R/helper_functions.R
In kdkorthauer/dmrseq: Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing
Defines functions
bumphunter.greaterOrEqual
estim
getEstimate
smoother
regionScanner
trimEdges
refineEdges
bumphunt
getEstimatePooled
#' BS.chr21: Whole-genome bisulfite sequencing for chromosome 21
#' from Lister et al.
#'
#' @description This dataset represents chromosome 21
#' from the IMR90 and H1 cell lines sequenced in Lister et al.
#' Only CpG methylation are included. The two samples from
#' each cell line are two different extractions (ie. technical replicates),
#' and are pooled in the analysis in the original paper.
#' @usage data(BS.chr21)
#' @format An object of class \code{BSseq}.
#'
#' @details All coordinates are in hg18.
#' @source Obtained from
#' \url{http://neomorph.salk.edu/human_methylome/data.html} specifically,
#' the files \url{mc_h1_r1.tar.gz}, \url{mc_h1_r2.tar.gz},
#' \url{mc_imr90_r1.tar.gz}, \url{mc_imr90_r2.tar.gz}
#' A script which downloads these files and constructs the \code{BS.chr21}
#' object may be found in \file{inst/scripts/get_BS.chr21.R} - this was
#' based off of and modified from the get_BS.chr22.R script in the
#' \code{bsseq} package. The object constructed here contains a
#' different chromosome (22 instead of 21), and two additional samples
#' (h1 and imr90 instead of just imr90) to enable identification of
#' cell type-DMRs for examples.
#' @references R Lister et al. \emph{Human DNA methylomes at base
#' resolution show widespread epigenomic differences}. Nature (2009) 462,
#' 315-322.
#' @examples
#' data(BS.chr21)
#' BS.chr21
"BS.chr21"
#' annot.chr21: Annotation information for chromosome 21, hg38 genome
#'
#' @description This is the annotation information returned from
#' \code{\link{getAnnot}}, subsetted for chromosome 21 for convenience
#' in running the examples. The annotation is obtained using the
#' \code{annotatr} package.
#'
#' @usage data(annot.chr21)
#'
#' @format a \code{GRangesList} object with two elements returned
#' by \code{\link{getAnnot}}. The first
#' contains CpG category information in the first element (optional)
#' coding gene sequence information in the second element (optional).
#' At least one of these elements needs to be non-null in order for
#' any annotation to be plotted, but it is not necessary to contain
#' both.
#'
#' @source Obtained from running
#' \code{annoTrack} function and then subsetting the results to
#' only include chromosome 21. A script which executes these steps
#' and constructs the \code{annot.chr21}
#' object may be found in \file{inst/scripts/get_annot.chr21.R}
#'
#' @examples
#' data(annot.chr21)
"annot.chr21"
#' dmrs.ex: Example results of DMRs
#'
#'@description Example output from \code{dmrseq} function run on the
#' example dataset \code{BS.chr21}.
#' @usage data(dmrs.ex)
#' @format a data.frame that contains the results of the inference. The
#' data.frame contains one row for each candidate region, and
#' 10 columns, in the following order: 1. chr =
#' chromosome, 2. start =
#' start basepair position of the region, 3. end = end basepair position
#' of the region,
#' 4. indexStart = the index of the region's first CpG,
#' 5. indexEnd = the index of the region's last CpG,
#' 6. L = the number of CpGs contained in the region,
#' 7. area = the sum of the smoothed beta values
#' 8. beta = the coefficient value for the condition difference,
#' 9. stat = the test statistic for the condition difference,
#' 10. pval = the permutation p-value for the significance of the test
#' statistic, and
#' 11. qval = the q-value for the test statistic (adjustment
#' for multiple comparisons to control false discovery rate).
#' @source Obtained from running the examples in \code{\link{dmrseq}}.
#' A script which executes these steps
#' and constructs the \code{dmrs.ex}
#' object may be found in \file{inst/scripts/get_dmrs.ex.R}
#' @examples
#' data(dmrs.ex)
"dmrs.ex"
getEstimatePooled <- function(meth.mat = meth.mat, cov.mat = cov.mat, pos = pos,
chr = chr, design, coeff) {
# check whether the covariate of interest is a two group comparison if not
# (covariate is a multi-level factor or a continuous variable) then use
# single-loci estimate of methylation effect instead of pooled mean diff
if (length(unique(design[, coeff])) == 2) {
lev1 <- 1
lev2 <- 0
# pooled
est <- DelayedMatrixStats::rowSums2(meth.mat[,
unname(design[, coeff] == lev1),
drop = FALSE])/
DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[,coeff] == lev1),
drop = FALSE]) -
DelayedMatrixStats::rowSums2(meth.mat[,
unname(design[, coeff] == lev2),
drop = FALSE])/
DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[, coeff] == lev2),
drop = FALSE])
sd <- cbind(DelayedMatrixStats::rowMads(meth.mat[,
unname(design[, coeff] == lev1)]/
cov.mat[, unname(design[, coeff] == lev1)], na.rm=TRUE)^2,
DelayedMatrixStats::rowMads(meth.mat[,
unname(design[, coeff] == lev2)]/
cov.mat[, unname(design[, coeff] == lev2)], na.rm=TRUE)^2)
# when sd is zero because one of the groups had only a single sample
# with nonzero coverage, impute with the other group's sd est
sd[DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[, coeff] == lev1), drop = FALSE] > 0) == 1 &
DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[, coeff] == lev2), drop = FALSE] > 0) == 1,] <- 1
sd[DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[, coeff] == lev1), drop = FALSE] > 0) == 1,1] <-
sd[DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[, coeff] == lev1), drop = FALSE] > 0) == 1,2]
sd[DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[, coeff] == lev2), drop = FALSE] > 0) == 1,2] <-
sd[DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[, coeff] == lev2), drop = FALSE] > 0) == 1,1]
sd <- 1.4826 * sqrt(DelayedMatrixStats::rowSums2(sd))
return(list(rawBeta = est, sd = sd))
} else {
# continuous or multi-level factor case
stop("Error: don't use pooled estimate when there ",
"are more than 2 groups")
}
}
bumphunt <- function(bs,
design, coeff = 2, coeff.adj = 3,
minInSpan = 30, minNumRegion = 5,
cutoff = NULL, maxGap = 1000, maxGapSmooth = 2500, smooth = FALSE,
bpSpan = 1000, verbose = TRUE, parallel = FALSE, block = FALSE,
blockSize = 5000, chrsPerChunk = 1, fact = FALSE,
adjustCovariate = NULL, ...) {
# calculate smoothing span from minInSpan
bpSpan2 <- NULL
chrs <- as.character(unique(seqnames(bs)))
if (!block){ # don't balance minInSpan and bpSpan for blocks
for (ch in chrs) {
pos <- chrSelectBSseq(bs, ch)
bpSpan2 <- c(bpSpan2, minInSpan * (max(start(pos)) -
min(start(pos)) + 1)/sum(seqnames(pos) == ch))
}
bpSpan2 <- mean(bpSpan2, na.rm = TRUE)
}
# get 75th percentile of mean coverage genomewide before subsetting
# by chromosome
covQ75 <- quantile(DelayedMatrixStats::rowMeans2(getCoverage(bs, type="Cov")), 0.75)
if (chrsPerChunk > 1){
sizeRank <- rank(-seqnames(bs)@lengths, ties.method = "first")
nChunks <- ceiling(length(unique(seqnames(bs))) / chrsPerChunk)
all.chrs <- as.character(unique(seqnames(bs)))
chrs <- vector("list", nChunks)
chunk <- 1
while ((chunk <= nChunks || length(chrs[[chunk]]) < chrsPerChunk) &&
length(all.chrs) > 0 ){
largest <- which.min(sizeRank)
smallest <- which.max(sizeRank)
if (largest != smallest && ( length(chrs[[chunk]]) == 0 ||
(chrsPerChunk - length(chrs[[chunk]])) > 1 )){
chrs[[chunk]] <- c(chrs[[chunk]], all.chrs[c(largest, smallest)])
all.chrs <- all.chrs[-c(largest,smallest)]
sizeRank <- sizeRank[-c(largest,smallest)]
}else{
chrs[[chunk]] <- c(chrs[[chunk]], all.chrs[largest])
all.chrs <- all.chrs[-largest]
sizeRank <- sizeRank[-largest]
}
# put in original seqlevel order
chrs[[chunk]] <- as.character(seqnames(chrSelectBSseq(bs,
chrs[[chunk]]))@values)
if(length(chrs[[chunk]]) == chrsPerChunk){
chunk <- min(nChunks, chunk + 1)
}
}
}
tab <- NULL
for (chromosome in chrs) {
meth.mat <- getCoverage(chrSelectBSseq(bs, chromosome),
type = "M")
cov.mat <- getCoverage(chrSelectBSseq(bs, chromosome),
type = "Cov")
pos <- start(chrSelectBSseq(bs, chromosome))
chr <- as.character(seqnames(chrSelectBSseq(bs, chromosome)))
if (verbose)
message("...Chromosome ", paste(chromosome, collapse = ", "),
": ", appendLF = FALSE)
# skip chromosomes that have fewer than minNumRegion loci
if (length(pos) < minNumRegion){
message("No candidates found.")
next
}
cov.means <- DelayedMatrixStats::rowMeans2(cov.mat)
if (length(unique(design[, coeff])) == 2 &&
length(coeff) == 1 &&
all.equal(sort(unique(as.vector(design[,coeff])))[seq_len(2)],
c(0,1))) {
tmp <- getEstimatePooled(meth.mat = meth.mat,
cov.mat = cov.mat, pos = pos,
chr = chr, design, coeff)
rawBeta <- tmp$rawBeta
sd.raw <- tmp$sd
} else {
tmp <- estim(meth.mat = meth.mat, cov.mat = cov.mat,
pos = pos, chr = chr,
design = design, coeff = coeff, coeff.adj = coeff.adj)
rawBeta <- tmp$meth.diff
sd.raw <- tmp$sd.meth.diff
}
sd.raw[sd.raw < 1e-05] <- 1e-05
# truncate coverage at 75th percentile
# minimum sd proportional to median coverage
weights <- pmin(cov.means, covQ75) /
pmax(sd.raw, 1/pmax(cov.means, 5))
if (smooth) {
beta <- vector("list", 2)
beta[[1]] <- beta[[2]] <- rep(NA, length(pos))
beta.tmp <- smoother(y = rawBeta,
x = pos, chr = chr,
maxGapSmooth = maxGapSmooth,
weights = weights,
minNumRegion = minNumRegion,
minInSpan = minInSpan,
bpSpan = bpSpan, bpSpan2 = bpSpan2,
verbose = verbose,
parallel = parallel)
beta[[1]] <- beta.tmp[[1]]
beta[[2]] <- beta.tmp[[2]]
names(beta) <- names(beta.tmp)
Index <- which(beta$smoothed)
beta <- beta$fitted
} else {
beta <- rawBeta
Index <- seq(along = beta)
}
if (length(unique(as.vector(design[, coeff]))) == 2 &&
length(coeff) == 1 &&
all.equal(sort(unique(as.vector(design[,coeff])))[seq_len(2)],c(0,1))) {
ngroups <- length(unique(design[,coeff]))
rawBeta <- rawBeta/(sd.raw * ngroups / sqrt(nrow(design)))
}else{
rawBeta <- rawBeta/sd.raw
}
if(length(Index) > 0){
beta[-Index] <- rawBeta[-Index]
}else{ # if no loci were smoothed, use raw ests instead of NA
beta <- rawBeta
}
tab <- rbind(tab,
regionScanner(meth.mat = meth.mat, cov.mat = cov.mat, pos = pos,
chr = chr, x = beta, y = rawBeta, maxGap = maxGap, cutoff = cutoff,
minNumRegion = minNumRegion, design = design, coeff = coeff,
coeff.adj = coeff.adj,
verbose = verbose, parallel = parallel,
pDat=pData(bs), block = block, blockSize = blockSize, fact = fact,
adjustCovariate = adjustCovariate))
}
if (length(tab) == 0) {
if (verbose)
message("No regions found.")
return(NULL)
}
# convert index from within chromosome to overall
if (length(chrs) > 0){
if (is.list(chrs)){ # make length relative to single chrs
for(ch in chrs){
if(length(ch) > 1){
sublengths <- cumsum(table(seqnames(chrSelectBSseq(bs, ch))))
for (j in seq_len(length(ch)-1)) {
tab$indexStart[tab$chr %in% ch[j+1]] <- tab$indexStart[tab$chr %in% ch[j+1]] -
sublengths[names(sublengths) == ch[j]]
tab$indexEnd[tab$chr %in% ch[j+1]] <- tab$indexEnd[tab$chr %in% ch[j+1]] -
sublengths[names(sublengths) == ch[j]]
}
}
}
}
chrs <- unique(as.character(seqnames(bs)))
chrlengths <- table(seqnames(bs))
chrlengths <- chrlengths[chrs]
chrlengths <- cumsum(chrlengths)
# Add a check to make sure chr order lines up with
# cumulative sum table
if (identical(chrs, names(chrlengths))){
for (j in seq_len(length(chrs)-1)) {
ch <- chrs[[j+1]]
tab$indexStart[tab$chr %in% ch] <- tab$indexStart[tab$chr %in% ch] +
chrlengths[names(chrlengths) == chrs[j]]
tab$indexEnd[tab$chr %in% ch] <- tab$indexEnd[tab$chr %in% ch] +
chrlengths[names(chrlengths) == chrs[j]]
}
}else{
stop("Chromosome order could not be reliably determined. ",
"Please use the 'sort' function before running dmrseq")
}
}
return(tab)
}
refineEdges <- function(y, candidates = NULL,
cutoff = qt(0.975, nrow(design) - 2), verbose = FALSE,
minNumRegion, design) {
stopifnot(length(cutoff) <= 2)
stopifnot(is.list(candidates) && length(candidates) == 2)
if (verbose)
message("refineEdges: refining")
direction <- as.integer(bumphunter.greaterOrEqual(y, cutoff))
direction[y <= -cutoff] <- -1L
refineOne <- function(x, sig) {
idx <- which(direction[x] == sig)
if (length(idx) > 0) {
if (length(seq(min(idx),max(idx))) >= minNumRegion) {
x[seq(min(idx),max(idx))]
} else {
x
}
} else {
x
}
}
refineLong <- function(candidates, direction, minNumRegion, sig){
if (length(candidates) > 0) {
which.long <- which(lengths(candidates) > minNumRegion)
if (length(which.long) > 1) {
candidates[which.long] <- lapply(candidates[which.long],
FUN=refineOne, sig=sig)
candidates[vapply(candidates, is.null, FUN.VALUE=logical(1))] <- NULL
} else if (length(which.long) == 1) {
candidates[[which.long]] <- unlist(lapply(candidates[which.long],
FUN=refineOne, sig=sig))
}
}
return(candidates)
}
trimmed <- vector("list", 2)
trimmed[[1]] <- refineLong(candidates[[1]], direction,
minNumRegion, sig = 1)
trimmed[[2]] <- refineLong(candidates[[2]], direction,
minNumRegion, sig = -1)
return(trimmed)
}
trimEdges <- function(x, candidates = NULL, verbose = FALSE, minNumRegion) {
stopifnot(is.list(candidates) && length(candidates) == 2)
if (verbose)
message("trimEdges: trimming")
trimOne <- function(w, x) {
mid <- which.max(x[w])
new.start <- 1
new.end <- length(w)
if (x[w[mid]]/min(x[w]) > 4/3) {
if (sum(!is.na(x[w[1]:w[mid]])) > 4) {
fit1 <- lm(x[w[seq_len(mid)]] ~ w[seq_len(mid)])
if (length(summary(fit1)) > 0) {
if (nrow(summary(fit1)$coef) == 2) {
if (summary(fit1)$coef[2, 1] > 0 &&
summary(fit1)$coef[2, 4] < 0.01) {
new.cut <- (0.5 * (x[w[mid]] - min(x[w])) +
min(x[w]) + 0.75 * mean(x[w]))/2
new.start <- min(max(1, round(mid - 0.125 * length(w))),
max(1, mid - 2),
(seq_len(mid))[min(which(x[w[seq_len(mid)]] >=
new.cut))])
}
}
}
}
if (sum(!is.na(x[w[mid]:w[length(w)]])) > 4) {
fit2 <- lm(x[w[seq(mid,length(w))]] ~ w[seq(mid,length(w))])
if (length(fit2) > 0) {
if (nrow(summary(fit2)$coef) == 2) {
if (summary(fit2)$coef[2, 1] < 0 &&
summary(fit2)$coef[2, 4] < 0.01) {
new.cut <- (0.5 * (x[w[mid]] - min(x[w])) +
min(x[w]) + 0.75 * mean(x[w]))/2
new.end <- max(min(round(mid + 0.125 * length(w)),
length(w)), min(mid + 2, length(w)),
(seq(mid,length(w)))[max(which(x[w[seq(mid,
length(w))]] >= new.cut))])
}
}
}
}
}
if (length(seq(new.start,new.end)) >= minNumRegion) {
return(w[seq(new.start,new.end)])
} else {
return(w)
}
}
trimLong <- function(x, candidates, minNumRegion, sig){
if (length(candidates) > 0) {
x <- x * sig
which.long <- which(lengths(candidates) > minNumRegion)
if (length(which.long) > 1) {
candidates[which.long] <- lapply(candidates[which.long],
FUN=trimOne, x=x)
candidates[vapply(candidates, is.null, FUN.VALUE=logical(1))] <- NULL
} else if (length(which.long) == 1) {
candidates[[which.long]] <- unlist(lapply(candidates[which.long],
FUN=trimOne, x=x))
}
}
return(candidates)
}
trimmed <- vector("list", 2)
trimmed[[1]] <- trimLong(x, candidates[[1]], minNumRegion, sig = 1)
trimmed[[2]] <- trimLong(x, candidates[[2]], minNumRegion, sig = -1)
return(trimmed)
}
regionScanner <- function(meth.mat = meth.mat, cov.mat = cov.mat, pos = pos,
chr = chr, x, y = x, ind = seq(along = x), order = TRUE, minNumRegion = 5,
maxGap = 300, cutoff = quantile(abs(x), 0.99), assumeSorted = FALSE,
verbose = verbose, design = design, coeff = coeff, coeff.adj = coeff.adj,
parallel = parallel, pDat, block, blockSize, fact = fact,
adjustCovariate = NULL) {
if (any(is.na(x[ind]))) {
message(sum(is.na(x[ind]))," CpG(s) excluded due to zero coverage. ",
appendLF = FALSE)
ind <- intersect(which(!is.na(x)), ind)
}
# remove any empty factor levels
for (f in adjustCovariate){
if (is.factor(pDat[,f]))
pDat[,f] <- droplevels(pDat[,f])
}
cluster <- bumphunter::clusterMaker(factor(chr, levels=unique(chr)),
pos, maxGap = maxGap,
assumeSorted = assumeSorted)
Indexes <- bumphunter::getSegments(x = x[ind], f = cluster[ind],
cutoff = cutoff,
assumeSorted = TRUE,
verbose = FALSE)
# only keep up and down indices
Indexes <- Indexes[seq_len(2)]
if (sum(lengths(Indexes)) == 0) {
message("No candidates found. ")
return(NULL)
}
# refine edges -> start = first (stop = last) position with a raw difference
# that meets the threshold
Indexes <- refineEdges(y = y[ind], candidates = Indexes, cutoff = cutoff,
verbose = FALSE, minNumRegion = minNumRegion, design = design)
if (sum(lengths(Indexes)) == 0) {
message("No candidates found. ")
return(NULL)
}
# refine edges II -> for larger regions, when effect size changes over the
# region, remove portions at the beginning and end where effect size is
# drastically different than overall effect size
Indexes <- trimEdges(x = x[ind], candidates = Indexes, verbose = FALSE,
minNumRegion = minNumRegion)
if (sum(lengths(Indexes)) == 0) {
message("No candidates found. ")
return(NULL)
}
for (i in seq_len(2)) {
# get number of loci in region
lns <- lengths(Indexes[[i]])
Indexes[[i]] <- Indexes[[i]][lns >= minNumRegion]
}
if (sum(lengths(Indexes)) == 0) {
message("No candidates found. ")
return(NULL)
}
if(block){
# merge small candidate regions that are the same direction &
# are less than 1kb apart
# and on SAME chr
df <- S4Vectors::DataFrame(ind, chr = chr[ind], pos = pos[ind])
for(j in seq_along(Indexes)){
if (length(Indexes[[j]]) > 0){
reg <- as.data.frame(S4Vectors::aggregate(df,
S4Vectors::List(Indexes[[j]]),
chr = unlist(IRanges::heads(chr, 1L)),
Start = min(pos),
End = max(pos)))
reg <- GRanges(seqnames=reg$chr,
IRanges(start=reg$Start, end=reg$End))
# find 1kb flanking regions with no covered CpGs
flk <- c(IRanges::flank(reg, width=1000, start=TRUE),
IRanges::flank(reg, width=1000, start=FALSE))
start(flk) <- pmax(1, start(flk)) # don't let neg positions
overlap <- unique(IRanges::findOverlaps(flk, GRanges(seqnames=chr,
IRanges::IRanges(start=pos, end=pos)))@from)
if (length(overlap) > 0)
flk <- flk[-overlap]
# add back to regions and remake indice
reg <- as.matrix(as.data.frame(IRanges::reduce(c(flk, reg)))[,1:3])
idx <- apply(reg, 1, function(x) which(pos[ind] %in% x[2]:x[3] &
chr[ind] %in% x[1]))
if (is(idx, "list")){
Indexes[[j]] <- idx
}else{
Indexes[[j]] <- list(as.vector(idx))
}
}
}
# subset on those with at least blockSize bps
Indexes <- c(Indexes[[1]], Indexes[[2]])
nbp <- as.data.frame(S4Vectors::aggregate(df, S4Vectors::List(Indexes),
nbp = max(pos) - min(pos) + 1 ))$nbp
Indexes <- Indexes[nbp >= blockSize]
}else{
Indexes <- c(Indexes[[1]], Indexes[[2]])
maxLength <- max(lengths(Indexes))
if (maxLength > 1000) {
message("Note: Large candidate regions with more than 1000 ",
"CpGs detected.",
" If you'd like to detect ",
"large-scale blocks, set block=TRUE.",
" If you'd like to detect local DMRs, it is recommended to ",
"decrease the values of bpSpan, minInSpan, and/or ",
"maxGapSmooth increase computational efficiency.")
}
}
# only keep regions with replicates in each group for at least two CpGs
# for factor comparisons
replicateStatus <- function(candidates, design, coeff, fact){
levs <- unique(design[, coeff, drop = FALSE])
if(!is(levs, "matrix"))
levs <- as.matrix(levs)
if (fact){
indexRanges <- IRanges(unlist(lapply(candidates, min)),
unlist(lapply(candidates, max)))
cov.mat.cand <- extractROWS(cov.mat, indexRanges)
prev.mat <- rep(TRUE, nrow(cov.mat.cand))
for (l in seq_len(nrow(levs))){
cov.matl <- DelayedMatrixStats::rowSums2(
cov.mat.cand[,apply(design[, coeff, drop = FALSE], 1,
function(x) identical(unname(x),
unname(levs[l,]))), drop = FALSE] > 0) > 0 &
prev.mat
prev.mat <- cov.matl
}
rel <- unlist(lapply(IRanges::relist(cov.matl,indexRanges), sum))
return(which(rel > 1))
}else{
return(seq_along(candidates))
}
}
Indexes <- Indexes[replicateStatus(Indexes, design, coeff, fact)]
if (length(Indexes) == 0) {
message("No candidates found. ")
return(NULL)
}
asin.gls.cov <- function(ix, design, coeff,
correlation = corAR1(form = ~1 |sample),
correlationSmall = corCAR1(form = ~L | sample),
weights = varPower(form = ~1/MedCov, fixed = 0.5),
fact) {
if (is.factor(pDat[,colnames(design)[coeff[1]]])) # drop unused levels of test
pDat[,colnames(design)[coeff[1]]] <- droplevels(pDat[,colnames(design)[coeff[1]]])
dat <- data.frame(g.fac = rep(pDat[,colnames(design)[coeff[1]]],
each = length(ix)),
sample = factor(rep(seq_len(nrow(design)),
each=length(ix))),
meth = as.vector(meth.mat[ix, ]),
cov = as.vector(cov.mat[ix, ]),
L = as.vector(rep(pos[ix], nrow(design))))
if(length(coeff.adj) > 0){
for (k in adjustCovariate){
dat[,colnames(pDat)[k]] <-
rep(pDat[,colnames(pDat)[k]], each=length(ix))
}
}
if(length(unique(dat$g.fac)) == 2){
dat$g.fac <- as.factor(dat$g.fac)
}
# condition to remove regions with constant methylation / unmeth values
if (!((length(unique(na.omit(dat$meth))) == 1 &&
na.omit(dat$meth)[1] == 0) ||
(length(unique(na.omit(dat$cov - dat$meth))) == 1 &&
na.omit(dat$cov - dat$meth)[1] == 0))) {
dat$pos <- as.numeric(factor(dat$L))
if (block){
# one interior knot per 10K basepairs, with max of 10 or nloci/2
k <- min(c(ceiling((max(pos[ix]) - min(pos[ix])) / 10000) + 1,
10, ceiling(length(pos)/2)))
if (length(coeff.adj)==0){
X <- model.matrix( ~ dat$g.fac + ns(dat$L, df=k))
mm <- as.formula(paste0("Z ~ g.fac + ns(L, df=", k, ")"))
}else{
mm <- as.formula(paste0("~ g.fac + + ns(L, df=",
k, ") + ",
paste(colnames(pDat)[adjustCovariate],
collapse=" + ")))
X <- model.matrix(mm, data = dat)
mm <- as.formula(paste0("Z", paste(mm, collapse = "")))
}
}else{
if (length(coeff.adj)==0){
X <- model.matrix( ~ dat$g.fac + dat$L)
mm <- formula(Z ~ g.fac + factor(L))
}else{
mm <- as.formula(paste0("~ g.fac + factor(L) + ",
paste(colnames(pDat)[adjustCovariate],
collapse=" + ")))
X <- model.matrix(mm, data = dat)
mm <- as.formula(paste0("Z", paste(mm, collapse = "")))
}
}
Y <- as.matrix(dat$meth)
N <- as.matrix(dat$cov)
dat$MedCov <- rep(as.numeric(by(dat$cov, dat$pos, median)),
nrow(design))
# remove rows with zero coverage
whichZ <- which(dat$cov==0)
if (length(whichZ)>0){
dat <- dat[-whichZ,]
}
## small constants to bound p and phi pick this such that min value
## of z[m>0] is greater than all values of z[m==0]
c0 <- 0.05
c1 <- 0.001
## check to make sure data is complete
ixn <- N > 0
if (mean(ixn) < 1) {
## has missing entries
X <- X[ixn, , drop = FALSE]
Y <- Y[ixn]
N <- N[ixn]
## check design not enough df for regression
if (nrow(X) < ncol(X) + 1) {
message("Not enough degree of freedom to fit the ",
"model. Drop some terms in formula")
}
## design is not of full rank because of missing. Skip
if (nrow(X) < ncol(X) + 1 || any(abs(svd(X)$d) < 1e-08)){
df1 <- data.frame(stat = NA, constant = FALSE)
df2 <- data.frame(matrix(nrow = 1, ncol = length(coeff)))
# make sure colnames match nonconstant rows
if (!fact || length(unique(dat$g.fac)) <= 2){
names(df2) <- "beta"
}else{
names(df2) <- paste0("beta_", levels(as.factor(dat$g.fac))[-1])
}
df <- cbind(df1, df2)
return(df)
}
}
## Transform the methylation levels. Add a small constant to bound
## away from 0/1.
dat$Z <- asin(2 * (Y + c0)/(N + 2 * c0) - 1)
# Add a tiny amt of jitter to avoid numerically constant Z vals
# across a sample over the entire region
if (max(table(dat$Z, dat$sample)) >= length(ix) - 1) {
dat$Z <- asin(2 * (Y + c0)/(N + 2 * c0) - 1) +
runif(length(Y), -c1, c1)
}
if (length(ix) < 40) {
correlation <- correlationSmall
# check for presence of 1-2 coverage outliers that could end up
# driving the difference between the groups
grubbs.one <- grubbs.two <- 1
if (length(unique(dat$MedCov[seq_len(length(ix))])) > 1 &&
length(ix) <= 10) {
grubbs.one <- suppressWarnings(grubbs.test(
dat$MedCov[seq_len(length(ix))])$p.value)
if(length(ix) > 3){
grubbs.two <- suppressWarnings(
grubbs.test(dat$MedCov[seq_len(length(ix))],
type = 20)$p.value)
}
}
if (grubbs.one < 0.01 || grubbs.two < 0.01) {
weights <- varIdent(form = ~1)
}
}
# gls model fitting
fit <- tryCatch({
summary(gls(mm, weights = weights,
data = dat, correlation = correlation))
}, error = function(e) {
return(NA)
})
# error handling in case of false convergence (don't include
# first variance weighting, and then corr str)
if(sum(is.na(fit)) == length(fit)){
fit <- tryCatch({
summary(gls(mm, data = dat,
correlation = correlation))
}, error = function(e) {
return(NA)
})
if(sum(is.na(fit)) == length(fit)){
fit <- tryCatch({
summary(gls(mm, data = dat))
}, error = function(e) {
return(NA)
})
}
}
if (!(sum(is.na(fit)) == length(fit))) {
if (length(coeff)==1){
stat <- fit$tTable[2, 3]
beta <- fit$tTable[2, 1]
}else{
stat <- sqrt(anova(fit)$`F-value`[2])
beta <- fit$tTable[2:(2+length(coeff)-1), 1]
}
} else {
stat <- beta <- NA
}
df <- data.frame(stat = stat, constant=FALSE)
if (length(beta) > 1){
nms <- gsub("g.fac", "",
rownames(fit$tTable)[2:(2+length(coeff)-1)])
for(b in seq_len(length(beta))){
df[[paste0("beta_", nms[b])]] <- beta[b]
}
}else{
df$beta <- beta
}
df$stat = stat
return(df)
} else {
df1 <- data.frame(stat = NA, constant = TRUE)
df2 <- data.frame(matrix(nrow = 1, ncol = length(coeff)))
# make sure colnames match nonconstant rows
if (!fact || length(unique(dat$g.fac)) <= 2){
names(df2) <- "beta"
}else{
names(df2) <- paste0("beta_", levels(as.factor(dat$g.fac))[-1])
}
df <- cbind(df1, df2)
return(df)
}
}
t1 <- proc.time()
if (parallel) {
ret <- do.call("rbind", bplapply(Indexes,
function(Index) asin.gls.cov(ix = ind[Index],
design = design, coeff = coeff, fact=fact)))
} else {
ret <- do.call("rbind", lapply(Indexes,
function(Index) asin.gls.cov(ix = ind[Index],
design = design, coeff = coeff, fact=fact)))
}
# check for extreme beta values (rare; represents proportion differences >
# than 1 or large differences in sign and magnitude compared to simple
# proportion difference) and re-fit without variance weighting
# only for 2-group comparisons
if( length(coeff)==1 && fact ){
levs <- unique(design[, coeff])
indexRanges <- IRanges(unlist(lapply(Indexes, min)),
unlist(lapply(Indexes, max)))
prop.mat.dmr <- extractROWS(meth.mat/cov.mat, indexRanges)
prop.mat1.means <- DelayedMatrixStats::rowMeans2(prop.mat.dmr[,
design[, coeff] == levs[which.min(levs)]],
na.rm=TRUE)
prop.mat2.means <- DelayedMatrixStats::rowMeans2(prop.mat.dmr[,
design[, coeff] == levs[which.max(levs)]],
na.rm=TRUE)
simpleMeanDiff <- IRanges::mean(IRanges::relist(prop.mat2.means -
prop.mat1.means,
indexRanges), na.rm=TRUE)
reFit <- which(abs(ret$beta) > pi |
(abs(simpleMeanDiff - ret$beta/pi) > 1/3 &
sign(simpleMeanDiff) != sign(ret$beta)))
if(length(reFit) > 0){
ret[reFit,] <- do.call("rbind", lapply(Indexes[reFit],
function(Index) asin.gls.cov(ix = ind[Index],
design = design, coeff = coeff, fact = fact,
weights = NULL)))
}
}
df <- S4Vectors::DataFrame(ind, x = x[ind], chr = chr[ind], pos = pos[ind])
res <- as.data.frame(S4Vectors::aggregate(df, S4Vectors::List(Indexes),
chr = unlist(IRanges::heads(chr, 1L)),
START = min(pos), END = max(pos),
indexStart = min(ind), indexEnd = max(ind),
L = lengths(chr), area = abs(sum(x))))[,-1]
colnames(res)[colnames(res)=="START"] <- "start"
colnames(res)[colnames(res)=="END"] <- "end"
res <- cbind(res, ret[,grepl("beta", colnames(ret)), drop = FALSE])
res$stat <- ret$stat
# remove regions that had constant meth values
res <- res[!ret$constant, ]
t2 <- proc.time()
if (verbose) {
message(nrow(res), " regions scored (", round((t2 - t1)[3]/60, 2),
" min). ")
}
if (order && nrow(res) > 0)
res <- res[order(-abs(res$stat)), ]
return(res)
}
smoother <- function(y, x = NULL, weights = NULL, chr = chr,
maxGapSmooth = maxGapSmooth, minNumRegion = 5, verbose = TRUE,
minInSpan = minInSpan, bpSpan = bpSpan, bpSpan2 = bpSpan2,
parallel = parallel) {
locfitByCluster2 <- function(ix) {
## if y is vector change to matrix
yi <- matrix(y[ix], ncol = 1)
xi <- x[ix]
if (!is.null(weights)) {
weightsi <- matrix(weights[ix], ncol = 1)
} else {
weightsi <- NULL
}
clusteri <- clusterC[ix]
if (is.null((yi)))
stop("y (rawBeta) is missing")
if (is.null(xi))
stop("x (pos) is missing")
if (is.null(clusteri))
stop("cluster is missing")
if (is.null(weightsi))
weightsi <- matrix(1, nrow = nrow(yi), ncol = ncol(yi))
Indexes <- split(seq(along = clusteri), clusteri)
clusterL <- lengths(Indexes)
smoothed <- rep(TRUE, nrow(yi))
for (i in seq(along = Indexes)) {
Index <- Indexes[[i]]
if (clusterL[i] >= minNumRegion &&
sum(DelayedMatrixStats::rowSums2(is.na(yi[Index, ,
drop = FALSE])) == 0) >= minNumRegion) {
# for local DMRs, balance minInSpan and bpSpan
if (!is.null(bpSpan2)){
nn <- min(bpSpan2/(max(xi[Index]) - min(xi[Index]) + 1),
minInSpan/length(Index), 0.75)
}else{
nn <- minInSpan/length(Index)
}
for (j in seq_len(ncol(yi))) {
sdata <- data.frame(posi = xi[Index], yi = yi[Index, j],
weightsi = weightsi[Index, j])
fit <- locfit(yi ~ lp(posi, nn = nn, h = bpSpan),
data = sdata, weights = weightsi, family = "gaussian",
maxk = 10000)
pp <- preplot(fit, where = "data", band = "local",
newdata = data.frame(posi = xi[Index]))
yi[Index, j] <- pp$trans(pp$fit)
}
} else {
yi[Index, ] <- NA
smoothed[Index] <- FALSE
}
}
return(data.frame(fitted = as.vector(yi), smoothed = smoothed))
}
if (is.null(dim(y)))
y <- matrix(y, ncol = 1) ##need to change this to be more robust
if (!is.null(weights) && is.null(dim(weights)))
weights <- matrix(weights, ncol = 1)
ret.all <- NULL
t1 <- proc.time()
clusterC <- bumphunter::clusterMaker(factor(chr, levels=unique(chr)), x,
maxGap = maxGapSmooth)
Indexes <- split(seq(along = clusterC), clusterC)
idx <- NULL ## for R CMD check
if (parallel) {
ret <- do.call("rbind", bplapply(Indexes,
function(idx) locfitByCluster2(idx)))
} else {
ret <- do.call("rbind", lapply(Indexes,
function(idx) locfitByCluster2(idx)))
}
if (verbose) {
t2 <- proc.time()
message("Smoothed (",
round((t2 - t1)[3]/60, 2), " min). ",
appendLF = FALSE)
}
return(ret)
}
## Function to compute the coefficient estimates for regression of the
## methylation levels on the group indicator variable, at each CpG site
## - only used if not a standard two-group comparison
getEstimate <- function(mat, design, coeff, coeff.adj) {
vv <- design[, coeff, drop = FALSE]
QR <- qr(design)
Q <- qr.Q(QR)
R <- qr.R(QR)
df.residual <- ncol(mat) - QR$rank
bhat <- t(tcrossprod(backsolve(R, t(Q)), as.matrix(mat)))
vb <- chol2inv(R)[coeff,coeff]
if(is(vb, "matrix"))
vb <- mean(diag(vb))
vb <- rep(vb, nrow(mat))
# for any loci with missing coverage in at least one sample, need to
# estimate effect separately (since previous matrix mult method will
# produce NA). Split into groups based on which samples are missing
# (so only have to estimate QR matrix the number of times equal to the
# number of different patterns, rather than once for each loci with
# missing coverage)
if(sum(is.na(mat)) > 0){
NA.patterns <- unique(is.na(as.matrix(mat)))
nomissing <- which(rowSums(NA.patterns) == 0)
if (length(nomissing) > 0){
NA.patterns <- NA.patterns[-nomissing,]
}
for (l in seq_len(nrow(NA.patterns))){
idx <- which(apply(as.matrix(is.na(mat)), 1,
function(x) identical(x, NA.patterns[l,])))
vv <- design[-which(NA.patterns[l,]), coeff, drop = FALSE]
QR <- qr(design[-which(NA.patterns[l,]), , drop = FALSE])
Q <- qr.Q(QR)
R <- qr.R(QR)
bhat[idx,] <- t(tcrossprod(backsolve(R, t(Q)),
as.matrix(mat[idx,
-which(NA.patterns[l,]),
drop = FALSE])))
new.vb <- chol2inv(R)[coeff,coeff]
if(is(new.vb, "matrix"))
new.vb <- mean(diag(new.vb))
vb[idx] <- new.vb
}
}
if (!is.matrix(bhat))
bhat <- matrix(bhat, ncol = 1)
if (!is(mat, "matrix") && !is(mat, "DelayedMatrix"))
mat <- matrix(mat, nrow = 1)
if (length(coeff) == 1){ # two-group comparison or continuous
if (length(coeff.adj) > 0){
meth.diff <- exp(rowSums(bhat[,-coeff.adj, drop = FALSE])) /
(1 + exp(rowSums(bhat[,-coeff.adj, drop = FALSE]))) -
exp(rowSums(bhat[,-c(coeff, coeff.adj), drop = FALSE])) /
(1 + exp(rowSums(bhat[,-c(coeff,coeff.adj),drop=FALSE])))
}else{
meth.diff <- exp(rowSums(bhat)) / (1 + exp(rowSums(bhat))) -
exp(rowSums(bhat[,-coeff,drop=FALSE])) /
(1 + exp(rowSums(bhat[,-coeff, drop=FALSE])))
}
}else{ # multi-factor comparison - scan for max diff between any 2 groups
if(length(coeff.adj) == 0){
gdes <- t(unique(design) %*% t(bhat))
}else{
gdes <- t(unique(design[,-coeff.adj,drop=FALSE]) %*%
t(bhat[,-coeff.adj]))
}
group.means <- exp(gdes) / (1 + exp(gdes))
meth.diff <- as.numeric(rowDiffs(rowRanges(group.means)))
}
res <- as.matrix(mat) - t(tcrossprod(design, bhat))
se2 <- DelayedMatrixStats::rowSums2(res ^ 2, na.rm = TRUE) / df.residual
vb <- vb * se2
out <- list(meth.diff = meth.diff,
sd.meth.diff = sqrt(vb),
df.residual = df.residual)
return(out)
}
# *Experimental* - only used if not a standard two-group comparison
estim <- function(meth.mat = meth.mat, cov.mat = cov.mat, pos = pos,
chr = chr, design, coeff, coeff.adj) {
## For a linear regression of logit(meth.level) on the biological group
## indicator variable at each CpG site
mat <- meth.mat/cov.mat
if (sum(mat != 0, na.rm = TRUE) > 0 && sum(mat != 1, na.rm = TRUE) > 0){
eps <- min( min(mat[mat != 0], na.rm = TRUE),
1-max(mat[mat != 1], na.rm = TRUE) )
}else if (sum(mat != 0, na.rm = TRUE) > 0){
eps <- min(mat[mat != 0], na.rm = TRUE)
}else if(sum(mat != 1, na.rm = TRUE) > 0){
eps <- 1-max(mat[mat != 1, na.rm = TRUE])
}
if(eps == 1)
eps <- 0.1
mat[mat == 0] <- eps
mat[mat == 1] <- 1 - eps
logit.mat <- log(mat/(1 - mat))
lm.fit <- getEstimate(mat = logit.mat, design = design, coeff = coeff,
coeff.adj)
meth.diff <- lm.fit$meth.diff
sd.meth.diff <- lm.fit$sd.meth.diff
## Returning the final list of estimated mean methylation differences and
## their SDs at all CpG sites
out <- list(meth.diff = meth.diff, sd.meth.diff = sd.meth.diff)
return(out)
}
# pasting bumphunter's greaterOrEqual function since not exported
bumphunter.greaterOrEqual <- function(x, y) {
precision <- sqrt(.Machine$double.eps)
(x >= y) | (abs(x - y) <= precision)
}
kdkorthauer/dmrseq documentation built on Sept. 26, 2024, 9:32 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions bumphunter.greaterOrEqual estim getEstimate smoother regionScanner trimEdges refineEdges bumphunt getEstimatePooled
#' BS.chr21: Whole-genome bisulfite sequencing for chromosome 21
#' from Lister et al.
#'
#' @description This dataset represents chromosome 21
#' from the IMR90 and H1 cell lines sequenced in Lister et al.
#' Only CpG methylation are included. The two samples from
#' each cell line are two different extractions (ie. technical replicates),
#' and are pooled in the analysis in the original paper.
#' @usage data(BS.chr21)
#' @format An object of class \code{BSseq}.
#'
#' @details All coordinates are in hg18.
#' @source Obtained from
#' \url{http://neomorph.salk.edu/human_methylome/data.html} specifically,
#' the files \url{mc_h1_r1.tar.gz}, \url{mc_h1_r2.tar.gz},
#' \url{mc_imr90_r1.tar.gz}, \url{mc_imr90_r2.tar.gz}
#' A script which downloads these files and constructs the \code{BS.chr21}
#' object may be found in \file{inst/scripts/get_BS.chr21.R} - this was
#' based off of and modified from the get_BS.chr22.R script in the
#' \code{bsseq} package. The object constructed here contains a
#' different chromosome (22 instead of 21), and two additional samples
#' (h1 and imr90 instead of just imr90) to enable identification of
#' cell type-DMRs for examples.
#' @references R Lister et al. \emph{Human DNA methylomes at base
#' resolution show widespread epigenomic differences}. Nature (2009) 462,
#' 315-322.
#' @examples
#' data(BS.chr21)
#' BS.chr21
"BS.chr21"
#' annot.chr21: Annotation information for chromosome 21, hg38 genome
#'
#' @description This is the annotation information returned from
#' \code{\link{getAnnot}}, subsetted for chromosome 21 for convenience
#' in running the examples. The annotation is obtained using the
#' \code{annotatr} package.
#'
#' @usage data(annot.chr21)
#'
#' @format a \code{GRangesList} object with two elements returned
#' by \code{\link{getAnnot}}. The first
#' contains CpG category information in the first element (optional)
#' coding gene sequence information in the second element (optional).
#' At least one of these elements needs to be non-null in order for
#' any annotation to be plotted, but it is not necessary to contain
#' both.
#'
#' @source Obtained from running
#' \code{annoTrack} function and then subsetting the results to
#' only include chromosome 21. A script which executes these steps
#' and constructs the \code{annot.chr21}
#' object may be found in \file{inst/scripts/get_annot.chr21.R}
#'
#' @examples
#' data(annot.chr21)
"annot.chr21"
#' dmrs.ex: Example results of DMRs
#'
#'@description Example output from \code{dmrseq} function run on the
#' example dataset \code{BS.chr21}.
#' @usage data(dmrs.ex)
#' @format a data.frame that contains the results of the inference. The
#' data.frame contains one row for each candidate region, and
#' 10 columns, in the following order: 1. chr =
#' chromosome, 2. start =
#' start basepair position of the region, 3. end = end basepair position
#' of the region,
#' 4. indexStart = the index of the region's first CpG,
#' 5. indexEnd = the index of the region's last CpG,
#' 6. L = the number of CpGs contained in the region,
#' 7. area = the sum of the smoothed beta values
#' 8. beta = the coefficient value for the condition difference,
#' 9. stat = the test statistic for the condition difference,
#' 10. pval = the permutation p-value for the significance of the test
#' statistic, and
#' 11. qval = the q-value for the test statistic (adjustment
#' for multiple comparisons to control false discovery rate).
#' @source Obtained from running the examples in \code{\link{dmrseq}}.
#' A script which executes these steps
#' and constructs the \code{dmrs.ex}
#' object may be found in \file{inst/scripts/get_dmrs.ex.R}
#' @examples
#' data(dmrs.ex)
"dmrs.ex"
getEstimatePooled <- function(meth.mat = meth.mat, cov.mat = cov.mat, pos = pos,
chr = chr, design, coeff) {
# check whether the covariate of interest is a two group comparison if not
# (covariate is a multi-level factor or a continuous variable) then use
# single-loci estimate of methylation effect instead of pooled mean diff
if (length(unique(design[, coeff])) == 2) {
lev1 <- 1
lev2 <- 0
# pooled
est <- DelayedMatrixStats::rowSums2(meth.mat[,
unname(design[, coeff] == lev1),
drop = FALSE])/
DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[,coeff] == lev1),
drop = FALSE]) -
DelayedMatrixStats::rowSums2(meth.mat[,
unname(design[, coeff] == lev2),
drop = FALSE])/
DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[, coeff] == lev2),
drop = FALSE])
sd <- cbind(DelayedMatrixStats::rowMads(meth.mat[,
unname(design[, coeff] == lev1)]/
cov.mat[, unname(design[, coeff] == lev1)], na.rm=TRUE)^2,
DelayedMatrixStats::rowMads(meth.mat[,
unname(design[, coeff] == lev2)]/
cov.mat[, unname(design[, coeff] == lev2)], na.rm=TRUE)^2)
# when sd is zero because one of the groups had only a single sample
# with nonzero coverage, impute with the other group's sd est
sd[DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[, coeff] == lev1), drop = FALSE] > 0) == 1 &
DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[, coeff] == lev2), drop = FALSE] > 0) == 1,] <- 1
sd[DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[, coeff] == lev1), drop = FALSE] > 0) == 1,1] <-
sd[DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[, coeff] == lev1), drop = FALSE] > 0) == 1,2]
sd[DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[, coeff] == lev2), drop = FALSE] > 0) == 1,2] <-
sd[DelayedMatrixStats::rowSums2(cov.mat[,
unname(design[, coeff] == lev2), drop = FALSE] > 0) == 1,1]
sd <- 1.4826 * sqrt(DelayedMatrixStats::rowSums2(sd))
return(list(rawBeta = est, sd = sd))
} else {
# continuous or multi-level factor case
stop("Error: don't use pooled estimate when there ",
"are more than 2 groups")
}
}
bumphunt <- function(bs,
design, coeff = 2, coeff.adj = 3,
minInSpan = 30, minNumRegion = 5,
cutoff = NULL, maxGap = 1000, maxGapSmooth = 2500, smooth = FALSE,
bpSpan = 1000, verbose = TRUE, parallel = FALSE, block = FALSE,
blockSize = 5000, chrsPerChunk = 1, fact = FALSE,
adjustCovariate = NULL, ...) {
# calculate smoothing span from minInSpan
bpSpan2 <- NULL
chrs <- as.character(unique(seqnames(bs)))
if (!block){ # don't balance minInSpan and bpSpan for blocks
for (ch in chrs) {
pos <- chrSelectBSseq(bs, ch)
bpSpan2 <- c(bpSpan2, minInSpan * (max(start(pos)) -
min(start(pos)) + 1)/sum(seqnames(pos) == ch))
}
bpSpan2 <- mean(bpSpan2, na.rm = TRUE)
}
# get 75th percentile of mean coverage genomewide before subsetting
# by chromosome
covQ75 <- quantile(DelayedMatrixStats::rowMeans2(getCoverage(bs, type="Cov")), 0.75)
if (chrsPerChunk > 1){
sizeRank <- rank(-seqnames(bs)@lengths, ties.method = "first")
nChunks <- ceiling(length(unique(seqnames(bs))) / chrsPerChunk)
all.chrs <- as.character(unique(seqnames(bs)))
chrs <- vector("list", nChunks)
chunk <- 1
while ((chunk <= nChunks || length(chrs[[chunk]]) < chrsPerChunk) &&
length(all.chrs) > 0 ){
largest <- which.min(sizeRank)
smallest <- which.max(sizeRank)
if (largest != smallest && ( length(chrs[[chunk]]) == 0 ||
(chrsPerChunk - length(chrs[[chunk]])) > 1 )){
chrs[[chunk]] <- c(chrs[[chunk]], all.chrs[c(largest, smallest)])
all.chrs <- all.chrs[-c(largest,smallest)]
sizeRank <- sizeRank[-c(largest,smallest)]
}else{
chrs[[chunk]] <- c(chrs[[chunk]], all.chrs[largest])
all.chrs <- all.chrs[-largest]
sizeRank <- sizeRank[-largest]
}
# put in original seqlevel order
chrs[[chunk]] <- as.character(seqnames(chrSelectBSseq(bs,
chrs[[chunk]]))@values)
if(length(chrs[[chunk]]) == chrsPerChunk){
chunk <- min(nChunks, chunk + 1)
}
}
}
tab <- NULL
for (chromosome in chrs) {
meth.mat <- getCoverage(chrSelectBSseq(bs, chromosome),
type = "M")
cov.mat <- getCoverage(chrSelectBSseq(bs, chromosome),
type = "Cov")
pos <- start(chrSelectBSseq(bs, chromosome))
chr <- as.character(seqnames(chrSelectBSseq(bs, chromosome)))
if (verbose)
message("...Chromosome ", paste(chromosome, collapse = ", "),
": ", appendLF = FALSE)
# skip chromosomes that have fewer than minNumRegion loci
if (length(pos) < minNumRegion){
message("No candidates found.")
next
}
cov.means <- DelayedMatrixStats::rowMeans2(cov.mat)
if (length(unique(design[, coeff])) == 2 &&
length(coeff) == 1 &&
all.equal(sort(unique(as.vector(design[,coeff])))[seq_len(2)],
c(0,1))) {
tmp <- getEstimatePooled(meth.mat = meth.mat,
cov.mat = cov.mat, pos = pos,
chr = chr, design, coeff)
rawBeta <- tmp$rawBeta
sd.raw <- tmp$sd
} else {
tmp <- estim(meth.mat = meth.mat, cov.mat = cov.mat,
pos = pos, chr = chr,
design = design, coeff = coeff, coeff.adj = coeff.adj)
rawBeta <- tmp$meth.diff
sd.raw <- tmp$sd.meth.diff
}
sd.raw[sd.raw < 1e-05] <- 1e-05
# truncate coverage at 75th percentile
# minimum sd proportional to median coverage
weights <- pmin(cov.means, covQ75) /
pmax(sd.raw, 1/pmax(cov.means, 5))
if (smooth) {
beta <- vector("list", 2)
beta[[1]] <- beta[[2]] <- rep(NA, length(pos))
beta.tmp <- smoother(y = rawBeta,
x = pos, chr = chr,
maxGapSmooth = maxGapSmooth,
weights = weights,
minNumRegion = minNumRegion,
minInSpan = minInSpan,
bpSpan = bpSpan, bpSpan2 = bpSpan2,
verbose = verbose,
parallel = parallel)
beta[[1]] <- beta.tmp[[1]]
beta[[2]] <- beta.tmp[[2]]
names(beta) <- names(beta.tmp)
Index <- which(beta$smoothed)
beta <- beta$fitted
} else {
beta <- rawBeta
Index <- seq(along = beta)
}
if (length(unique(as.vector(design[, coeff]))) == 2 &&
length(coeff) == 1 &&
all.equal(sort(unique(as.vector(design[,coeff])))[seq_len(2)],c(0,1))) {
ngroups <- length(unique(design[,coeff]))
rawBeta <- rawBeta/(sd.raw * ngroups / sqrt(nrow(design)))
}else{
rawBeta <- rawBeta/sd.raw
}
if(length(Index) > 0){
beta[-Index] <- rawBeta[-Index]
}else{ # if no loci were smoothed, use raw ests instead of NA
beta <- rawBeta
}
tab <- rbind(tab,
regionScanner(meth.mat = meth.mat, cov.mat = cov.mat, pos = pos,
chr = chr, x = beta, y = rawBeta, maxGap = maxGap, cutoff = cutoff,
minNumRegion = minNumRegion, design = design, coeff = coeff,
coeff.adj = coeff.adj,
verbose = verbose, parallel = parallel,
pDat=pData(bs), block = block, blockSize = blockSize, fact = fact,
adjustCovariate = adjustCovariate))
}
if (length(tab) == 0) {
if (verbose)
message("No regions found.")
return(NULL)
}
# convert index from within chromosome to overall
if (length(chrs) > 0){
if (is.list(chrs)){ # make length relative to single chrs
for(ch in chrs){
if(length(ch) > 1){
sublengths <- cumsum(table(seqnames(chrSelectBSseq(bs, ch))))
for (j in seq_len(length(ch)-1)) {
tab$indexStart[tab$chr %in% ch[j+1]] <- tab$indexStart[tab$chr %in% ch[j+1]] -
sublengths[names(sublengths) == ch[j]]
tab$indexEnd[tab$chr %in% ch[j+1]] <- tab$indexEnd[tab$chr %in% ch[j+1]] -
sublengths[names(sublengths) == ch[j]]
}
}
}
}
chrs <- unique(as.character(seqnames(bs)))
chrlengths <- table(seqnames(bs))
chrlengths <- chrlengths[chrs]
chrlengths <- cumsum(chrlengths)
# Add a check to make sure chr order lines up with
# cumulative sum table
if (identical(chrs, names(chrlengths))){
for (j in seq_len(length(chrs)-1)) {
ch <- chrs[[j+1]]
tab$indexStart[tab$chr %in% ch] <- tab$indexStart[tab$chr %in% ch] +
chrlengths[names(chrlengths) == chrs[j]]
tab$indexEnd[tab$chr %in% ch] <- tab$indexEnd[tab$chr %in% ch] +
chrlengths[names(chrlengths) == chrs[j]]
}
}else{
stop("Chromosome order could not be reliably determined. ",
"Please use the 'sort' function before running dmrseq")
}
}
return(tab)
}
refineEdges <- function(y, candidates = NULL,
cutoff = qt(0.975, nrow(design) - 2), verbose = FALSE,
minNumRegion, design) {
stopifnot(length(cutoff) <= 2)
stopifnot(is.list(candidates) && length(candidates) == 2)
if (verbose)
message("refineEdges: refining")
direction <- as.integer(bumphunter.greaterOrEqual(y, cutoff))
direction[y <= -cutoff] <- -1L
refineOne <- function(x, sig) {
idx <- which(direction[x] == sig)
if (length(idx) > 0) {
if (length(seq(min(idx),max(idx))) >= minNumRegion) {
x[seq(min(idx),max(idx))]
} else {
x
}
} else {
x
}
}
refineLong <- function(candidates, direction, minNumRegion, sig){
if (length(candidates) > 0) {
which.long <- which(lengths(candidates) > minNumRegion)
if (length(which.long) > 1) {
candidates[which.long] <- lapply(candidates[which.long],
FUN=refineOne, sig=sig)
candidates[vapply(candidates, is.null, FUN.VALUE=logical(1))] <- NULL
} else if (length(which.long) == 1) {
candidates[[which.long]] <- unlist(lapply(candidates[which.long],
FUN=refineOne, sig=sig))
}
}
return(candidates)
}
trimmed <- vector("list", 2)
trimmed[[1]] <- refineLong(candidates[[1]], direction,
minNumRegion, sig = 1)
trimmed[[2]] <- refineLong(candidates[[2]], direction,
minNumRegion, sig = -1)
return(trimmed)
}
trimEdges <- function(x, candidates = NULL, verbose = FALSE, minNumRegion) {
stopifnot(is.list(candidates) && length(candidates) == 2)
if (verbose)
message("trimEdges: trimming")
trimOne <- function(w, x) {
mid <- which.max(x[w])
new.start <- 1
new.end <- length(w)
if (x[w[mid]]/min(x[w]) > 4/3) {
if (sum(!is.na(x[w[1]:w[mid]])) > 4) {
fit1 <- lm(x[w[seq_len(mid)]] ~ w[seq_len(mid)])
if (length(summary(fit1)) > 0) {
if (nrow(summary(fit1)$coef) == 2) {
if (summary(fit1)$coef[2, 1] > 0 &&
summary(fit1)$coef[2, 4] < 0.01) {
new.cut <- (0.5 * (x[w[mid]] - min(x[w])) +
min(x[w]) + 0.75 * mean(x[w]))/2
new.start <- min(max(1, round(mid - 0.125 * length(w))),
max(1, mid - 2),
(seq_len(mid))[min(which(x[w[seq_len(mid)]] >=
new.cut))])
}
}
}
}
if (sum(!is.na(x[w[mid]:w[length(w)]])) > 4) {
fit2 <- lm(x[w[seq(mid,length(w))]] ~ w[seq(mid,length(w))])
if (length(fit2) > 0) {
if (nrow(summary(fit2)$coef) == 2) {
if (summary(fit2)$coef[2, 1] < 0 &&
summary(fit2)$coef[2, 4] < 0.01) {
new.cut <- (0.5 * (x[w[mid]] - min(x[w])) +
min(x[w]) + 0.75 * mean(x[w]))/2
new.end <- max(min(round(mid + 0.125 * length(w)),
length(w)), min(mid + 2, length(w)),
(seq(mid,length(w)))[max(which(x[w[seq(mid,
length(w))]] >= new.cut))])
}
}
}
}
}
if (length(seq(new.start,new.end)) >= minNumRegion) {
return(w[seq(new.start,new.end)])
} else {
return(w)
}
}
trimLong <- function(x, candidates, minNumRegion, sig){
if (length(candidates) > 0) {
x <- x * sig
which.long <- which(lengths(candidates) > minNumRegion)
if (length(which.long) > 1) {
candidates[which.long] <- lapply(candidates[which.long],
FUN=trimOne, x=x)
candidates[vapply(candidates, is.null, FUN.VALUE=logical(1))] <- NULL
} else if (length(which.long) == 1) {
candidates[[which.long]] <- unlist(lapply(candidates[which.long],
FUN=trimOne, x=x))
}
}
return(candidates)
}
trimmed <- vector("list", 2)
trimmed[[1]] <- trimLong(x, candidates[[1]], minNumRegion, sig = 1)
trimmed[[2]] <- trimLong(x, candidates[[2]], minNumRegion, sig = -1)
return(trimmed)
}
regionScanner <- function(meth.mat = meth.mat, cov.mat = cov.mat, pos = pos,
chr = chr, x, y = x, ind = seq(along = x), order = TRUE, minNumRegion = 5,
maxGap = 300, cutoff = quantile(abs(x), 0.99), assumeSorted = FALSE,
verbose = verbose, design = design, coeff = coeff, coeff.adj = coeff.adj,
parallel = parallel, pDat, block, blockSize, fact = fact,
adjustCovariate = NULL) {
if (any(is.na(x[ind]))) {
message(sum(is.na(x[ind]))," CpG(s) excluded due to zero coverage. ",
appendLF = FALSE)
ind <- intersect(which(!is.na(x)), ind)
}
# remove any empty factor levels
for (f in adjustCovariate){
if (is.factor(pDat[,f]))
pDat[,f] <- droplevels(pDat[,f])
}
cluster <- bumphunter::clusterMaker(factor(chr, levels=unique(chr)),
pos, maxGap = maxGap,
assumeSorted = assumeSorted)
Indexes <- bumphunter::getSegments(x = x[ind], f = cluster[ind],
cutoff = cutoff,
assumeSorted = TRUE,
verbose = FALSE)
# only keep up and down indices
Indexes <- Indexes[seq_len(2)]
if (sum(lengths(Indexes)) == 0) {
message("No candidates found. ")
return(NULL)
}
# refine edges -> start = first (stop = last) position with a raw difference
# that meets the threshold
Indexes <- refineEdges(y = y[ind], candidates = Indexes, cutoff = cutoff,
verbose = FALSE, minNumRegion = minNumRegion, design = design)
if (sum(lengths(Indexes)) == 0) {
message("No candidates found. ")
return(NULL)
}
# refine edges II -> for larger regions, when effect size changes over the
# region, remove portions at the beginning and end where effect size is
# drastically different than overall effect size
Indexes <- trimEdges(x = x[ind], candidates = Indexes, verbose = FALSE,
minNumRegion = minNumRegion)
if (sum(lengths(Indexes)) == 0) {
message("No candidates found. ")
return(NULL)
}
for (i in seq_len(2)) {
# get number of loci in region
lns <- lengths(Indexes[[i]])
Indexes[[i]] <- Indexes[[i]][lns >= minNumRegion]
}
if (sum(lengths(Indexes)) == 0) {
message("No candidates found. ")
return(NULL)
}
if(block){
# merge small candidate regions that are the same direction &
# are less than 1kb apart
# and on SAME chr
df <- S4Vectors::DataFrame(ind, chr = chr[ind], pos = pos[ind])
for(j in seq_along(Indexes)){
if (length(Indexes[[j]]) > 0){
reg <- as.data.frame(S4Vectors::aggregate(df,
S4Vectors::List(Indexes[[j]]),
chr = unlist(IRanges::heads(chr, 1L)),
Start = min(pos),
End = max(pos)))
reg <- GRanges(seqnames=reg$chr,
IRanges(start=reg$Start, end=reg$End))
# find 1kb flanking regions with no covered CpGs
flk <- c(IRanges::flank(reg, width=1000, start=TRUE),
IRanges::flank(reg, width=1000, start=FALSE))
start(flk) <- pmax(1, start(flk)) # don't let neg positions
overlap <- unique(IRanges::findOverlaps(flk, GRanges(seqnames=chr,
IRanges::IRanges(start=pos, end=pos)))@from)
if (length(overlap) > 0)
flk <- flk[-overlap]
# add back to regions and remake indice
reg <- as.matrix(as.data.frame(IRanges::reduce(c(flk, reg)))[,1:3])
idx <- apply(reg, 1, function(x) which(pos[ind] %in% x[2]:x[3] &
chr[ind] %in% x[1]))
if (is(idx, "list")){
Indexes[[j]] <- idx
}else{
Indexes[[j]] <- list(as.vector(idx))
}
}
}
# subset on those with at least blockSize bps
Indexes <- c(Indexes[[1]], Indexes[[2]])
nbp <- as.data.frame(S4Vectors::aggregate(df, S4Vectors::List(Indexes),
nbp = max(pos) - min(pos) + 1 ))$nbp
Indexes <- Indexes[nbp >= blockSize]
}else{
Indexes <- c(Indexes[[1]], Indexes[[2]])
maxLength <- max(lengths(Indexes))
if (maxLength > 1000) {
message("Note: Large candidate regions with more than 1000 ",
"CpGs detected.",
" If you'd like to detect ",
"large-scale blocks, set block=TRUE.",
" If you'd like to detect local DMRs, it is recommended to ",
"decrease the values of bpSpan, minInSpan, and/or ",
"maxGapSmooth increase computational efficiency.")
}
}
# only keep regions with replicates in each group for at least two CpGs
# for factor comparisons
replicateStatus <- function(candidates, design, coeff, fact){
levs <- unique(design[, coeff, drop = FALSE])
if(!is(levs, "matrix"))
levs <- as.matrix(levs)
if (fact){
indexRanges <- IRanges(unlist(lapply(candidates, min)),
unlist(lapply(candidates, max)))
cov.mat.cand <- extractROWS(cov.mat, indexRanges)
prev.mat <- rep(TRUE, nrow(cov.mat.cand))
for (l in seq_len(nrow(levs))){
cov.matl <- DelayedMatrixStats::rowSums2(
cov.mat.cand[,apply(design[, coeff, drop = FALSE], 1,
function(x) identical(unname(x),
unname(levs[l,]))), drop = FALSE] > 0) > 0 &
prev.mat
prev.mat <- cov.matl
}
rel <- unlist(lapply(IRanges::relist(cov.matl,indexRanges), sum))
return(which(rel > 1))
}else{
return(seq_along(candidates))
}
}
Indexes <- Indexes[replicateStatus(Indexes, design, coeff, fact)]
if (length(Indexes) == 0) {
message("No candidates found. ")
return(NULL)
}
asin.gls.cov <- function(ix, design, coeff,
correlation = corAR1(form = ~1 |sample),
correlationSmall = corCAR1(form = ~L | sample),
weights = varPower(form = ~1/MedCov, fixed = 0.5),
fact) {
if (is.factor(pDat[,colnames(design)[coeff[1]]])) # drop unused levels of test
pDat[,colnames(design)[coeff[1]]] <- droplevels(pDat[,colnames(design)[coeff[1]]])
dat <- data.frame(g.fac = rep(pDat[,colnames(design)[coeff[1]]],
each = length(ix)),
sample = factor(rep(seq_len(nrow(design)),
each=length(ix))),
meth = as.vector(meth.mat[ix, ]),
cov = as.vector(cov.mat[ix, ]),
L = as.vector(rep(pos[ix], nrow(design))))
if(length(coeff.adj) > 0){
for (k in adjustCovariate){
dat[,colnames(pDat)[k]] <-
rep(pDat[,colnames(pDat)[k]], each=length(ix))
}
}
if(length(unique(dat$g.fac)) == 2){
dat$g.fac <- as.factor(dat$g.fac)
}
# condition to remove regions with constant methylation / unmeth values
if (!((length(unique(na.omit(dat$meth))) == 1 &&
na.omit(dat$meth)[1] == 0) ||
(length(unique(na.omit(dat$cov - dat$meth))) == 1 &&
na.omit(dat$cov - dat$meth)[1] == 0))) {
dat$pos <- as.numeric(factor(dat$L))
if (block){
# one interior knot per 10K basepairs, with max of 10 or nloci/2
k <- min(c(ceiling((max(pos[ix]) - min(pos[ix])) / 10000) + 1,
10, ceiling(length(pos)/2)))
if (length(coeff.adj)==0){
X <- model.matrix( ~ dat$g.fac + ns(dat$L, df=k))
mm <- as.formula(paste0("Z ~ g.fac + ns(L, df=", k, ")"))
}else{
mm <- as.formula(paste0("~ g.fac + + ns(L, df=",
k, ") + ",
paste(colnames(pDat)[adjustCovariate],
collapse=" + ")))
X <- model.matrix(mm, data = dat)
mm <- as.formula(paste0("Z", paste(mm, collapse = "")))
}
}else{
if (length(coeff.adj)==0){
X <- model.matrix( ~ dat$g.fac + dat$L)
mm <- formula(Z ~ g.fac + factor(L))
}else{
mm <- as.formula(paste0("~ g.fac + factor(L) + ",
paste(colnames(pDat)[adjustCovariate],
collapse=" + ")))
X <- model.matrix(mm, data = dat)
mm <- as.formula(paste0("Z", paste(mm, collapse = "")))
}
}
Y <- as.matrix(dat$meth)
N <- as.matrix(dat$cov)
dat$MedCov <- rep(as.numeric(by(dat$cov, dat$pos, median)),
nrow(design))
# remove rows with zero coverage
whichZ <- which(dat$cov==0)
if (length(whichZ)>0){
dat <- dat[-whichZ,]
}
## small constants to bound p and phi pick this such that min value
## of z[m>0] is greater than all values of z[m==0]
c0 <- 0.05
c1 <- 0.001
## check to make sure data is complete
ixn <- N > 0
if (mean(ixn) < 1) {
## has missing entries
X <- X[ixn, , drop = FALSE]
Y <- Y[ixn]
N <- N[ixn]
## check design not enough df for regression
if (nrow(X) < ncol(X) + 1) {
message("Not enough degree of freedom to fit the ",
"model. Drop some terms in formula")
}
## design is not of full rank because of missing. Skip
if (nrow(X) < ncol(X) + 1 || any(abs(svd(X)$d) < 1e-08)){
df1 <- data.frame(stat = NA, constant = FALSE)
df2 <- data.frame(matrix(nrow = 1, ncol = length(coeff)))
# make sure colnames match nonconstant rows
if (!fact || length(unique(dat$g.fac)) <= 2){
names(df2) <- "beta"
}else{
names(df2) <- paste0("beta_", levels(as.factor(dat$g.fac))[-1])
}
df <- cbind(df1, df2)
return(df)
}
}
## Transform the methylation levels. Add a small constant to bound
## away from 0/1.
dat$Z <- asin(2 * (Y + c0)/(N + 2 * c0) - 1)
# Add a tiny amt of jitter to avoid numerically constant Z vals
# across a sample over the entire region
if (max(table(dat$Z, dat$sample)) >= length(ix) - 1) {
dat$Z <- asin(2 * (Y + c0)/(N + 2 * c0) - 1) +
runif(length(Y), -c1, c1)
}
if (length(ix) < 40) {
correlation <- correlationSmall
# check for presence of 1-2 coverage outliers that could end up
# driving the difference between the groups
grubbs.one <- grubbs.two <- 1
if (length(unique(dat$MedCov[seq_len(length(ix))])) > 1 &&
length(ix) <= 10) {
grubbs.one <- suppressWarnings(grubbs.test(
dat$MedCov[seq_len(length(ix))])$p.value)
if(length(ix) > 3){
grubbs.two <- suppressWarnings(
grubbs.test(dat$MedCov[seq_len(length(ix))],
type = 20)$p.value)
}
}
if (grubbs.one < 0.01 || grubbs.two < 0.01) {
weights <- varIdent(form = ~1)
}
}
# gls model fitting
fit <- tryCatch({
summary(gls(mm, weights = weights,
data = dat, correlation = correlation))
}, error = function(e) {
return(NA)
})
# error handling in case of false convergence (don't include
# first variance weighting, and then corr str)
if(sum(is.na(fit)) == length(fit)){
fit <- tryCatch({
summary(gls(mm, data = dat,
correlation = correlation))
}, error = function(e) {
return(NA)
})
if(sum(is.na(fit)) == length(fit)){
fit <- tryCatch({
summary(gls(mm, data = dat))
}, error = function(e) {
return(NA)
})
}
}
if (!(sum(is.na(fit)) == length(fit))) {
if (length(coeff)==1){
stat <- fit$tTable[2, 3]
beta <- fit$tTable[2, 1]
}else{
stat <- sqrt(anova(fit)$`F-value`[2])
beta <- fit$tTable[2:(2+length(coeff)-1), 1]
}
} else {
stat <- beta <- NA
}
df <- data.frame(stat = stat, constant=FALSE)
if (length(beta) > 1){
nms <- gsub("g.fac", "",
rownames(fit$tTable)[2:(2+length(coeff)-1)])
for(b in seq_len(length(beta))){
df[[paste0("beta_", nms[b])]] <- beta[b]
}
}else{
df$beta <- beta
}
df$stat = stat
return(df)
} else {
df1 <- data.frame(stat = NA, constant = TRUE)
df2 <- data.frame(matrix(nrow = 1, ncol = length(coeff)))
# make sure colnames match nonconstant rows
if (!fact || length(unique(dat$g.fac)) <= 2){
names(df2) <- "beta"
}else{
names(df2) <- paste0("beta_", levels(as.factor(dat$g.fac))[-1])
}
df <- cbind(df1, df2)
return(df)
}
}
t1 <- proc.time()
if (parallel) {
ret <- do.call("rbind", bplapply(Indexes,
function(Index) asin.gls.cov(ix = ind[Index],
design = design, coeff = coeff, fact=fact)))
} else {
ret <- do.call("rbind", lapply(Indexes,
function(Index) asin.gls.cov(ix = ind[Index],
design = design, coeff = coeff, fact=fact)))
}
# check for extreme beta values (rare; represents proportion differences >
# than 1 or large differences in sign and magnitude compared to simple
# proportion difference) and re-fit without variance weighting
# only for 2-group comparisons
if( length(coeff)==1 && fact ){
levs <- unique(design[, coeff])
indexRanges <- IRanges(unlist(lapply(Indexes, min)),
unlist(lapply(Indexes, max)))
prop.mat.dmr <- extractROWS(meth.mat/cov.mat, indexRanges)
prop.mat1.means <- DelayedMatrixStats::rowMeans2(prop.mat.dmr[,
design[, coeff] == levs[which.min(levs)]],
na.rm=TRUE)
prop.mat2.means <- DelayedMatrixStats::rowMeans2(prop.mat.dmr[,
design[, coeff] == levs[which.max(levs)]],
na.rm=TRUE)
simpleMeanDiff <- IRanges::mean(IRanges::relist(prop.mat2.means -
prop.mat1.means,
indexRanges), na.rm=TRUE)
reFit <- which(abs(ret$beta) > pi |
(abs(simpleMeanDiff - ret$beta/pi) > 1/3 &
sign(simpleMeanDiff) != sign(ret$beta)))
if(length(reFit) > 0){
ret[reFit,] <- do.call("rbind", lapply(Indexes[reFit],
function(Index) asin.gls.cov(ix = ind[Index],
design = design, coeff = coeff, fact = fact,
weights = NULL)))
}
}
df <- S4Vectors::DataFrame(ind, x = x[ind], chr = chr[ind], pos = pos[ind])
res <- as.data.frame(S4Vectors::aggregate(df, S4Vectors::List(Indexes),
chr = unlist(IRanges::heads(chr, 1L)),
START = min(pos), END = max(pos),
indexStart = min(ind), indexEnd = max(ind),
L = lengths(chr), area = abs(sum(x))))[,-1]
colnames(res)[colnames(res)=="START"] <- "start"
colnames(res)[colnames(res)=="END"] <- "end"
res <- cbind(res, ret[,grepl("beta", colnames(ret)), drop = FALSE])
res$stat <- ret$stat
# remove regions that had constant meth values
res <- res[!ret$constant, ]
t2 <- proc.time()
if (verbose) {
message(nrow(res), " regions scored (", round((t2 - t1)[3]/60, 2),
" min). ")
}
if (order && nrow(res) > 0)
res <- res[order(-abs(res$stat)), ]
return(res)
}
smoother <- function(y, x = NULL, weights = NULL, chr = chr,
maxGapSmooth = maxGapSmooth, minNumRegion = 5, verbose = TRUE,
minInSpan = minInSpan, bpSpan = bpSpan, bpSpan2 = bpSpan2,
parallel = parallel) {
locfitByCluster2 <- function(ix) {
## if y is vector change to matrix
yi <- matrix(y[ix], ncol = 1)
xi <- x[ix]
if (!is.null(weights)) {
weightsi <- matrix(weights[ix], ncol = 1)
} else {
weightsi <- NULL
}
clusteri <- clusterC[ix]
if (is.null((yi)))
stop("y (rawBeta) is missing")
if (is.null(xi))
stop("x (pos) is missing")
if (is.null(clusteri))
stop("cluster is missing")
if (is.null(weightsi))
weightsi <- matrix(1, nrow = nrow(yi), ncol = ncol(yi))
Indexes <- split(seq(along = clusteri), clusteri)
clusterL <- lengths(Indexes)
smoothed <- rep(TRUE, nrow(yi))
for (i in seq(along = Indexes)) {
Index <- Indexes[[i]]
if (clusterL[i] >= minNumRegion &&
sum(DelayedMatrixStats::rowSums2(is.na(yi[Index, ,
drop = FALSE])) == 0) >= minNumRegion) {
# for local DMRs, balance minInSpan and bpSpan
if (!is.null(bpSpan2)){
nn <- min(bpSpan2/(max(xi[Index]) - min(xi[Index]) + 1),
minInSpan/length(Index), 0.75)
}else{
nn <- minInSpan/length(Index)
}
for (j in seq_len(ncol(yi))) {
sdata <- data.frame(posi = xi[Index], yi = yi[Index, j],
weightsi = weightsi[Index, j])
fit <- locfit(yi ~ lp(posi, nn = nn, h = bpSpan),
data = sdata, weights = weightsi, family = "gaussian",
maxk = 10000)
pp <- preplot(fit, where = "data", band = "local",
newdata = data.frame(posi = xi[Index]))
yi[Index, j] <- pp$trans(pp$fit)
}
} else {
yi[Index, ] <- NA
smoothed[Index] <- FALSE
}
}
return(data.frame(fitted = as.vector(yi), smoothed = smoothed))
}
if (is.null(dim(y)))
y <- matrix(y, ncol = 1) ##need to change this to be more robust
if (!is.null(weights) && is.null(dim(weights)))
weights <- matrix(weights, ncol = 1)
ret.all <- NULL
t1 <- proc.time()
clusterC <- bumphunter::clusterMaker(factor(chr, levels=unique(chr)), x,
maxGap = maxGapSmooth)
Indexes <- split(seq(along = clusterC), clusterC)
idx <- NULL ## for R CMD check
if (parallel) {
ret <- do.call("rbind", bplapply(Indexes,
function(idx) locfitByCluster2(idx)))
} else {
ret <- do.call("rbind", lapply(Indexes,
function(idx) locfitByCluster2(idx)))
}
if (verbose) {
t2 <- proc.time()
message("Smoothed (",
round((t2 - t1)[3]/60, 2), " min). ",
appendLF = FALSE)
}
return(ret)
}
## Function to compute the coefficient estimates for regression of the
## methylation levels on the group indicator variable, at each CpG site
## - only used if not a standard two-group comparison
getEstimate <- function(mat, design, coeff, coeff.adj) {
vv <- design[, coeff, drop = FALSE]
QR <- qr(design)
Q <- qr.Q(QR)
R <- qr.R(QR)
df.residual <- ncol(mat) - QR$rank
bhat <- t(tcrossprod(backsolve(R, t(Q)), as.matrix(mat)))
vb <- chol2inv(R)[coeff,coeff]
if(is(vb, "matrix"))
vb <- mean(diag(vb))
vb <- rep(vb, nrow(mat))
# for any loci with missing coverage in at least one sample, need to
# estimate effect separately (since previous matrix mult method will
# produce NA). Split into groups based on which samples are missing
# (so only have to estimate QR matrix the number of times equal to the
# number of different patterns, rather than once for each loci with
# missing coverage)
if(sum(is.na(mat)) > 0){
NA.patterns <- unique(is.na(as.matrix(mat)))
nomissing <- which(rowSums(NA.patterns) == 0)
if (length(nomissing) > 0){
NA.patterns <- NA.patterns[-nomissing,]
}
for (l in seq_len(nrow(NA.patterns))){
idx <- which(apply(as.matrix(is.na(mat)), 1,
function(x) identical(x, NA.patterns[l,])))
vv <- design[-which(NA.patterns[l,]), coeff, drop = FALSE]
QR <- qr(design[-which(NA.patterns[l,]), , drop = FALSE])
Q <- qr.Q(QR)
R <- qr.R(QR)
bhat[idx,] <- t(tcrossprod(backsolve(R, t(Q)),
as.matrix(mat[idx,
-which(NA.patterns[l,]),
drop = FALSE])))
new.vb <- chol2inv(R)[coeff,coeff]
if(is(new.vb, "matrix"))
new.vb <- mean(diag(new.vb))
vb[idx] <- new.vb
}
}
if (!is.matrix(bhat))
bhat <- matrix(bhat, ncol = 1)
if (!is(mat, "matrix") && !is(mat, "DelayedMatrix"))
mat <- matrix(mat, nrow = 1)
if (length(coeff) == 1){ # two-group comparison or continuous
if (length(coeff.adj) > 0){
meth.diff <- exp(rowSums(bhat[,-coeff.adj, drop = FALSE])) /
(1 + exp(rowSums(bhat[,-coeff.adj, drop = FALSE]))) -
exp(rowSums(bhat[,-c(coeff, coeff.adj), drop = FALSE])) /
(1 + exp(rowSums(bhat[,-c(coeff,coeff.adj),drop=FALSE])))
}else{
meth.diff <- exp(rowSums(bhat)) / (1 + exp(rowSums(bhat))) -
exp(rowSums(bhat[,-coeff,drop=FALSE])) /
(1 + exp(rowSums(bhat[,-coeff, drop=FALSE])))
}
}else{ # multi-factor comparison - scan for max diff between any 2 groups
if(length(coeff.adj) == 0){
gdes <- t(unique(design) %*% t(bhat))
}else{
gdes <- t(unique(design[,-coeff.adj,drop=FALSE]) %*%
t(bhat[,-coeff.adj]))
}
group.means <- exp(gdes) / (1 + exp(gdes))
meth.diff <- as.numeric(rowDiffs(rowRanges(group.means)))
}
res <- as.matrix(mat) - t(tcrossprod(design, bhat))
se2 <- DelayedMatrixStats::rowSums2(res ^ 2, na.rm = TRUE) / df.residual
vb <- vb * se2
out <- list(meth.diff = meth.diff,
sd.meth.diff = sqrt(vb),
df.residual = df.residual)
return(out)
}
# *Experimental* - only used if not a standard two-group comparison
estim <- function(meth.mat = meth.mat, cov.mat = cov.mat, pos = pos,
chr = chr, design, coeff, coeff.adj) {
## For a linear regression of logit(meth.level) on the biological group
## indicator variable at each CpG site
mat <- meth.mat/cov.mat
if (sum(mat != 0, na.rm = TRUE) > 0 && sum(mat != 1, na.rm = TRUE) > 0){
eps <- min( min(mat[mat != 0], na.rm = TRUE),
1-max(mat[mat != 1], na.rm = TRUE) )
}else if (sum(mat != 0, na.rm = TRUE) > 0){
eps <- min(mat[mat != 0], na.rm = TRUE)
}else if(sum(mat != 1, na.rm = TRUE) > 0){
eps <- 1-max(mat[mat != 1, na.rm = TRUE])
}
if(eps == 1)
eps <- 0.1
mat[mat == 0] <- eps
mat[mat == 1] <- 1 - eps
logit.mat <- log(mat/(1 - mat))
lm.fit <- getEstimate(mat = logit.mat, design = design, coeff = coeff,
coeff.adj)
meth.diff <- lm.fit$meth.diff
sd.meth.diff <- lm.fit$sd.meth.diff
## Returning the final list of estimated mean methylation differences and
## their SDs at all CpG sites
out <- list(meth.diff = meth.diff, sd.meth.diff = sd.meth.diff)
return(out)
}
# pasting bumphunter's greaterOrEqual function since not exported
bumphunter.greaterOrEqual <- function(x, y) {
precision <- sqrt(.Machine$double.eps)
(x >= y) | (abs(x - y) <= precision)
}
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