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segmentation.R
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segmentation.R
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# file: segmentation.R
# author: Gavin Ha, Ph.D.
# Justin Rhoades
# Dana-Farber Cancer Institute
# Broad Institute
# contact: <gavinha@broadinstitute.org>
# ULP-WGS website: http://www.broadinstitute.org/~gavinha/ULP-WGS/
# HMMcopy website: http://compbio.bccrc.ca/software/hmmcopy/ and https://www.bioconductor.org/packages/release/bioc/html/HMMcopy.html
# date: Oct 26, 2016
# description: Hidden Markov model (HMM) to analyze Ultra-low pass whole genome sequencing (ULP-WGS) data.
# This script is the main script to run the HMM.
HMMsegment <- function(x, validInd = NULL, dataType = "copy", param = NULL,
chrTrain = c(1:22), maxiter = 50, estimateNormal = TRUE, estimatePloidy = TRUE,
estimatePrecision = TRUE, estimateSubclone = TRUE, estimateTransition = TRUE,
estimateInitDist = TRUE, logTransform = FALSE, verbose = TRUE) {
chr <- as.factor(seqnames(x[[1]]))
# setup columns for multiple samples #
dataMat <- as.matrix(as.data.frame(lapply(x, function(y) { mcols(x[[1]])[, dataType] })))
# normalize by median and log data #
if (logTransform){
dataMat <- apply(dataMat, 2, function(x){ log(x / median(x, na.rm = TRUE)) })
}else{
dataMat <- log(2^dataMat)
}
## update variable x with loge instead of log2
for (i in 1:length(x)){
mcols(x[[i]])[, dataType] <- dataMat[, i]
}
if (!is.null(chrTrain)) {
chrInd <- chr %in% chrTrain
}else{
chrInd <- !logical(length(chr))
}
if (!is.null(validInd)){
chrInd <- chrInd & validInd
}
if (is.null(param)){
param <- getDefaultParameters(dataMat[chrInd])
}
#if (param$n_0 == 0){
# param$n_0 <- .Machine$double.eps
#}
####### RUN EM ##########
convergedParams <- runEM(dataMat, chr, chrInd, param, maxiter,
verbose, estimateNormal = estimateNormal, estimatePloidy = estimatePloidy,
estimateSubclone = estimateSubclone, estimatePrecision = estimatePrecision,
estimateTransition = estimateTransition, estimateInitDist = estimateInitDist)
# Calculate likelihood using converged params
# S <- param$numberSamples
# K <- length(param$ct)
# KS <- K ^ S
# py <- matrix(0, KS, nrow(dataMat))
# iter <- convergedParams$iter
# lambdasKS <- as.matrix(expand.grid(as.data.frame(convergedParams$lambda[, , iter])))
# for (ks in 1:KS) {
# probs <- tdistPDF(dataMat, convergedParams$mus[ks, , iter], lambdasKS[ks, ], param$nu)
# py[ks, ] <- apply(probs, 1, prod) # multiply across samples for each data point to get joint likelihood.
# }
#
viterbiResults <- runViterbi(convergedParams, chr)
# setup columns for multiple samples #
segs <- segmentData(x, validInd, viterbiResults$states, convergedParams)
#output$segs <- processSegments(output$segs, chr, start(x), end(x), x$DataToUse)
names <- c("HOMD","HETD","NEUT","GAIN","AMP","HLAMP",paste0(rep("HLAMP", 8), 2:25))
#if (c(0) %in% param$ct){ #if state 0 HOMD is IN params#
#names <- c("HOMD", names)
# shift states to start at 2 (HETD)
#tmp <- lapply(segs, function(x){ x$state <- x$state + 1; x})
#viterbiResults$states <- as.numeric(viterbiResults$states) + 1
#}
### PUTTING TOGETHER THE COLUMNS IN THE OUTPUT ###
cnaList <- list()
S <- length(x)
for (s in 1:S){
id <- names(x)[s]
copyNumber <- param$jointCNstates[viterbiResults$state, s]
subclone.status <- param$jointSCstatus[viterbiResults$state, s]
cnaList[[id]] <- data.frame(cbind(sample = as.character(id),
chr = as.character(seqnames(x[[s]])),
start = start(x[[s]]), end = end(x[[s]]),
copy.number = copyNumber,
event = names[copyNumber + 1],
logR = round(log2(exp(dataMat[,s])), digits = 4),
subclone.status = as.numeric(subclone.status)
))
cnaList[[id]] <- transform(cnaList[[id]],
start = as.integer(as.character(start)),
end = as.integer(as.character(end)),
copy.number = as.numeric(copy.number),
logR = as.numeric(as.character(logR)),
subclone.status = as.numeric(subclone.status))
## order by chromosome ##
chrOrder <- unique(chr) #c(1:22,"X","Y")
cnaList[[id]] <- cnaList[[id]][order(match(cnaList[[id]][, "chr"],chrOrder)),]
## remove MT chr ##
cnaList[[id]] <- cnaList[[id]][cnaList[[id]][,"chr"] %in% chrOrder, ]
## segment mean loge -> log2
#segs[[s]]$median.logR <- log2(exp(segs[[s]]$median.logR))
segs[[s]]$median <- log2(exp(segs[[s]]$median))
## add subclone status
segs[[s]]$subclone.status <- param$jointSCstatus[segs[[s]]$state, s]
}
convergedParams$segs <- segs
return(list(cna = cnaList, results = convergedParams, viterbiResults = viterbiResults))
}
getTransitionMatrix <- function(K, e, strength){
A <- matrix(0, K, K)
for (j in 1:K) {
A[j, ] <- (1 - e[1]) / (K - 1)
A[j, j] <- e[1]
}
A <- normalize(A)
A_prior <- A
dirPrior <- A * strength[1]
return(list(A=A, dirPrior=dirPrior))
}
getDefaultParameters <- function(x, maxCN = 5, ct.sc = NULL, ploidy = 2, e = 0.9999999, e.sameState = 10, strength = 10000000, includeHOMD = FALSE){
if (includeHOMD){
ct <- 0:maxCN
}else{
ct <- 1:maxCN
}
param <- list(
strength = strength, e = e,
ct = c(ct, ct.sc),
ct.sc.status = c(rep(FALSE, length(ct)), rep(TRUE, length(ct.sc))),
phi_0 = 2, alphaPhi = 4, betaPhi = 1.5,
n_0 = 0.5, alphaN = 2, betaN = 2,
sp_0 = 0.5, alphaSp = 2, betaSp = 2,
lambda = as.matrix(rep(100, length(ct)+length(ct.sc)), ncol=1),
nu = 2.1,
kappa = rep(75, length(ct)),
alphaLambda = 5
)
K <- length(param$ct)
## initialize hyperparameters for precision using observed data ##
if (!is.null(dim(x))){ # multiple samples (columns)
param$numberSamples <- ncol(x)
#betaLambdaVal <- ((apply(x, 2, function(x){ sd(diff(x), na.rm=TRUE) }) / sqrt(length(param$ct))) ^ 2)
betaLambdaVal <- ((apply(x, 2, sd, na.rm = TRUE) / sqrt(length(param$ct))) ^ 2)
}else{ # only 1 sample
param$numberSamples <- 1
betaLambdaVal <- ((sd(x, na.rm = TRUE) / sqrt(length(param$ct))) ^ 2)
}
param$betaLambda <- matrix(betaLambdaVal, ncol = param$numberSamples, nrow = length(param$ct), byrow = TRUE)
param$alphaLambda <- rep(param$alphaLambda, K)
# increase prior precision for -1, 0, 1 copies at ploidy
#param$lambda[param$ct %in% c(1,2,3)] <- 1000 # HETD, NEUT, GAIN
#param$lambda[param$ct == 4] <- 100
#param$lambda[which.max(param$ct)] <- 50 #highest CN
#param$lambda[param$ct == 0] <- 1 #HOMD
S <- param$numberSamples
logR.var <- 1 / ((apply(x, 2, sd, na.rm = TRUE) / sqrt(length(param$ct))) ^ 2)
if (!is.null(dim(x))){ # multiple samples (columns)
param$lambda <- matrix(logR.var, nrow=K, ncol=S, byrow=T, dimnames=list(c(),colnames(x)))
}else{ # only 1 sample
#logR.var <- 1 / ((sd(x, na.rm = TRUE) / sqrt(length(param$ct))) ^ 2)
param$lambda <- matrix(logR.var, length(param$ct))
param$lambda[param$ct %in% c(2)] <- logR.var
param$lambda[param$ct %in% c(1,3)] <- logR.var
param$lambda[param$ct >= 4] <- logR.var / 5
param$lambda[param$ct == max(param$ct)] <- logR.var / 15
param$lambda[param$ct.sc.status] <- logR.var / 10
}
# define joint copy number states #
param$jointCNstates <- expand.grid(rep(list(param$ct), S))
param$jointSCstatus <- expand.grid(rep(list(param$ct.sc.status), S))
colnames(param$jointCNstates) <- paste0("Sample.", 1:param$numberSamples)
colnames(param$jointSCstatus) <- paste0("Sample.", 1:param$numberSamples)
# Initialize transition matrix to the prior
txn <- getTransitionMatrix(K ^ S, e, strength)
## set higher transition probs for same CN states across samples ##
# joint states where at least "tol" fraction of samples with the same CN state
#apply(param$jointCNstates, 1, function(x){ sum(duplicated(as.numeric(x))) > 0 })
cnStateDiff <- apply(param$jointCNstates, 1, function(x){ (abs(max(x) - min(x)))})
if (e.sameState > 0 & S > 1){
txn$A[, cnStateDiff == 0] <- txn$A[, cnStateDiff == 0] * e.sameState * K
txn$A[, cnStateDiff >= 3] <- txn$A[, cnStateDiff >=3] / e.sameState / K
}
for (i in 1:nrow(txn$A)){
for (j in 1:ncol(txn$A)){
if (i == j){
txn$A[i, j] <- e
}
}
}
txn$A <- normalize(txn$A)
param$A <- txn$A
param$dirPrior <- txn$A * strength[1]
param$A[, param$ct.sc.status] <- param$A[, param$ct.sc.status] / 10
param$A <- normalize(param$A)
param$dirPrior[, param$ct.sc.status] <- param$dirPrior[, param$ct.sc.status] / 10
if (includeHOMD){
K <- length(param$ct)
param$A[1, 2:K] <- param$A[1, 2:K] * 1e-5; param$A[2:K, 1] <- param$A[2:K, 1] * 1e-5;
param$A[1, 1] <- param$A[1, 1] * 1e-5
param$A <- normalize(param$A); param$dirPrior <- param$A * param$strength
}
param$kappa <- rep(75, K ^ S)
param$kappa[cnStateDiff == 0] <- param$kappa[cnStateDiff == 0] + 125
param$kappa[cnStateDiff >=3] <- param$kappa[cnStateDiff >=3] - 50
param$kappa[which(rowSums(param$jointCNstates==2) == S)] <- 800
return(param)
}
segmentData <- function(dataGR, validInd, states, convergedParams){
if (sum(convergedParams$param$ct == 0) ==0){
includeHOMD <- FALSE
}else{
includeHOMD <- TRUE
}
if (!includeHOMD){
names <- c("HETD","NEUT","GAIN","AMP","HLAMP",paste0(rep("HLAMP", 8), 2:25))
}else{
names <- c("HOMD","HETD","NEUT","GAIN","AMP","HLAMP",paste0(rep("HLAMP", 8), 2:25))
}
states <- states[validInd]
S <- length(dataGR)
jointStates <- convergedParams$param$jointCNstates
jointSCstatus <- convergedParams$param$jointSCstatus
colNames <- c("seqnames", "start", "end", "copy")
segList <- list()
for (i in 1:S){
id <- names(dataGR)[i]
dataIn <- dataGR[[i]][validInd, ]
rleResults <- t(sapply(runValue(seqnames(dataIn)), function(x){
ind <- as.character(seqnames(dataIn)) == x
r <- rle(states[ind])
}))
rleLengths <- unlist(rleResults[, "lengths"])
rleValues <- unlist(rleResults[, "values"])
sampleDF <- as.data.frame(dataIn)
numSegs <- length(rleLengths)
segs <- as.data.frame(matrix(NA, ncol = 7, nrow = numSegs,
dimnames = list(c(), c("chr", "start", "end", "state", "event", "median", "copy.number"))))
prevInd <- 0
for (j in 1:numSegs){
start <- prevInd + 1
end <- prevInd + rleLengths[j]
segDF <- sampleDF[start:end, colNames]
prevInd <- end
numR <- nrow(segDF)
segs[j, "chr"] <- as.character(segDF[1, "seqnames"])
segs[j, "start"] <- segDF[1, "start"]
segs[j, "state"] <- rleValues[j]
segs[j, "copy.number"] <- jointStates[rleValues[j], i]
if (segDF[1, "seqnames"] == segDF[numR, "seqnames"]){
segs[j, "end"] <- segDF[numR, "end"]
segs[j, "median"] <- round(median(segDF$copy, na.rm = TRUE), digits = 6)
if (includeHOMD){
segs[j, "event"] <- names[segs[j, "copy.number"] + 1]
}else{
segs[j, "event"] <- names[segs[j, "copy.number"]]
}
}else{ # segDF contains 2 different chromosomes
print(j)
}
}
segList[[id]] <- segs
}
return(segList)
}
runViterbi <- function(convergedParams, chr){
message("runViterbi: Segmenting and classifying")
chrs <- levels(chr)
chrsI <- vector('list', length(chrs))
# initialise the chromosome index and the init state distributions
for(i in 1:length(chrs)) {
chrsI[i] <- list(which(chr == chrs[i]))
}
segs <- vector('list', length(chrs))
py <- convergedParams$py
N <- ncol(py)
Z <- rep(0, N)
convergeIter <- convergedParams$iter
piG <- convergedParams$pi[, convergeIter]
A <- convergedParams$A
for(c in 1:length(chrsI)) {
I <- chrsI[[c]]
output <- .Call("viterbi", log(piG), log(A), log(py[, I]), PACKAGE = "HMMcopy")
Z[I] <- output$path
segs[[c]] <- output$seg
}
return(list(segs=segs, states=Z))
}
# Normalize a given array to sum to 1
normalize <- function(A) {
vectorNormalize <- function(x){ x / (sum(x) + (sum(x) == 0)) }
if (length(dim(A)) < 2){
M <- vectorNormalize(A)
}else{
M <- t(apply(A, 1, vectorNormalize))
}
return(M);
}
# processSegments <- function(seg, chr, start, end, copy) {
# segment <- data.frame()
# chromosomes <- levels(chr)
# for (i in 1:length(chromosomes)) {
# seg_length = dim(seg[[i]])[1]
# chr_name <- rep(chromosomes[i], seg_length)
# chr_index <- which(chr == chromosomes[i])
# chr_start <- start[chr_index][seg[[i]][, 1]]
# chr_stop <- end[chr_index][seg[[i]][, 2]]
# chr_state <- seg[[i]][, 3]
# chr_median <- rep(0, seg_length)
# for(j in 1:seg_length) {
# chr_median[j] <-
# median(na.rm = TRUE, log2(exp(copy[chr_index][seg[[i]][j, 1]:seg[[i]][j, 2]])))
# }
# segment <- rbind(segment, cbind(chr = chr_name,
# start = as.numeric(chr_start), end = chr_stop, state = chr_state,
# median = chr_median))
# }
# segment <- transform(segment, start = as.numeric(as.character(start)),
# end = as.numeric(as.character(end)), as.numeric(as.character(state)),
# median = as.numeric(as.character(median)))
# return(segment)
# }