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hictrans.v3.R
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hictrans.v3.R
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library(data.table)
library(hashmap)
suppressMessages(library(changepoint))
library(hashmap)
library(optparse)
library(Rcpp)
library(caTools)
library(depmixS4)
library(DEoptimR)
## Change the path of build_matrix program when running from different folder##
buildMatrixPath <- "/home/abhijit/overflow/proj_overflow/HiCProcessingPipeLine/Cancer_Cell_Lines/HiCtrans_Calls/HiCtrans/build_matrix"
buildMatrixPath <- normalizePath(buildMatrixPath)
############### Get breakpoint calls at restriction level resolution #############
## Cluster the breakpoints
RE_HClust <- function(box.df,cl.A,cl.B) {
box.df <- box.df[,c(1:6)]
colnames(box.df) <- c("chrA","BoundaryAS","BoundaryAE","chrB","BoundaryBS","BoundaryBE")
chrA.dist <- matrix(ncol=nrow(box.df),nrow=nrow(box.df))
chrB.dist <- matrix(ncol=nrow(box.df),nrow=nrow(box.df))
i <- 1
while (i <= nrow(box.df)) {
chrA.AS_i <- box.df$BoundaryAS[i]
chrA.AE_i <- box.df$BoundaryAE[i]
chrB.BS_i <- box.df$BoundaryBS[i]
chrB.BE_i <- box.df$BoundaryBE[i]
j <- 1
while (j <= nrow(box.df)) {
chrA.AS_j <- box.df$BoundaryAS[j]
chrA.AE_j <- box.df$BoundaryAE[j]
chrB.BS_j <- box.df$BoundaryBS[j]
chrB.BE_j <- box.df$BoundaryBE[j]
chrA.AS_AS.ij <- abs(chrA.AS_i - chrA.AS_j)
chrA.AE_AS.ij <- abs(chrA.AE_i - chrA.AS_j)
chrA.AS_AE.ij <- abs(chrA.AS_i - chrA.AE_j)
chrA.AE_AE.ij <- abs(chrA.AE_i - chrA.AE_j)
chrB.BS_BS.ij <- abs(chrB.BS_i - chrB.BS_j)
chrB.BE_BS.ij <- abs(chrB.BE_i - chrB.BS_j)
chrB.BS_BE.ij <- abs(chrB.BS_i - chrB.BE_j)
chrB.BE_BE.ij <- abs(chrB.BE_i - chrB.BE_j)
chrA.dist[i,j] <- min(chrA.AS_AS.ij,chrA.AE_AS.ij,chrA.AS_AE.ij,chrA.AE_AE.ij)
chrB.dist[i,j] <- min(chrB.BS_BS.ij,chrB.BE_BS.ij,chrB.BS_BE.ij,chrB.BE_BE.ij)
j <- j + 1
}
i <- i + 1
}
if (nrow(chrA.dist) > 1 & nrow(chrB.dist) > 1) {
chrA.hclust <- hclust(as.dist(chrA.dist),method="single")
chrB.hclust <- hclust(as.dist(chrB.dist),method="single")
chrA.clust <- cutree(chrA.hclust,h=cl.A)
chrB.clust <- cutree(chrB.hclust,h=cl.B)
box.df[,"chrA.clus"] <- chrA.clust
box.df[,"chrB.clus"] <- chrB.clust
} else {
chrA.clust <- 1
chrB.clust <- 1
box.df[,"chrA.clus"] <- chrA.clust
box.df[,"chrB.clus"] <- chrB.clust
}
clus <- unique(data.frame(chrA.clust,chrB.clust))
box.cl <- list()
i <- 1
while (i <= nrow(clus)) {
d <- box.df[box.df$chrA.clus==clus$chrA.clust[i] & box.df$chrB.clus==clus$chrB.clust[i],]
box.cl[[i]] <- data.frame(chrA=d$chrA[1],BoundaryAS=min(d$BoundaryAS),BoundaryAE=max(d$BoundaryAE),
chrB=d$chrB[1],BoundaryBS=min(d$BoundaryBS),BoundaryBE=max(d$BoundaryBE))
i <- i + 1
}
box.cl <- as.data.frame(do.call(rbind,box.cl))
colnames(box.cl) <- c("chrA","BoundaryAS","BoundaryAE","chrB","BoundaryBS","BoundaryBE")
return(box.cl)
}
#Process the valid pair file
mapVPs_on_REs <- function(re.bed,chrom.size,vp.file,prefix,chromA,chromB,id){
system(paste0("grep -w -e ",chromA," -e ",chromB," ",re.bed," > ",prefix,"_",chromA,"_",chromB,"_REfrags.",id,".bed"),wait=T)
system(paste0("grep -w -e ",chromA," -e ",chromB," ",chrom.size," > ",prefix,"_",chromA,"_",chromB,"_chromSizes.",id,".txt"),wait=T)
cmd <- paste0(buildMatrixPath," --binfile ",prefix,"_",chromA,"_",chromB,"_REfrags.",id,".bed --chrsizes ",prefix,"_",chromA,"_",chromB,"_chromSizes.",id,".txt --ifile ",vp.file," --oprefix ",prefix,"_",chromA,"_",chromB,"_",id," --matrix-format complete")
system(cmd,wait=T)
bed <- as.data.frame(fread(as.character(paste0(prefix,"_",chromA,"_",chromB,"_",id,"_abs.bed")),header=F))
mat <- as.data.frame(fread(as.character(paste0(prefix,"_",chromA,"_",chromB,"_",id,".matrix")),header=F))
colnames(bed) <- c("chrom","start","end","index")
colnames(mat) <- c("indexA","indexB","count")
bed_chrom.hash <- hashmap(bed$index,as.character(bed$chrom))
bed_start.hash <- hashmap(bed$index,bed$start)
bed_end.hash <- hashmap(bed$index,bed$end)
mat.df <- data.frame(
chromA = bed_chrom.hash[[mat$indexA]],
startA = bed_start.hash[[mat$indexA]],
endA = bed_end.hash[[mat$indexA]],
chromB = bed_chrom.hash[[mat$indexB]],
startB = bed_start.hash[[mat$indexB]],
endB = bed_end.hash[[mat$indexB]],
count = mat$count
)
write.table(mat.df,file=paste0(prefix,"_",chromA,"_",chromB,"_",id,".txt"),row.names=F,quote=F,sep="\t")
return(list(mat.df=mat.df,bed=bed,chromA=chromA,chromB=chromB))
}
#Calculate the Cis and trans coverage values
cis_trans_coverage <- function(files,prefix,chrA,chrB,id){
df <- files$mat.df
df_cis_chromA <- df[with(df,chromA==as.character(chrA) & chromB==as.character(chrA)),]
df_cis_chromB <- df[with(df,chromA==as.character(chrB) & chromB==as.character(chrB)),]
df_trans_chromA <- df[with(df,chromA==as.character(chrA) & chromB==as.character(chrB)),]
df_trans_chromB <- df[with(df,chromA==as.character(chrB) & chromB==as.character(chrA)),]
df_cis_chromA.up <- df_cis_chromA[with(df_cis_chromA,startA > endB),]
df_cis_chromB.up <- df_cis_chromB[with(df_cis_chromB,startA > endB),]
df_cis_chromA.dw <- df_cis_chromA[with(df_cis_chromA,endA < startB),]
df_cis_chromB.dw <- df_cis_chromB[with(df_cis_chromB,endA < startB),]
cis_chromA.up <- aggregate(with(df_cis_chromA.up,count ~ startA),FUN = sum)
cis_chromA.dw <- aggregate(with(df_cis_chromA.dw,count ~ startA),FUN = sum)
cis_chromB.up <- aggregate(with(df_cis_chromB.up,count ~ startA),FUN = sum)
cis_chromB.dw <- aggregate(with(df_cis_chromB.dw,count ~ startA),FUN = sum)
trans_chromA <- aggregate(with(df_trans_chromA,count ~ startA),FUN = sum)
trans_chromB <- aggregate(with(df_trans_chromB,count ~ startA),FUN = sum)
cis_chromA.up.hash <- hashmap(cis_chromA.up$startA,cis_chromA.up$count)
cis_chromB.up.hash <- hashmap(cis_chromB.up$startA,cis_chromB.up$count)
cis_chromA.dw.hash <- hashmap(cis_chromA.dw$startA,cis_chromA.dw$count)
cis_chromB.dw.hash <- hashmap(cis_chromB.dw$startA,cis_chromB.dw$count)
trans_chromA.hash <- hashmap(trans_chromA$startA,trans_chromA$count)
trans_chromB.hash <- hashmap(trans_chromB$startA,trans_chromB$count)
df <- files$bed
df_chromA <- df[with(df,chrom==as.character(chrA)),]
df_chromB <- df[with(df,chrom==as.character(chrB)),]
df_chromA[,"cis.up"] <- cis_chromA.up.hash[[df_chromA$start]]
df_chromA[,"cis.dw"] <- cis_chromA.dw.hash[[df_chromA$start]]
df_chromB[,"cis.up"] <- cis_chromB.up.hash[[df_chromB$start]]
df_chromB[,"cis.dw"] <- cis_chromB.dw.hash[[df_chromB$start]]
df_chromA[,"trans"] <- trans_chromA.hash[[df_chromA$start]]
df_chromB[,"trans"] <- trans_chromB.hash[[df_chromB$start]]
df_chromA$cis.up[is.na(df_chromA$cis.up)] <- 0
df_chromA$cis.dw[is.na(df_chromA$cis.dw)] <- 0
df_chromB$cis.up[is.na(df_chromB$cis.up)] <- 0
df_chromB$cis.dw[is.na(df_chromB$cis.dw)] <- 0
df_chromA$trans[is.na(df_chromA$trans)] <- 0
df_chromB$trans[is.na(df_chromB$trans)] <- 0
df = rbind(df_chromA,df_chromB)
write.table(df,file=paste0(prefix,"_",chrA,"_",chrB,"_",id,"_Coverage.bed"),col.names=F,row.names=F,quote=F,sep="\t")
}
#Calculate cis and trans coverage and then calculate directionality index. Followed by HMM segmentation (2 states)
di_and_hmm <- function(coverage,chromA,chromA.start,chromA.end,chromB,chromB.start,chromB.end,prefix,id){
bed <- as.data.frame(fread(as.character(coverage),header=F))
colnames(bed) <- c("chrom","start","end","index","up","dw","trans")
bed$up[bed$up == 0] <- rbinom(length(bed$up[bed$up == 0]), 3, 0.75)
bed$dw[bed$dw == 0] <- rbinom(length(bed$dw[bed$dw == 0]), 3, 0.75)
bed$trans[bed$trans == 0] <- rbinom(length(bed$trans[bed$trans == 0]), 3, 0.75)
chromA_span <- chromA.end-chromA.start
chromB_span <- chromB.end-chromB.start
if (chromA_span < 1e6){
chromA_span <- 1e6-chromA_span
chromA_span <- floor(chromA_span/2)
chromA.start <- chromA.start-chromA_span
chromA.end <- chromA.end+chromA_span
}
if (chromB_span < 1e6){
chromB_span <- 1e6-chromB_span
chromB_span <- floor(chromB_span/2)+1
chromB.start <- chromB.start-chromB_span
chromB.end <- chromB.end+chromB_span
}
bed_chromA <- bed[with(bed,chrom==chromA),]
bed_chromB <- bed[with(bed,chrom==chromB),]
bed_chromA_cis_di <- list()
bed_chromA_trans_di <- list()
i <- 1
while (i <= nrow(bed_chromA)){
up <- bed_chromA$up[i]
dw <- bed_chromA$dw[i]
trans <- bed_chromA$trans[i]
bed_chromA_cis_di[[i]] <- (dw-up)/abs(dw-up)*(((up-mean(c(dw,up)))^2/mean(c(dw,up)))+((dw-mean(c(dw,up)))^2/mean(c(dw,up))))
bed_chromA_trans_di[[i]] <- ((trans-mean(bed_chromA$trans))/abs(trans-mean(bed_chromA$trans)))*((trans-mean(bed_chromA$trans))^2/mean(bed_chromA$trans))
i <- i+1
}
smooth <- 5
bed_chromA_cis_di <- unlist(bed_chromA_cis_di)
bed_chromA_trans_di <- unlist(bed_chromA_trans_di)
bed_chromA_cis_di[is.na(bed_chromA_cis_di)] <- 0
bed_chromA_trans_di[is.na(bed_chromA_trans_di)] <- 0
bed_chromA_cis_di <- runmean(bed_chromA_cis_di,k=smooth,endrule="mean")
bed_chromA_trans_di <- runmean(bed_chromA_trans_di,k=smooth,endrule="mean")
bed_chromA <- as.data.frame(cbind(bed_chromA,bed_chromA_cis_di,bed_chromA_trans_di))
bed_chromA <- bed_chromA[with(bed_chromA,start >= chromA.start & end <= chromA.end),]
bed_chromB_cis_di <- list()
bed_chromB_trans_di <- list()
i <- 1
while (i <= nrow(bed_chromB)){
up <- bed_chromB$up[i]
dw <- bed_chromB$dw[i]
trans <- bed_chromB$trans[i]
bed_chromB_cis_di[[i]] <- (dw-up)/abs(dw-up)*(((up-mean(c(dw,up)))^2/mean(c(dw,up)))+((dw-mean(c(dw,up)))^2/mean(c(dw,up))))
bed_chromB_trans_di[[i]] <- ((trans-mean(bed_chromB$trans))/abs(trans-mean(bed_chromB$trans)))*((trans-mean(bed_chromB$trans))^2/mean(bed_chromB$trans))
i <- i+1
}
bed_chromB_cis_di <- unlist(bed_chromB_cis_di)
bed_chromB_trans_di <- unlist(bed_chromB_trans_di)
bed_chromB_cis_di[is.na(bed_chromB_cis_di)] <- 0
bed_chromB_trans_di[is.na(bed_chromB_trans_di)] <- 0
bed_chromB_cis_di <- runmean(bed_chromB_cis_di,k=smooth,endrule="mean")
bed_chromB_trans_di <- runmean(bed_chromB_trans_di,k=smooth,endrule="mean")
bed_chromB <- as.data.frame(cbind(bed_chromB,bed_chromB_cis_di,bed_chromB_trans_di))
bed_chromB <- bed_chromB[with(bed_chromB,start >= chromB.start & end <= chromB.end),]
nstates <- 2
chromA.hmm <- depmix(response=list(bed_chromA_cis_di~1,bed_chromA_trans_di~1),data=bed_chromA, nstates=nstates,family=list(gaussian(),gaussian()),ntimes=nrow(bed_chromA))
chromA.hmm.fit <- fit(chromA.hmm)
chromA.hmm.states <- posterior(chromA.hmm.fit)$state
x <- summary(chromA.hmm.fit)
chromA.trans.state <- which.max(as.vector(x[1:nstates,3]))
cis.hash <- hashmap(c(1:nstates),as.vector(x[1:nstates,1]))
trans.hash <- hashmap(c(1:nstates),as.vector(x[1:nstates,3]))
chromA_cis_DI <- as.vector(x[chromA.trans.state,1])
bed_chromA <- cbind(bed_chromA,chromA.hmm.states,cis.mean=cis.hash[[chromA.hmm.states]],trans.mean=trans.hash[[chromA.hmm.states]])
chrA.hmmBoundary.start <- min(bed_chromA[bed_chromA$chromA.hmm.states==chromA.trans.state,]$start)
chrA.hmmBoundary.end <- max(bed_chromA[bed_chromA$chromA.hmm.states==chromA.trans.state,]$end)
write.table(bed_chromA,file=paste0(prefix,".",chromA,"_",id,".hmm.states.bed"),col.names=T,row.names=F,sep="\t",quote=F)
chromB.hmm <- depmix(response=list(bed_chromB_cis_di~1,bed_chromB_trans_di~1),data=bed_chromB, nstates=nstates,family=list(gaussian(),gaussian()),ntimes=nrow(bed_chromB))
chromB.hmm.fit <- fit(chromB.hmm)
chromB.hmm.states <- posterior(chromB.hmm.fit)$state
x <- summary(chromB.hmm.fit)
chromB.trans.state <- which.max(as.vector(x[1:nstates,3]))
cis.hash <- hashmap(c(1:nstates),as.vector(x[1:nstates,1]))
trans.hash <- hashmap(c(1:nstates),as.vector(x[1:nstates,3]))
chromB_cis_DI <- as.vector(x[chromB.trans.state,1])
bed_chromB <- cbind(bed_chromB,chromB.hmm.states,cis.mean=cis.hash[[chromB.hmm.states]],trans.mean=trans.hash[[chromB.hmm.states]])
chrB.hmmBoundary.start <- min(bed_chromB[bed_chromB$chromB.hmm.states==chromB.trans.state,]$start)
chrB.hmmBoundary.end <- max(bed_chromB[bed_chromB$chromB.hmm.states==chromB.trans.state,]$end)
write.table(bed_chromB,file=paste0(prefix,".",chromB,"_",id,".hmm.states.bed"),col.names=T,row.names=F,sep="\t",quote=F)
return(
list(
chrA.hmmBoundary.start=chrA.hmmBoundary.start,
chrA.hmmBoundary.end=chrA.hmmBoundary.end,
chrB.hmmBoundary.start=chrB.hmmBoundary.start,
chrB.hmmBoundary.end=chrB.hmmBoundary.end,
chromA_cis_DI=chromA_cis_DI,
chromB_cis_DI=chromB_cis_DI
)
)
}
#Optimize the HMM segment border (+/-bp) to find the breakpoint using DE optimization
optimization_func <- function(boundary,df,as,bs,ae,be,bk,chromA_cis_DI,chromB_cis_DI){
chromA_span <- ae-as
chromB_span <- be-bs
chromA_pos <- boundary[1]
chromB_pos <- boundary[2]
chromA_up <- floor(chromA_pos-(chromA_span/2))
chromA_dw <- floor(chromA_pos+(chromA_span/2))
chromB_up <- floor(chromB_pos-(chromB_span/2))
chromB_dw <- floor(chromB_pos+(chromB_span/2))
chromA_dw.chromB_up <- sum(df[df$startA >= chromA_pos & df$endA <= chromA_dw & df$startB >= chromB_up & df$endB <= chromB_pos,]$count)
chromA_up.chromB_dw <- sum(df[df$startA >= chromA_up & df$endA <= chromA_pos & df$startB >= chromB_pos & df$endB <= chromB_dw,]$count)
chromAB_up <- sum(df[df$startA >= chromA_up & df$endA <= chromA_pos & df$startB >= chromB_up & df$endB <= chromB_pos,]$count)
chromAB_dw <- sum(df[df$startA >= chromA_pos & df$endA <= chromA_dw & df$startB >= chromB_pos & df$endB <= chromB_dw,]$count)
if ((chromA_cis_DI > 0 & chromB_cis_DI < 0) | (chromA_cis_DI < 0 & chromB_cis_DI > 0)){
if (chromA_cis_DI > 0 & chromB_cis_DI < 0) {
if (bk == 1){
value <- (chromA_dw.chromB_up+chromA_up.chromB_dw)-chromAB_dw
} else if (bk == 2){
value <- (chromA_dw.chromB_up+chromA_up.chromB_dw)-chromAB_up
} else if (bk == 3){
value <- (chromAB_up+chromAB_dw)-chromA_dw.chromB_up
} else if (bk == 4){
value <- (chromAB_up+chromAB_dw)-chromA_up.chromB_dw
}
} else if (chromA_cis_DI < 0 & chromB_cis_DI > 0){
if (bk == 1){
value <- (chromA_dw.chromB_up+chromA_up.chromB_dw)-chromAB_dw
} else if (bk == 2){
value <- (chromA_dw.chromB_up+chromA_up.chromB_dw)-chromAB_up
} else if (bk == 3){
value <- (chromAB_up+chromAB_dw)-chromA_dw.chromB_up
} else if (bk == 4){
value <- (chromAB_up+chromAB_dw)-chromA_up.chromB_dw
}
}
} else if ((chromA_cis_DI > 0 & chromB_cis_DI > 0) | (chromA_cis_DI < 0 & chromB_cis_DI < 0)) {
if (chromA_cis_DI > 0 & chromB_cis_DI > 0){
if (bk == 1){
value <- (chromA_dw.chromB_up+chromA_up.chromB_dw)-chromAB_dw
} else if (bk == 2){
value <- (chromA_dw.chromB_up+chromA_up.chromB_dw)-chromAB_up
} else if (bk == 3){
value <- (chromAB_up+chromAB_dw)-chromA_dw.chromB_up
} else if (bk == 4){
value <- (chromAB_up+chromAB_dw)-chromA_up.chromB_dw
}
} else if (chromA_cis_DI < 0 & chromB_cis_DI < 0){
if (bk == 1){
value <- (chromA_dw.chromB_up+chromA_up.chromB_dw)-chromAB_dw
} else if (bk == 2){
value <- (chromA_dw.chromB_up+chromA_up.chromB_dw)-chromAB_up
} else if (bk == 3){
value <- (chromAB_up+chromAB_dw)-chromA_dw.chromB_up
} else if (bk == 4){
value <- (chromAB_up+chromAB_dw)-chromA_up.chromB_dw
}
}
}
value
}
bp_optimization <- function(intr.file,boundary,chromA,chromB,ssA,seA,ssB,seB){
intr.file <- as.data.frame(fread(as.character(intr.file),header=T))
intr.file <- intr.file[intr.file$chromA == as.character(chromA) & intr.file$chromB == as.character(chromB),]
as <- boundary$chrA.hmmBoundary.start-ssA
bs <- boundary$chrB.hmmBoundary.start-ssB
ae <- boundary$chrA.hmmBoundary.end+seA
be <- boundary$chrB.hmmBoundary.end+seB
chromA_span <- ae-as
chromB_span <- be-bs
upup <- sum(intr.file[with(intr.file,startA >= as & endA <= as+floor(chromA_span/2) & startB >= bs & endB <= bs+floor(chromB_span/2)),]$count)
dwdw <- sum(intr.file[with(intr.file,startA >= as+floor(chromA_span/2) & endA <= ae & startB >= bs+floor(chromB_span/2) & endB <= be),]$count)
updw <- sum(intr.file[with(intr.file,startA >= as & endA <= as+floor(chromA_span/2) & startB >= bs+floor(chromB_span/2) & endB <= be),]$count)
dwup <- sum(intr.file[with(intr.file,startA >= as+floor(chromA_span/2) & endA <= ae & startB >= bs & endB <= bs+floor(chromB_span/2)),]$count)
mx <- which.max(c(upup,dwdw,updw,dwup))
chromA_span <- ae-as
chromB_span <- be-bs
if (chromA_span < 1e6){
chromA_span <- 1e6-chromA_span
chromA_span <- floor(chromA_span/2)
as <- as-chromA_span
ae <- ae+chromA_span
}
if (chromB_span < 1e6){
chromB_span <- 1e6-chromB_span
chromB_span <- floor(chromB_span/2)+1
bs <- bs-chromB_span
be <- be+chromB_span
}
#Optimization parameters
JDEoptim(
lower=c(as,bs),
upper=c(ae,be),
fn=optimization_func,
df=intr.file,
compare_to="median",
maxiter=1000,
NP=100,
fnscale=1e-5,
tau_pF=0.1,
as=as,
bs=bs,
ae=ae,
be=be,
bk=mx,
chromA_cis_DI = as.numeric(boundary$chromA_cis_DI),
chromB_cis_DI = as.numeric(boundary$chromB_cis_DI),
trace=TRUE
)
}
############### HiCtrans calling on the binned Hi-C data #########################
## This function is called from HClust to calculate the overlap between the detected segments ##
OverLap <- function(x,y,u,v) {
if (y < u) {
return(1)
} else if (v < x) {
return(1)
} else if (y > u & y <= v) {
return(0)
} else if (x > u & x <= v) {
return(0)
} else if (u > x & v <= y) {
return(0)
} else if (v > x & u <= y) {
return(0)
} else if (x >= u & y < v) {
return(0)
} else if (u >= x & v < y) {
return(0)
}
}
## Given distances, this function can cluster a 2D interacting segments ##
HClust <- function(box.df,cl.A,cl.B,colNameA,colNameB,step=0) {
if (step==0) {
box.df <- box.df[,c(1:6,8,9,10,12)]
colnames(box.df) <- c("chrA","BoundaryAS","BoundaryAE","chrB","BoundaryBS","BoundaryBE","zscore","count","id","resolution")
}
chrA.dist <- matrix(ncol=nrow(box.df),nrow=nrow(box.df))
chrB.dist <- matrix(ncol=nrow(box.df),nrow=nrow(box.df))
i <- 1
while (i <= nrow(box.df)) {
chrA.AS_i <- box.df$BoundaryAS[i]
chrA.AE_i <- box.df$BoundaryAE[i]
chrB.BS_i <- box.df$BoundaryBS[i]
chrB.BE_i <- box.df$BoundaryBE[i]
j <- 1
while (j <= nrow(box.df)) {
chrA.AS_j <- box.df$BoundaryAS[j]
chrA.AE_j <- box.df$BoundaryAE[j]
chrB.BS_j <- box.df$BoundaryBS[j]
chrB.BE_j <- box.df$BoundaryBE[j]
if (step==0 | step==2) {
chrA.AS_AS.ij <- abs(chrA.AS_i - chrA.AS_j)
chrA.AE_AS.ij <- abs(chrA.AE_i - chrA.AS_j)
chrA.AS_AE.ij <- abs(chrA.AS_i - chrA.AE_j)
chrA.AE_AE.ij <- abs(chrA.AE_i - chrA.AE_j)
chrB.BS_BS.ij <- abs(chrB.BS_i - chrB.BS_j)
chrB.BE_BS.ij <- abs(chrB.BE_i - chrB.BS_j)
chrB.BS_BE.ij <- abs(chrB.BS_i - chrB.BE_j)
chrB.BE_BE.ij <- abs(chrB.BE_i - chrB.BE_j)
chrA.dist[i,j] <- min(chrA.AS_AS.ij,chrA.AE_AS.ij,chrA.AS_AE.ij,chrA.AE_AE.ij)
chrB.dist[i,j] <- min(chrB.BS_BS.ij,chrB.BE_BS.ij,chrB.BS_BE.ij,chrB.BE_BE.ij)
} else {
chrA.dist[i,j] <- OverLap(chrA.AS_i,chrA.AE_i,chrA.AS_j,chrA.AE_j)
chrB.dist[i,j] <- OverLap(chrB.BS_i,chrB.BE_i,chrB.BS_j,chrB.BE_j)
}
j <- j + 1
}
i <- i + 1
}
if (nrow(chrA.dist) > 1 & nrow(chrB.dist) > 1) {
chrA.hclust <- hclust(as.dist(chrA.dist),method="single")
chrB.hclust <- hclust(as.dist(chrB.dist),method="single")
chrA.clust <- cutree(chrA.hclust,h=cl.A)
chrB.clust <- cutree(chrB.hclust,h=cl.B)
box.df[,"chrA.clus"] <- chrA.clust
box.df[,"chrB.clus"] <- chrB.clust
} else {
chrA.clust <- 1
chrB.clust <- 1
box.df[,"chrA.clus"] <- chrA.clust
box.df[,"chrB.clus"] <- chrB.clust
}
clus <- unique(data.frame(chrA.clust,chrB.clust))
if (step==1) {
return(box.df)
}
if (step==0) {
box.cl <- list()
i <- 1
while (i <= nrow(clus)) {
d <- box.df[box.df$chrA.clus==clus$chrA.clust[i] & box.df$chrB.clus==clus$chrB.clust[i],]
box.cl[[i]] <- data.frame(chrA=d$chrA[1],BoundaryAS=min(d$BoundaryAS),BoundaryAE=max(d$BoundaryAE),
chrB=d$chrB[1],BoundaryBS=min(d$BoundaryBS),BoundaryBE=max(d$BoundaryBE),
zscore=mean(d$zscore),count=mean(d$count),resolution=d$resolution[1],
id=paste0(d$id,collapse=","),A=clus$chrA.clust[i],B=clus$chrB.clust[i])
i <- i + 1
}
box.cl <- as.data.frame(do.call(rbind,box.cl))
colnames(box.cl) <- c("chrA","BoundaryAS","BoundaryAE","chrB","BoundaryBS","BoundaryBE","zscore","count","resolution","id",as.character(colNameA),as.character(colNameB))
return(box.cl)
}
}
## Finding out the common multi-resolution translocation boxes and breakpoints ##
ZoomIn <- function(d, f, r, l) {
g <- 1
clus <- unique(data.frame(d[,c("chrA.clus","chrB.clus")]))
w <- list()
i <- 1
while (i <= nrow(clus)) {
v <- d[d$chrA.clus==clus$chrA.clus[i] & d$chrB.clus==clus$chrB.clus[i],]
b <- list()
j <- 1
while (j <= length(r)) {
b[[j]] <- v[v$resolution==r[j],]
if (nrow(b[[j]]) > 0) {
b[[j]][,"Level"] <- paste0("L_",j)
}
j <- j + 1
}
b <- do.call(rbind,b)
if (nrow(b) >= l) {
w[[i]] <- b
w[[i]][,"group"] <- g
g <- g + 1
}
i <- i + 1
}
w <- as.data.frame(do.call(rbind, w))
g <- unique(w$g)
b <- list()
h <- list()
n <- 1
i <- 1
while (i <= length(g)) {
d <- w[w$g==g[i],]
d <- d[d$Level==d[nrow(d),]$Level,]
j <- 1
while (j <= nrow(d)) {
k <- as.integer(strsplit(as.character(d$id[j]),",")[[1]])
r <- d$resolution[j]
b[[n]] <- f[f$resolution==r & f$id %in% k & f$type=="BreakPoint",]
j <- j + 1
n <- n + 1
}
h[[i]] <- w[w$g==g[i],]
h[[i]] <- h[[i]][h[[i]]$Level==h[[i]][1,]$Level,]
i <- i + 1
}
b <- as.data.frame(do.call(rbind,b))
if (nrow(b) > 0) {
b <- HClust(box.df=b,cl.A=min(b$resolution),cl.B=min(b$resolution),colNameA="resA.cl",colNameB="resB.cl",step=0)
h <- as.data.frame(do.call(rbind,h))
b <- b[,c(1:9)]
h <- h[,c(1:9)]
b[,"class"] <- "BreakPoint"
h[,"class"] <- "TranslocationBox"
df <- as.data.frame(rbind(h,b))
} else {
df <- data.frame(chrA=c(),BoundaryAS=c(),BoundaryAE=c(),chrB=c(),BoundaryBS=c(),BoundaryBE=c(),zscore=c(),count=c(),resolution=c(),id=c(),A=c(),B=c(),class=c())
}
return(df)
}
## From ijk type data, this function will create a matrix object ##
CreateMatrix <- function(ijk.file, bed.file, chromA, chromB, prefix) {
ijk <- as.data.frame(fread(ijk.file,h=F))
bed <- as.data.frame(fread(bed.file,h=F))
colnames(ijk) <- c("A","B","C")
colnames(bed) <- c("chr","start","end","index")
bed <- bed[bed$chr %in% c(chromA,chromB),]
indexA.conversion <- hashmap(bed[bed$chr==chromA,]$index,c(1:nrow(bed[bed$chr==chromA,])))
indexB.conversion <- hashmap(bed[bed$chr==chromB,]$index,c(1:nrow(bed[bed$chr==chromB,])))
ijk <- ijk[ijk$A %in% bed[bed$chr==chromA,]$index,]
ijk <- ijk[ijk$B %in% bed[bed$chr==chromB,]$index,]
ijk$A <- indexA.conversion[[ijk$A]]
ijk$B <- indexB.conversion[[ijk$B]]
bed[bed$chr==chromA,]$index <- 1:nrow(bed[bed$chr==chromA,])
bed[bed$chr==chromB,]$index <- 1:nrow(bed[bed$chr==chromB,])
mat <- matrix(0,nrow=nrow(bed[bed$chr==chromA,]),ncol=nrow(bed[bed$chr==chromB,]))
print (dim(mat))
i <- 1
while (i <= nrow(ijk)) {
mat[ijk$A[i],ijk$B[i]] <- ijk$C[i]
i <- i + 1
}
write.table(mat,file=paste0(prefix,".",chromA,"_",chromB,".mat.txt"),row.names=F,col.names=F,sep="\t",quote=F)
return(list(mat=as.matrix(mat),bedA=as.data.frame(bed[bed$chr==chromA,]),bedB=as.data.frame(bed[bed$chr==chromB,])))
}
## This function scans the inter-chromosomal interaction matrix for mean values with a given box size ##
cppFunction('NumericVector lmat(NumericMatrix mat, int x, int y) {
int n = mat.nrow();
int m = mat.ncol();
int k = 0;
int s1 = round(n/x);
int s2 = round(m/y);
int s3 = (s1 * s2);
Rcpp::NumericVector M(s3);
for(int i = 0; i < n; i = i + x) {
int a = i;
int b = i + (x-1);
for (int j = 0; j < m; j = j + y){
int u = j;
int v = j + (y-1);
if (b < mat.nrow()) {
if (v < mat.ncol()) {
NumericMatrix F = mat(Range(a,b),Range(u,v));
M[k] = findMean(F);
k = k + 1;
}
}
}
}
return(M);
}', includes='double findMean(NumericMatrix F) {
int sum = 0;
int n = F.nrow();
int m = F.ncol();
for (int i=0; i<n; i++) {
for (int j=0; j<m; j++) {
sum += F(i,j);
}
}
return (double)sum/(n*m);
}')
## This function shrinks the Hi-C matrix from high to low resolution ##
cppFunction('NumericMatrix ShrinkMatrixCPP(NumericMatrix mat_high, NumericMatrix x_coord, NumericMatrix y_coord) {
NumericMatrix mat_low(x_coord.nrow(),y_coord.nrow());
for (int i = 0; i < x_coord.nrow(); i++) {
int x_0 = x_coord(i,1) - 1;
int x_1 = x_coord(i,2) - 1;
for (int j = 0; j < y_coord.nrow(); j++) {
int y_0 = y_coord(j,1) - 1;
int y_1 = y_coord(j,2) - 1;
NumericMatrix sub_mat = mat_high(Range(x_0,x_1),Range(y_0,y_1));
int sum = 0;
for (int u = 0; u < sub_mat.nrow(); u++) {
for (int v = 0; v < sub_mat.ncol(); v++) {
sum += sub_mat(u,v);
}
}
mat_low(i,j) = sum;
}
}
return(mat_low);
}')
## For a detected translocation box, this function detects the top interacting region ##
EstimateMaxRegion <- function(box, mat, locq) {
b <- list()
k <- 1
i <- 1
while (i <= nrow(box)) {
s.1 <- box$start1[i]
e.1 <- box$end1[i]
s.2 <- box$start2[i]
e.2 <- box$end2[i]
m <- mat[s.1:e.1,s.2:e.2]
rownames(m) <- c(s.1:e.1)
colnames(m) <- c(s.2:e.2)
m[m > 0] <- 1
x.val <- rowSums(m)/sum(rowSums(m))
y.val <- colSums(m)/sum(colSums(m))
val.mat <- outer(x.val,y.val)
val.mat <- mat[s.1:e.1,s.2:e.2] * val.mat
p <- which(val.mat >= quantile(val.mat, locq), arr.ind = TRUE)
x.pos <- as.integer(rownames(m)[p[,1]])
y.pos <- as.integer(colnames(m)[p[,2]])
j <- 1
while (j <= nrow(p)) {
if (x.pos[j] >= 2 & x.pos[j] < nrow(mat) & y.pos[j] >= 2 & y.pos[j] < ncol(mat)) {
max.count <- max(mat[c(x.pos[j]-1):c(x.pos[j]+1),c(y.pos[j]-1):c(y.pos[j]+1)])
b[[k]] <- cbind(box[i,],pos.1=x.pos[j],pos.2=y.pos[j],count=max.count)
k <- k + 1
}
j <- j + 1
}
i <- i + 1
}
#b <- as.data.frame(do.call(rbind,b))
b <- as.data.frame(data.table::rbindlist(b))
return(b)
}
## This function calls the lmat function described above ##
BackgroundEnrichment <- function(i, mat, f) {
print (f[i,])
x.s <- f$d1[i]
y.s <- f$d2[i]
val <- lmat(mat,x.s,y.s)
return(data.frame(MEAN=mean(val),SD=sd(val)))
}
## Check for all combination of 1D segments from both the chromosomes, the enrichment of their interaction ##
BoxEnrichment <- function(m, x, y, core, boxzscore, cutoff=0.05) {
n <- 0
repeat {
print (paste0("Estimating box enrichment: Trimmining ",cutoff*1e2,"% of values"))
d <- list()
k <- 1
i <- 1
while (i <= nrow(x)) {
a <- x$start[i]
b <- x$end[i]
j <- 1
while (j <= nrow(y)) {
u <- y$start[j]
v <- y$end[j]
d[[k]] <- data.frame(start1=x$start[i],end1=x$end[i],d1=(x$end[i]-x$start[i]),
start2=y$start[j],end2=y$end[j],d2=(y$end[j]-y$start[j]),
val=mean(m[a:b,u:v],trim=cutoff))
k <- k + 1
j <- j + 1
}
i <- i + 1
}
d <- as.data.frame(do.call(rbind,d))
d[,"val.center"] <- d$val - mean(d$val)
d <- d[d$val.center > 0 & d$d1 > 2 & d$d2 > 2,]
cutoff <- cutoff - 0.04
if (nrow(d) > 0) {
break
}
if (cutoff < 0) {
n <- n + 1
if (n == 1) {
cutoff <- 0
} else if (n > 1) {
print ("No enrichemnt of changepoint boxes detected! Try to run with Lower resolution")
break
}
}
print ("Lowering trimming threshold")
}
if (nrow(d) > 0) {
v <- lapply(c(1:nrow(d)), BackgroundEnrichment, m, d)
v <- as.data.frame(do.call(rbind,v))
d[,"val.bg.mean"] <- v$MEAN
d[,"val.bg.sd"] <- v$SD
d[,"zscore"] <- ifelse(is.na(v$SD), NA, (d$val - d$val.bg.mean)/d$val.bg.sd)
d <- na.omit(d)
d <- d[d$zscore >= boxzscore,]
print (d)
if (nrow(d) > 0) {
return(d)
} else {
print (paste0("No enriched segment found"))
return(data.frame())
}
} else {
print ("No enrichemnt of changepoint boxes detected! Try to run with Lower resolution")
return(data.frame())
}
}
## Given a change-point vector, convert the vector to data.frame like object ##
CptsInBedFormat <- function(cpt.obj) {
start <- list()
end <- list()
val <- list()
start[[1]] <- 1
end[[1]] <- cpt.obj@cpts[1]
val[[1]] <- cpt.obj@param.est$mean[1]
i <- 2
while (i <= length(cpt.obj@cpts)) {
start[[i]] <- cpt.obj@cpts[i-1]
end[[i]] <- cpt.obj@cpts[i]
val[[i]] <- cpt.obj@param.est$mean[i]
i <- i + 1
}
start <- unlist(start)
end <- unlist(end)
val <- unlist(val)
return(data.frame(start,end,val))
}
## This is the main function which call the binary-segmentation algorithm and detects 1D segmentations ##
GetSegments <- function(mat, bed, chromA, chromB, prefix, covq=0.75, locq, mincount, minzscore, resolution, glbq, boxzscore) {
r.sum <- rowSums(mat)
c.sum <- rowSums(t(mat))
r.center <- r.sum - quantile(r.sum, covq)
c.center <- c.sum - quantile(c.sum, covq)
##Modified on 09/27/2021
##Commented
#seg <- ifelse(resolution > 250000, 50, 100)
#r.cpt <- suppressWarnings(cpt.meanvar(r.center,Q=seg,method="BinSeg"))
#c.cpt <- suppressWarnings(cpt.meanvar(c.center,Q=seg,method="BinSeg"))
##Updated
r.cpt <- suppressWarnings(cpt.meanvar(r.center,Q=ifelse((length(r.center)/2) < 100, round(((length(r.center)/2) - 1), 0), 100),method="BinSeg"))
c.cpt <- suppressWarnings(cpt.meanvar(c.center,Q=ifelse((length(c.center)/2) < 100, round(((length(c.center)/2) - 1), 0), 100),method="BinSeg"))
## Flush changepoint summary ##
print ("Row and Column changepoint summary")
print (r.cpt)
print (c.cpt)
cat ("\n")
###############################
r.df <- CptsInBedFormat(r.cpt)
c.df <- CptsInBedFormat(c.cpt)
r.df <- r.df[r.df$val > 0,]
c.df <- c.df[c.df$val > 0,]
if (nrow(r.df) > 0 & nrow(c.df) > 0) {
## Flush out the changepoints and their mean values ##
print ("Row segments with positive values")
print (r.df)
print ("Column segments with positive values")
print (c.df)
######################################################
box <- BoxEnrichment(mat,r.df,c.df,core,boxzscore)
if (nrow(box) > 0) {
box <- EstimateMaxRegion(box, mat, locq)
if (nrow(box) > 0) {
chrA.start.hash <- hashmap(bed[bed$chr==chromA,]$index,bed[bed$chr==chromA,]$start)
chrB.start.hash <- hashmap(bed[bed$chr==chromB,]$index,bed[bed$chr==chromB,]$start)
chrA.end.hash <- hashmap(bed[bed$chr==chromA,]$index,bed[bed$chr==chromA,]$end)
chrB.end.hash <- hashmap(bed[bed$chr==chromB,]$index,bed[bed$chr==chromB,]$end)
box[,"chr1"] <- chromA
box[,"x1"] <- chrA.start.hash[[box$start1]]
box[,"y1"] <- chrA.start.hash[[box$end1]]
box[,"chr2"] <- chromB
box[,"x2"] <- chrB.start.hash[[box$start2]]
box[,"y2"] <- chrB.start.hash[[box$end2]]
box[,"chrA"] <- chromA
box[,"chrA.Brk.pos1"] <- chrA.start.hash[[box$pos.1]]
box[,"chrA.Brk.pos2"] <- chrA.end.hash[[box$pos.1]]
box[,"chrB"] <- chromB
box[,"chrB.Brk.pos1"] <- chrB.start.hash[[box$pos.2]]
box[,"chrB.Brk.pos2"] <- chrB.end.hash[[box$pos.2]]
write.table(box,file=paste0(prefix,".",chromA,"_",chromB,".Details.txt"),row.names=F,sep="\t",quote=F)
wt.threshold <- data.frame(count=as.integer(names(table(as.vector(mat)))),freq=as.vector(table(as.vector(mat))))
wt.threshold <- wt.threshold[-c(1),]
wt.threshold[,"csum"] <- cumsum(wt.threshold$freq)/sum(wt.threshold$freq)
wt.threshold <- min(wt.threshold[wt.threshold$csum >= glbq,]$count)
wt.threshold <- ifelse(wt.threshold < mincount, mincount, wt.threshold)
print (wt.threshold)
box[,"color"] <- "0,0,255"
box <- box[,c("chr1","x1","y1","chr2","x2","y2","color","zscore","chrA","chrA.Brk.pos1","chrA.Brk.pos2","chrB","chrB.Brk.pos1","chrB.Brk.pos2","count")]
box <- box[box$count >= wt.threshold & box$zscore >= minzscore,]
if (nrow(box) > 0) {
box[,"id"] <- 1:nrow(box)
boxA <- box[,c("chr1","x1","y1","chr2","x2","y2","color","zscore","count","id")]
boxB <- data.frame(chr1=box$chrA,x1=box$chrA.Brk.pos1,y1=box$chrA.Brk.pos2,
chr2=box$chrB,x2=box$chrB.Brk.pos1,y2=box$chrB.Brk.pos2,
color=box$color,zscore=box$zscore,count=box$count,id=box$id)
boxB[,"color"] <- "0,0,0"
boxA[,"type"] <- "TranslocationBox"
boxB[,"type"] <- "BreakPoint"
box <- rbind(boxA,boxB)
box[,"resolution"] <- resolution
write.table(box,file=paste0(prefix,".",chromA,"_",chromB,".Translocations_jcbx.txt"),row.names=F,sep="\t",quote=F)
return(box)
} else {
print ("Minimum count filter not passed. Try to decrease the minimum count to get a breakpoint at this resolution")
return(data.frame())
}
} else {
print ("Minimum count percentile not passed. Try to decrease the minimum count percentile to get a breakpoint at this resolution")
return(data.frame())
}
} else {
print ("Minimum count filter not passed. Try to decrease the minimum count to get a breakpoint at this resolution")
return(data.frame())
}
} else {
if (covq > 0.5) {
print (paste0("Descresing the quantile value to ",(covq-0.25)," to get positive segments"))
GetSegments(mat, bed, chromA, chromB, prefix, covq=(covq-0.25), locq, mincount, minzscore, resolution, glbq, boxzscore)
} else {
print ("No positive segments from changepoint analysis! Try a lower resolution hic data for this chromosome")
return(data.frame())
}
}
}
CoordinateShrunkage <- function(v,len) {
d <- list()
j <- 1
while(j < length(v)) {
d[[j]] <- data.frame(p=j,s=v[j],e=(v[j+1]-1))
j <- j + 1
}
if (v[j]==len) {
d[[j]] <- data.frame(p=j,s=v[j],e=v[j])
} else {
d[[j]] <- data.frame(p=j,s=v[j],e=len)
}
d <- as.data.frame(do.call(rbind,d))
return(d)
}
ShrinkMatrix <- function(mat.high,x.coord,y.coord) {
cat ("Shrinking Matrix to lower resolution\n")
x.coord <- as.matrix(x.coord)
y.coord <- as.matrix(y.coord)
colnames(x.coord) <- NULL
colnames(y.coord) <- NULL
mat.low <- ShrinkMatrixCPP(mat.high,x.coord,y.coord)
return(mat.low)
}
## This function provide a Lower-resolution Hi-C matrix from a Higher-resolution Hi-C matrix ##
HighToLowResolutionCoversion <- function(mat, high.resolution, low.resolution, bed.x, bed.y, bed, prefix) {
mat_list <- list()
bed_list <- list()
x.coord_list <- list()
y.coord_list <- list()
mat_list[[1]] <- mat
bed_list[[1]] <- bed
i <- 1
while (i <= length(low.resolution)) {
x.start <- hashmap(c(bed.x$index),c(bed.x$start))
x.end <- hashmap(c(bed.x$index),c(bed.x$end))
y.start <- hashmap(c(bed.y$index),c(bed.y$start))
y.end <- hashmap(c(bed.y$index),c(bed.y$end))
scale.factor <- low.resolution[i]/high.resolution
x.dim <- nrow(mat)
y.dim <- ncol(mat)
x.divison <- seq(1,x.dim,by=scale.factor)
y.divison <- seq(1,y.dim,by=scale.factor)
x.coord <- CoordinateShrunkage(x.divison,x.dim)
y.coord <- CoordinateShrunkage(y.divison,y.dim)
bedA <- data.frame(chr=rep(as.character(bed.x$chr[1]),nrow(x.coord)),start=x.start[[x.coord$s]],end=x.end[[x.coord$e]],index=c(1:nrow(x.coord)))
bedB <- data.frame(chr=rep(as.character(bed.y$chr[1]),nrow(y.coord)),start=y.start[[y.coord$s]],end=y.end[[y.coord$e]],index=c(1:nrow(y.coord)))
x.coord_list[[i]] <- cbind(bedA,x.coord)
y.coord_list[[i]] <- cbind(bedB,y.coord)
mat_list[[i+1]] <- ShrinkMatrix(mat,x.coord,y.coord)
bed_list[[i+1]] <- data.frame(rbind(bedA,bedB))
write.table(mat_list[[i+1]],file=paste0(prefix,"_",as.integer(low.resolution[i]),".",bedA$chr[1],"_",bedB$chr[1],".matrix"),row.names=F,col.names=F,sep="\t",quote=F)
write.table(bed_list[[i+1]],file=paste0(prefix,"_",as.integer(low.resolution[i]),".",bedA$chr[1],"_",bedB$chr[1],"_abs.bed"),row.names=F,col.names=F,sep="\t",quote=F)
i <- i + 1
}
d <- list(mat=mat_list,bed=bed_list,x.coord=x.coord_list,y.coord=y.coord_list)
return(d)
}
option_list = list(
make_option(c("--mat"), type="character", help="An upper triangular Hi-C sparse matrix\n\t\tIt should have the following columns\n
\t\t<indexA> <indexB> <count>\n
\t\t1 1 300
\t\t1 2 30
\t\t1 3 10
\t\t2 2 200
\t\t2 3 20
\t\t3 3 200
\t\t....\n"),
make_option(c("--bed"), type="character", help="Bed file with index information\n\t\tIt should have the following columns\n\n
\t\t<chr> <start> <end> <index>\n
\t\tchr1 1 40000 1
\t\tchr1 40000 80000 2
\t\tchr1 80000 120000 3
\t\t....\n"),
make_option(c("--chrA"), type="character", help="Chromosome A name. It will represent the rows in the inter-chromosomal matrix. It should be the <indexA> chromosome.\n"),
make_option(c("--chrB"), type="character", help="Chromosome B name. It will represent the columns in the inter-chromosomal matrix. It should be the <indexB> chromosome.\n"),
make_option(c("--prefix"), type="character", help="Prefix of the output file <prefix.chrA_chrB>. All the output files and folders will be generated with this prefix.\n"),
make_option(c("--covq"), type="numeric", default=0.1, help="Quantile value to be subtracted from one dimensional trans-coverage profile [trans.coverage - quantile(trans.coverage, covq)] [default 0.10].\n
\t\tBins with very low coverage values are removed with this filter.
\t\tIncreasing <covq> value will keep only the most stringent bins in the two chromosome.\n"),
make_option(c("--minzscore"), type="numeric", default=1, help="Minimum Zscore of a possible translocation box to be retained [default is 1].\n
\t\tHiCtrans will find enriched boxes within the inter-chromosomal matrix as potential translocation box.
\t\tThe enrichment is calculated as Z-score against a background with all possible similar sized boxes in the inter-chromosomal matrix.
\t\tIncreasing <minzscore> value will keep the most enriched trans interacting boxes.\n"),
make_option(c("--minboxsize"), type="numeric", default=0, help="Minimum size of a possible translocation box relative to its Hi-C resolution [default is 0 i.e. no filtering. If set to non-zero value, then (Breakpoint.start - Breakpoint.end)/HiC.resolution > minboxsize filtering will be applied].\n
\t\tHiCtrans will find enriched boxes within the inter-chromosomal matrix as potential translocation box.
\t\tThe minimum box size threshold will filter out small false positive multi-resolution supported potential translocation boxes.\n
\t\tIncreasing <minboxsize> value will keep the most enriched and larger trans interacting boxes.\n"),
make_option(c("--boxzscore"), type="numeric", default=1, help="Minimum Zscore of a possible translocation box to be retained [default is 1].\n
\t\tHiCtrans will keep boxes enriched above boxzscore threshold to find translocations among them.
\t\tIncreasing <boxzscore> value will keep the most enriched trans interacting boxes.\n"),
make_option(c("--locq"), type="numeric", default=0.1, help="Top percentile to be reported as possible breakpoints within a translocation box [default top 0.1%]\n
\t\tFor each enriched translocated boxes, HiCtrans will report top <locq>% interacting pairs (Weighted by the frequency of total interaction).
\t\tDecreasing <locq> will reduce the number of reported breakpoints within an enriched trans interacting box. A <locq> value of 0 will report only the top interacting pair\n"),
make_option(c("--mincount"), type="numeric", default=10, help="Minimum count of a possible breakpoint to be retained when compared to all possible chrA-chrB interaction [default cutoff is 10].\n
\t\tThis is an absolute minimum count cutoff to filter out breakpoints detected at any resolution.
\t\tIncreasing <mincount> value will keep only the most stringent interacting pair.\n"),
make_option(c("--glbq"), type="numeric", default=0.1, help="Percentile value for minimum count cutoff at each resolution [default cutoff is at top 0.1% of the count distribution].\n
\t\tThis is a relative count cutoff based on the inter-chromosomal count distribution determined for each resolution independently.
\t\tIncreasing <glbq> value keep only the most stringent interacting pair.\n"),
make_option(c("--resolutions"), type="character", default="2,4,5,10", help="Comma separated list of integers to be multiplied with the starting Hi-C resolution to get the lower resolutions [default 2,4,5,10].\n
\t\tHiCtrans will search for enriched trans-interacting boxes and breakpoint finding at different resolutions.
\t\tProvide only integer values in a comma separated list.\n"),
make_option(c("--multires"), type="numeric", default=2, help="Number of Hi-C resolutions at which the breakpoint should be supported with [default is at least 2 different resolutions].\n
\t\tHiCtrans wiil find enriched trans-interacting boxes and subsequent breakpoints at different resolutions. The ultimate goal is to find a true translocation supported by multiple resolutions.