## CNregions function modified to not throw errors in small sample sizes ##
CNregions.mod <- function (seg, epsilon = 0.005, adaptive = FALSE, rmCNV = FALSE,
cnv = NULL, frac.overlap = 0.5, rmSmallseg = TRUE, nProbes = 15)
{
colnames(seg) = c("sample", "chromosome", "start", "end",
"num.mark", "seg.mean")
seg = subset(seg, chromosome <= 22)
seg = seg[order(seg[, 1], seg[, 2], seg[, 3]), ]
if (rmSmallseg) {
seg = subset(seg, num.mark >= nProbes)
}
if (rmCNV) {
cat("Removing CNV...", "\n")
if (is.null(cnv))
stop("To remove CNV, please include the cnv file. Otherwise set rmCNV=F")
gr.cnv = GRanges(seqnames = as.character(cnv[, 1]), ranges = IRanges(start = as.numeric(cnv[,
2]), end = as.numeric(cnv[, 3])))
gr.seg = GRanges(seqnames = paste("chr", seg[, 2], sep = ""),
ranges = IRanges(start = seg[, 3], end = seg[, 4]))
overlap = findOverlaps(gr.cnv, gr.seg)
queryHits = queryHits(overlap)
subjectHits = subjectHits(overlap)
start = pmax(seg[subjectHits, 3], cnv[queryHits, 2])
end = pmin(seg[subjectHits, 4], cnv[queryHits, 3])
seg.length = seg[subjectHits, 4] - seg[subjectHits, 3]
seg.length[seg.length == 0] = 1
p.overlap = (end - start)/seg.length
cnv.idx = subjectHits[which(p.overlap >= frac.overlap)]
cnv.idx = unique(cnv.idx)
seg = seg[-cnv.idx, ]
}
if (dim(seg)[1] == 0)
stop("seg file is empty.")
samples = unique(seg$sample)
chr = start.maploc = end.maploc = nur = out = NULL
for(i in unique(as.numeric(seg[, 2]))) { #unique(as.numeric(seg[, 2]))
subdata = subset(seg, chromosome == i)
u.breakpts = sort(unique(subdata[, 3]))
l = length(u.breakpts)
outi = NULL
for (j in 1:length(samples)) {
sj = subset(subdata, sample == samples[j])
if (dim(sj)[1] == 0)
stop("Found sample(s) with no segments. Check parameter setting. Relax threshold values.")
outj = rep(NA, l)
sj$start[1] = u.breakpts[1]
idx = c(match(sj$start, u.breakpts), l)
outj = c(rep(sj[, 6], times = diff(idx)), tail(sj[,
6], 1))
outi = cbind(outi, outj)
}
# print(paste0(i,",",dim(outi)))
u.start = u.breakpts
u.se = sort(union(unique(subdata$end), unique(subdata$start)))
u.end = lapply(2:length(u.start), FUN = function(x) u.se[which(u.start[x] <=
u.se)[1]])
u.end = unlist(u.end)
u.end = u.end[complete.cases(u.end)]
u.end = c(u.end, max(u.se))
if(nrow(outi) > 1) adjrow.dist = apply(sqrt(diff(outi)^2), 1, mean)
else adjrow.dist = mean(sqrt(diff(outi[1,])^2))
adjrow.dist = c(0, adjrow.dist)
if (adaptive) {
epsilon = quantile(adjrow.dist, prob = 0.97)
}
seg.break = which(adjrow.dist >= epsilon)
if (length(seg.break) == 0)
stop("Check parameter setting. Set lower epsilon value.")
start = seg.break
if(length(u.start) > 1){
if (start[1] > 1) {
start = c(1, start)
}
end = seg.break - 1
if (end[1] == 0) {
end = end[-1]
}
len = dim(outi)[1]
if (tail(end, 1) < len) {
end = c(end, len)
}
}
else{
start = 1
end = 1
}
nuri = end - start + 1
rowidx = rep(c(1:length(nuri)), nuri)
nur = c(nur, nuri)
get.medoid = function(x) {
if (dim(x)[1] > 1) {
pam(x, k = 1)$medoid
}
else {
x
}
}
outi.medoid = by(outi, rowidx, get.medoid)
outi.medoid = matrix(unlist(outi.medoid), nrow = length(outi.medoid),
byrow = T)
start.maploc = c(start.maploc, u.start[start])
end.maploc = c(end.maploc, u.end[end])
chr = c(chr, rep(i, dim(outi.medoid)[1]))
out = rbind(out, outi.medoid)
}
colnames(out) = samples
rownames(out) = paste("chr", chr, ".", start.maploc, "-",
end.maploc, sep = "")
reducedM = t(out)
return(reducedM)
}
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