNumber)}else{p.adjust(binned_df_filtered$pvalue, method = "BH", n=cisBinNumber+length(all_bins))}
}
if(cistrans=='trans'){
binned_df_filtered$qvalue <- p.adjust(binned_df_filtered$pvalue, method = "BH", n=transBinNumber)
}
test <- data.frame(binned_df_filtered)
test[,"pvalue"] <- test$pvalue
pval.plot <- ggplot(test,aes(x=pvalue))
tryCatch(
{
x11()
print(pval.plot + geom_density())
},
error=function(cond) {
message("No interactive plot, try saving image")
message(cond)
return(tryCatch(
{
pdf(file=paste(sampleName,"pvalue_distribution.pdf",sep="_"))
print(pval.plot + geom_density())
dev.off()
},
error=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
},
warning=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
}
))
},
warning=function(cond) {
message("No interactive plot, try saving image")
message(cond)
return(tryCatch(
{
pdf(file=paste(sampleName,"pvalue_distribution.pdf",sep="_"))
print(pval.plot + geom_density())
dev.off()
},
error=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
},
warning=function(cond2) {
message("No pdf output, quality assesment plot is not produced")
message(cond2)
return(NA)
}
))
})
binned_df_filtered=binned_df_filtered[,c('chr1','locus1','chr2','locus2','cov1','cov2','probability', 'predicted','frequencies', 'pvalue','qvalue','logFoldChange')]
colnames(binned_df_filtered)=c('chr1','locus1','chr2','locus2','relCoverage1','relCoverage2','probability', 'expected','readCount', 'pvalue','qvalue','logObservedOverExpected')
return(binned_df_filtered)
}
####################################################################################################################################
####################################################################################################################################
## run GOTHiC
GOTHiC <- function(fileName1, fileName2, sampleName, res, BSgenomeName='BSgenome.Hsapiens.UCSC.hg19', genome=BSgenome.Hsapiens.UCSC.hg19, restrictionSite='A^AGCTT', enzyme='HindIII',cistrans='all',filterdist=10000, DUPLICATETHRESHOLD=1, fileType='BAM', parallel=FALSE, cores=NULL){
if(file.exists(paste(sampleName,"paired_filtered",sep="_"))){
message("Loading paired reads file ...")
load(paste(sampleName,"paired_filtered",sep="_"))
pairedReadsFile <- filtered
}else{
message("Pairing reads")
pairedReadsFile <- pairReads(fileName1, fileName2, sampleName, DUPLICATETHRESHOLD=1, fileType=fileType)
}
if (file.exists(paste("interactingLoci",sampleName,sep="_"))){
message("Loading mapped reads file ...")
load(paste("interactingLoci",sampleName,sep="_"))
interactions <- interactingLoci
}else{
message("Mapping reads to restriction sites")
interactions <- mapReadsToRestrictionSites(pairedReadsFile, sampleName, BSgenomeName, genome, restrictionSite, enzyme, parallel, cores)
}
message("Computing binomial ...")
binom <- .binomialHiC(interactions, res,sampleName, BSgenomeName, genome, restrictionSite, enzyme, parallel, cores,cistrans=cistrans,filterdist=filterdist)
return(binom)
}
GOTHiChicup <-function(fileName, sampleName, res, restrictionFile, cistrans='all', parallel=FALSE, cores=NULL)
{
interactions <- .importHicup(fileName, checkConsistency=TRUE, fileType=ifelse(grepl("\\.bam$", fileName)|grepl("\\.sam$", fileName), "bam", "table"))
interactions <- .mapHicupToRestrictionFragment(interactions, restrictionFile)
interactions <- .binInteractions(interactions, res)
binom <- .binomialHiChicup(interactions, restrictionFile, sampleName, cistrans, parallel, cores)
return(binom)
}
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