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aux.R
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# BaGFoot :
#
# Programmed by Songjoon Baek 2017
#
# Last Update : 4.18.2018
# library("hash"); # 2.2.6
# library("parallel");
# library("digest"); #0.6.9
# library("data.table"); # 1.10.0
# library("Cairo"); # 1.5-9
# library("aplpack"); # 1.3.0
# library('Rsamtools'); # 1.22.0
# library('BSgenome.Hsapiens.UCSC.hg19'); # 1.4.0
# library('BSgenome.Mmusculus.UCSC.mm9'); # 1.4.0
# library(doMC);
# library(foreach);
################################### bfoot.configuration #########################################
# MCCORES=getOption("mc.cores", 2L); # Number of CPU cores for parallel computation
MCCORES <- 8
MCCORES_MOTIF <- 8
# How many motifs are calculated at the same time
# MCCORES_MOTIF = 1; # How many motifs are calculated at the same time
FAST_AGGREGATION_MC_CORES <- 8
# Number of CPU cores for less-memory dependent calculation
MAX_MOTIF_SITES <- 10000000
# Maximum number of FIMO sites for each motif
BFOOT_DEBUG <- F
# dyn.load("/opt/R/bagfootr/bagfoot/src/bagfoot.so");
# doMC::registerDoMC(cores=MCCORES);
################################### bfoot#########################################################
bagfootVersion <<- "0.9.7.02"
cat(sprintf("BaGFoot: v.%s\n", bagfootVersion))
printf <- function(...) {
cat(sprintf(...))
}
extractBaseName <- function(filename) {
bname <- basename(filename)
ext <- regexpr("\\.", bname)[[1]]
bnameNoExt <- substring(bname, 1, ext - 1)
bnameNoExt
}
countCutcountOnSites <- function(cutcount, sites) {
if (sites == "" || is.na(sites) || is.null(sites)) {
return(sum(sapply(cutcount, function(x) {
sum(x$value)
})))
}
if (is.character(sites)) {
hs <- readcsv(sites)
} else {
hs <- sites
}
chroms <- levels(hs$chr)
sum_for_chr <- function(chr) {
# print(chr);
tdchr <- cutcount[[chr]]
hschr <- hs[hs$chr == chr, ]
cc <- vector("numeric", length = max(hs$ed + 1))
cc[tdchr$st] <- tdchr$val
cc[tdchr$ed + 1] <- cc[tdchr$ed + 1] - tdchr$val
cc <- cumsum(cc)
s <- sum(sapply(seq_along(hschr$st), function(ii) sum(cc[hschr$st[ii]:hschr$ed[ii]])))
rm(cc)
s
}
# browser();
sum(unlist(parallel::mclapply(chroms, sum_for_chr, mc.cores = MCCORES)))
}
readCutCountOnSites <- function(cutcount_file = "", site_file = "") {
cutcount <- NULL
sites <- NULL
reduced_bgr_filename <- file.path(dirname(cutcount_file), sprintf("%s.bgr", paste(extractBaseName(cutcount_file), "on", extractBaseName(site_file), sep = "_")))
reduced_bgr_filename_zip <- paste(reduced_bgr_filename, ".gz", sep = "")
overlapping <- function(A, B) {
if (as.character(A$chr[1]) != as.character(B$chr[1])) {
null
} else {
R <- vector("logical", length = nrow(A))
rangeA <- 1:length(A$st)
rangeB <- 1:length(B$st)
if ((length(rangeA) != 0) && (length(rangeB) != 0)) {
P <- list(Ast = A$st[rangeA], Aed = A$ed[rangeA], Bst = B$st[rangeB], Bed = B$ed[rangeB])
RR <- intersect_intervals_really_fast_parallel(P)
I1 <- unique(RR)
R[rangeA[I1]] <- TRUE
}
# print(c(i,cend, cendcomp));
R
}
}
filterCutcountByHotspot <- function(sites, chr) {
cat(sprintf("reducing cutcount data for %s\n", chr))
hotspot <- sites[sites$chr == chr, c("chr", "st", "ed")]
extendHotspot <- function(hotspot, extensionbp = 200) {
hotspot$st <- hotspot$st - extensionbp
hotspot$ed <- hotspot$ed + extensionbp
hotspot <- hotspot[hotspot$st > 0, ]
hotspot
}
if (nrow(hotspot) == 0) {
subcutcount <- NULL
} else {
# browser();
exh <- extendHotspot(hotspot, extensionbp = 200)
cutcountchr <- cutcount[[chr]]
ovlp <- overlapping(cutcountchr, exh)
subcutcount <- cutcountchr[ovlp, ]
}
subcutcount
}
if (file.exists(reduced_bgr_filename_zip)) {
cat(sprintf("reading the cutcount data file: %s\n", reduced_bgr_filename_zip))
cutcount <- readcutcount2(reduced_bgr_filename_zip)
} else if (site_file == "" || is.na(site_file) || is.null(site_file)) {
cat(sprintf("The site file is set to %s\n.", site_file))
cutcount <- readcutcount2(cutcount_file)
} else {
cat(sprintf("converting the cutcount data file: %s into %s\n", cutcount_file, reduced_bgr_filename))
cutcount <- readcutcount2(cutcount_file)
sites <- readhotspot(site_file)
# browser();
reduced_cutcount <- parallel::mclapply(names(cutcount), function(x) {
filterCutcountByHotspot(sites, x)
}, mc.cores = MCCORES)
names(reduced_cutcount) <- names(cutcount)
for (ii in seq_along(reduced_cutcount)) {
chr <- names(reduced_cutcount)[[ii]]
dat <- reduced_cutcount[[ii]]
if (!is.null(dat)) {
cat(sprintf("saving cutcount data (%s) for %s\n", reduced_bgr_filename, chr))
dat$st <- dat$st - 1 # zero-based format
if (ii == 1) {
write.table(dat, file = reduced_bgr_filename, sep = " ", append = F, quote = F, row.names = F, col.names = F)
} else {
write.table(dat, file = reduced_bgr_filename, sep = " ", append = T, quote = F, row.names = F, col.names = F)
}
}
}
# browser();
system(sprintf("gzip %s", reduced_bgr_filename)) # gzip
cutcount <- reduced_cutcount
}
cutcount
}
readcutcount2 <- function(cutcountfile) {
cutcount <- readbgr(cutcountfile)
rl <- rle(as.vector(cutcount$chr))
chrs <- rl$values
ed <- cumsum(rl$lengths)
st <- c(1, ed[-length(ed)] + 1)
cutcountPerChrom <- lapply(1:length(chrs), function(ii) {
cutcount[st[ii]:ed[ii], ]
})
names(cutcountPerChrom) <- chrs
cutcount <- {}
cutcountPerChrom
}
getOutputDirAndFilename <- function(outputdir_base, dataname, sitefilename, halfwidth) {
# dataname = datanamelist[[ii]];
hotspotname <- gsub(".csv", "", basename(sitefilename))
od <- file.path(outputdir_base, sprintf("%s_on%s_%dbp", dataname, hotspotname, halfwidth))
list(
dir = od,
filename = file.path(od, sprintf("../%s_on%s_%dbp_out.txt", dataname, hotspotname, halfwidth))
)
}
drawMotifAggPlotOnMotifSetsForMultipleRangesAndWithComparisons <- function(sitefiledir = "",
cache = "",
outputdir = "",
datanamelist = list(),
cutcountdatalist = list(),
normalization = "default",
ncutcounts = NA,
sitefilelist = list(),
motiflistlist = list(),
ftablefile = Fed_mm9_withMap,
nuccodefilePattern = nuccode_hexamer_withMappability_35mer_mm9,
halfwidthlist = c(50, 100),
comparisonIndices = list(c(1, 2), c(3, 2)),
range = NA,
yrange = c(-2.2, 0.5),
graphout = F) {
threshold <- 0
# browser();
outputdir_base <- outputdir
if (!exists("ftablefile")) { # read once,
freqtable <- readFrequencyTable(ftablefile)
np <- round(log(nrow(freqtable) - 1) / log(4))
} else {
np <- 6 # by default
}
chrs <- paste("chr", c(1:22, "X", "Y"), sep = "")
nuccodefiles <- sapply(chrs, function(x) gsub("{chr}", x, nuccodefilePattern, fixed = T))
doexist <- file.exists(nuccodefiles)
chrs <- chrs[doexist]
nuccodefiles <- nuccodefiles[doexist]
nuccodes <- parallel::mclapply(nuccodefiles, function(nuccodefile) {
nuccode <- readNucleotideCodeForChromosomeForCuts(nuccodefile, np)
nuccode
}, mc.cores = MCCORES, mc.preschedule = F)
if (normalization == "default") {
oldncutcounts <- ncutcounts
# browser();
ncutcounts <- sapply(1:length(cutcountdatalist), function(kk) countCutcountOnSites(cutcountdatalist[[kk]], sitefilelist[[kk]]))
}
if (is.na(ncutcounts[1])) {
stop("Error in drawMotifAggPlotOnMotifSetsForMultipleRangesAndWithComparisons: ncutcounts should not be zero.")
}
for (halfwidth in halfwidthlist) {
ll <- lapply(seq_along(datanamelist), function(ii) {
# print(ii);
dataname <- datanamelist[[ii]]
out <- getOutputDirAndFilename(outputdir_base, dataname, sitefilelist[[ii]], halfwidth)
run <- drawMotifAggPlotOnMotifSets(
dataname = dataname,
cutcount = cutcountdatalist[[ii]],
ncutcount = ncutcounts[ii],
hotspotfile = sitefilelist[[ii]],
motiflist = read.table(motiflistlist[[ii]], stringsAsFactors = F),
freqtablefile = ftablefile,
nuccodes = nuccodes,
sitefiledir = sitefiledir,
cache = cache,
outputdir = out$dir,
halfwidth = halfwidth,
logyrange = yrange,
range = range,
graphout = graphout
)
gc()
}) # }, mc.cores=3); # parallel::mclapply
} # for (half
for (index in comparisonIndices) {
for (halfwidth in halfwidthlist) {
i1 <- index[1]
i2 <- index[2]
dataname1 <- datanamelist[[i1]]
dataname2 <- datanamelist[[i2]]
motiflistfile1 <- motiflistlist[[i1]]
motiflistfile2 <- motiflistlist[[i2]]
out1 <- getOutputDirAndFilename(outputdir_base, dataname1, sitefilelist[[i1]], halfwidth)
out2 <- getOutputDirAndFilename(outputdir_base, dataname2, sitefilelist[[i2]], halfwidth)
title <- sprintf("Footprinting Aggregation Plot (%s vs. %s)", dataname1, dataname2)
datadir1 <- sprintf("%s_%dbp", dataname1, halfwidth)
datadir2 <- sprintf("%s_%dbp", dataname2, halfwidth)
MAoutputdir <- file.path(outputdir, "comparison")
dlog(sprintf("\n\n\n\n\n\\nMAoutputdir=%s\n", MAoutputdir))
# browser();
MultipleAggregationPlot(
dataname1 = dataname1,
dataname2 = dataname2,
motiflistfile1 = motiflistfile1,
motiflistfile2 = motiflistfile2,
outputfile1 = out1$filename,
outputfile2 = out2$filename,
title = title, threshold = threshold,
datadir1 = datadir1,
datadir2 = datadir2,
outputdir = MAoutputdir,
# scalefactor = c(scalefactor[i1], scalefactor[i2]),
range = range,
logyrange = yrange, graphout = graphout
)
}
}
}
scatterDataOutGroups <- function(outputdir = "",
datanamelist = list(),
motiflistlist = list(),
sitefilelist = list(),
halfwidthlist = c(50, 100), threshold = 0,
comparisonIndices = list(c(1, 2), c(3, 2)),
range = NA) {
out <- c()
for (index in comparisonIndices) {
for (halfwidth in halfwidthlist) {
i1 <- index[1]
i2 <- index[2]
dataname1 <- datanamelist[[i1]]
dataname2 <- datanamelist[[i2]]
sitefilename <- extractBaseName(basename(sitefilelist[[i1]]))
if (length(motiflistlist) == 1) {
motiflistfile1 <- motiflistlist
} else {
motiflistfile1 <- motiflistlist[[i1]]
}
out1 <- getOutputDirAndFilename(outputdir, datanamelist[[i1]], sitefilelist[[i1]], halfwidth)
out2 <- getOutputDirAndFilename(outputdir, datanamelist[[i2]], sitefilelist[[i2]], halfwidth)
outputfile1 <- out1$filename
outputfile2 <- out2$filename
title <- sprintf("Footprinting Aggregation Plot (%s vs. %s)", dataname1, dataname2)
datadir1 <- sprintf("%s_%dbp", dataname1, halfwidth)
datadir2 <- sprintf("%s_%dbp", dataname2, halfwidth)
MAoutputdir <- file.path(outputdir, "comparison")
outputcsvfile <- sprintf("%s_%s_On_%s_footprint_depth_table.csv", dataname1, dataname2, sitefilename)
outfilename <- ScatterDataOut(
dataname1 = dataname1,
dataname2 = dataname2,
motiflistfile = motiflistfile1,
outputfile1 = outputfile1,
outputfile2 = outputfile2,
title = title, threshold = threshold, datadir1 = datadir1, datadir2 = datadir2, outputcsv =
outputcsvfile,
range = range
)
out <- c(out, outfilename)
}
}
out
}
ScatterDataOut <- function(dataname1 = "", dataname2 = "", motiflistfile = "",
outputfile1 = "", outputfile2 = "", title = "", threshold = 0, datadir1 = "", datadir2 = "", outputcsv = " ", range = NA) {
motiflist <- read.table(motiflistfile)
calcObsMeans <- function(mobs1, mobs2, motifcenter, motiflogo, meanlog1, meanlog2, numsite) {
motifoffset <- 2
halfwidth <- (length(mobs1) - 1) / 2
motifwidth <- nchar(motiflogo)
xr <- seq(from = 1, to = nchar(motiflogo)) - motifcenter
hgt <- 0
x1 <- min(xr)
x2 <- max(xr)
linea <- x1 - motifoffset
lineb <- x2 + motifoffset
motifregion <- linea:lineb + halfwidth + 1
flanking <- setdiff(1:length(mobs1), motifregion)
c1 <- log(sum(mobs2[motifregion]) / sum(mobs1[motifregion]), 2)
c2 <- log(sum(mobs2[flanking]) / sum(mobs1[flanking]), 2)
c3 <- log(sum(mobs2) / sum(mobs1), 2)
meancc1 <- mean(mobs1)
meancc2 <- mean(mobs2)
data.frame(lrmotif = c1, lrflanking = c2, lrtotal = c3, fdepth1 = meanlog1, fdepth2 = meanlog2, meancc1 = meancc1, meancc2 = meancc2, numsite)
}
calcprog <- function(ii) {
motif <- motiflist[ii, ]
name <- as.character(motif$motif)
# print(name);
# rank = difforder[ii];
sites1 <- output1[ii, ]
sites2 <- output2[ii, ]
numsite1 <- sites1$numSites
numsite2 <- sites2$numSites
motif <- motiflist[ii, ]
name <- as.character(motif$motif)
motiflogo <- as.character(motif$logo)
motifcenter <- motif$center
load(as.character(output1$graphdatafile[ii]))
mobs1 <- mobs
mexp1 <- mexp
logratio1 <- logratio - baselogratio
meanlog1 <- motiflogratio - baselogratio
load(as.character(output2$graphdatafile[ii]))
mobs2 <- mobs
mexp2 <- mexp
logratio2 <- logratio - baselogratio
meanlog2 <- motiflogratio - baselogratio
plot1yrange <- 0
halfwidth <- (length(logratio1) - 1) / 2
filenamebase <- make.names(name)
calcObsMeans(mobs1, mobs2, motifcenter, motiflogo, meanlog1, meanlog2, numsite1)
}
if (!is.na(range[1])) {
motiflist <- motiflist[range, ]
}
output1 <- read.table(outputfile1, sep = "\t") # 200
output2 <- read.table(outputfile2, sep = "\t") # 199
selection <- (output1$numSites >= threshold) & (output2$numSites >= threshold)
output1 <- output1[selection, ]
output2 <- output2[selection, ]
motiflist <- motiflist[selection, ]
cal <- do.call("rbind", lapply(1:nrow(motiflist), calcprog))
tabl <- data.frame(motiflist, cal)
write.table(tabl, file = outputcsv)
outputcsv
}
runGfoot <- function(Gfoot, range = NA, graphout = F, halfwidths = c(50), normalization = "default", yrange = c(-2.2, 0.5)) {
drawMotifAggPlotOnMotifSetsForMultipleRangesAndWithComparisons(
sitefiledir = Gfoot@motifDB@directory,
cache = Gfoot@cachedir,
outputdir = Gfoot@outputdir,
datanamelist = list(Gfoot@control@name, Gfoot@treatment@name),
cutcountdatalist = list(Gfoot@control@cutcount, Gfoot@treatment@cutcount),
ncutcounts = c(Gfoot@control@count, Gfoot@treatment@count),
normalization = normalization,
sitefilelist = list(Gfoot@sitefile, Gfoot@sitefile),
motiflistlist = list(Gfoot@motifDB@motiflistfile, Gfoot@motifDB@motiflistfile),
ftablefile = Gfoot@gfootoption@biasfile,
nuccodefilePattern = Gfoot@gfootoption@hexamer_pattern,
halfwidthlist = halfwidths,
comparisonIndices = list(c(1, 2)),
range = range,
graphout = graphout,
yrange = yrange
)
scatterDataOutGroups(
outputdir = Gfoot@outputdir,
datanamelist = list(Gfoot@control@name, Gfoot@treatment@name),
motiflistlist = list(Gfoot@motifDB@motiflistfile, Gfoot@motifDB@motiflistfile),
sitefilelist = list(Gfoot@sitefile, Gfoot@sitefile),
halfwidthlist = halfwidths,
comparisonIndices = list(c(1, 2)),
range = range
)
}
####
# OOP definition of
#' @export CutCount
CutCount <- setClass("CutCount",
representation(
file = "character",
cutcount = "list",
count = "numeric",
name = "character"
),
prototype = list(
file = "",
cutcount = list(),
count = 0,
name = ""
),
validity = function(object) {
if (file.exists(object@file) && object@count > 0) TRUE else "File not exist or count==0"
}
)
#' @export
setGeneric(
name = "readCutCountSites", # CutCount::readCutCountOnSites
def = function(obj, site_filename) {
standardGeneric("readCutCountSites")
}
)
#' @export
setMethod(
f = "readCutCountSites", # CutCount::readCutCountOnSites
signature = "CutCount",
definition = function(obj, site_filename) {
if (length(obj@cutcount) == 0) {
cat(sprintf("reading %s ...\n", obj@file))
obj@cutcount <- readCutCountOnSites(cutcount_file = obj@file, site_file = site_filename)
}
return(obj)
}
)
#' @export MotifDB
MotifDB <- setClass("MotifDB",
representation(
motiflistfile = "character",
directory = "character"
),
prototype = list(
motiflistfile = "",
directory = ""
)
)
#' @export GFootOption
GFootOption <- setClass("GFootOption",
representation(
biasfile = "character",
hexamer_pattern = "character"
),
prototype = list(
biasfile = "",
hexamer_pattern = ""
)
)
#' @export GFoot
GFoot <- setClass("GFoot",
representation(
control = "CutCount",
treatment = "CutCount",
sitefile = "character",
motifDB = "MotifDB",
gfootoption = "GFootOption",
outputdir = "character",
cachedir = "character",
outputfiles = "character"
),
prototype = list(
sitefile = "",
motifDB = MotifDB(),
gfootoption = GFootOption(),
outputdir = getwd(),
cachedir = file.path(getwd(), "cache"),
outputfiles = ""
)
)
#' @export
setGeneric(
name = "loadCutcount", # GFoot::run
def = function(obj) {
standardGeneric("loadCutcount")
}
)
#' @export
setMethod(
f = "loadCutcount", # GFoot::calcHeatmap
signature = "GFoot",
definition = function(obj) {
if (length(obj@control@cutcount) == 0) {
obj@control <- readCutCountSites(obj@control, obj@sitefile) # read cutcount
}
if (length(obj@treatment@cutcount) == 0) {
obj@treatment <- readCutCountSites(obj@treatment, obj@sitefile) # read cutcount
}
return(obj)
}
)
#' @export
setGeneric(
name = "run", # GFoot::run
def = function(obj, range = NA, graphout = F, yrange = c(-2.2, 0.5), mc.cores = MCCORES) {
standardGeneric("run")
}
)
#' @export
setMethod(
f = "run", # GFoot::run
signature = "GFoot",
definition = function(obj, range = NA, graphout = F, yrange = c(-2.2, 0.5), mc.cores = MCCORES) {
# obj <- loadCutcount(obj);
if (length(obj@control@cutcount) == 0) {
# browser();
obj@control <- readCutCountSites(obj@control, obj@sitefile) # read cutcount
}
if (length(obj@treatment@cutcount) == 0) {
obj@treatment <- readCutCountSites(obj@treatment, obj@sitefile) # read cutcount
}
# obj@treatment<-readCutCountSites(obj@treatment, obj@sitefile); #read cutcount
cat(sprintf("Num. of CPU cores to use: %d\n", mc.cores))
MCCORES <- mc.cores
obj@outputfiles <- runGfoot(
obj, # GFoot obj
range = range, # Range of CREB
graphout = graphout,
yrange = yrange
)
return(obj)
}
)
###################################### bfoot_calc##################################################
gfoot_ptm <<- proc.time()
# initial time
dlog <- function(output, ...) {
# ~ # cat(sprintf("[%g sec] %s", etime[["elapsed"]], output, ...));
}
#' @useDynLib bagfoot intersect_intervals
intersect_intervals_fast_index2C <- function(X1, X2, Y1, Y2) {
Isorted1 <- order(X1)
Isorted2 <- order(Y1)
.Call("intersect_intervals", as.numeric(X1), as.numeric(X2), as.numeric(Y1), as.numeric(Y2), as.integer(Isorted1), as.integer(Isorted2))
}
#' @export
readFrequencyTable <- function(ftablefile) {
cat(sprintf("reading %s ......\n", ftablefile))
ftable <- read.table(ftablefile)
nr <- nrow(ftable)
ftable[nr, c("GPRatio", "ObCutRatio")] <- 0
ftable <- cbind(ftable, Aj = ftable$ObCuts / ftable$GenomicPositionCount)
ftable$Aj[nr] <- 0
# cat(sprintf("Num of cuts = %d ......\n", sum(ftable$ObCuts)));
ftable
}
intersect_intervals_fast_parallel_index2 <- function(P) {
R <- {}
if (length(P$Ast) == 0 || length(P$Bst) == 0) {
return(R)
}
R <- intersect_intervals_fast_index2C(P$Ast, P$Aed, P$Bst, P$Bed)
R
}
OverlappingIndexAParallel <- function(A, B, mc.cores = MCCORES) {
OverlappingIndexAParallelPerChrom <- function(i) {
# cat(paste(chrs[i],'\n'));
result <- NULL
iA <- 1:length(A$st)
iB <- 1:length(B$st)
rangeA <- iA[A$chr == chrs[i]]
rangeB <- iB[B$chr == chrs[i]]
if ((length(rangeA) != 0) && (length(rangeB) != 0)) {
P <- list(Ast = A$st[rangeA], Aed = A$ed[rangeA], Bst = B$st[rangeB], Bed = B$ed[rangeB])
RR <- intersect_intervals_fast_parallel_index2(P)
if (!is.null(RR)) {
result <- rangeA[RR]
}
}
result
}
chrs <- levels(factor(A$chr))
if (mc.cores == 1) {
kk <- lapply(seq_along(chrs), OverlappingIndexAParallelPerChrom)
} else {
kk <- parallel::mclapply(seq_along(chrs), OverlappingIndexAParallelPerChrom, mc.cores = mc.cores)
}
# browser();
unlist(kk)
}
CompareHotspotsFaster <- function(A, B, mc.cores = MCCORES) {
idx <- OverlappingIndexAParallel(A, B, mc.cores = mc.cores)
A[idx, ]
}
filesizenotzero <- function(filename) {
return(ifelse(file.exists(filename), file.info(filename)$size > 0, FALSE))
}
readbgr2 <- function(filename) {
if (filename == "") {
R <- NULL
R
} else {
# browser();
R <- readLines(filename, n = 7)
i <- 1
while (i < 7) {
if (substr(R[[i]], 1, 3) == "chr") break
i <- i + 1
}
if (i >= 7) {
R <- {}
sprintf("%s is not a readable bed file.", A)
return
} else {
# R=read.csv(filename, sep="", header=FALSE,comment.char="", skip=i-1, colClasses=c("factor","numeric","numeric","numeric"), stringsAsFactors=FALSE);
if (length(grep(".gz", filename)) == 1) {
freadfilename <- paste("zcat", filename)
} else {
freadfilename <- filename
}
R <- data.frame(data.table::fread(freadfilename, header = FALSE, skip = i - 1, colClasses = c("factor", "numeric", "numeric", "numeric"), stringsAsFactors = FALSE))
names(R) <- c("chr", "st", "ed", "value")
R$chr <- factor(R$chr)
R$st <- R$st + 1
R
}
}
}
readcutcount3 <- function(cutcountfile) {
cutcount <- readbgr2(cutcountfile)
cutcountPerChrom <- split(cutcount, cutcount$chr)
cutcount <- {}
cutcountPerChrom
}
readBGRAsList <- readcutcount3
drawMotifAggPlotOnMotifSets <- function(dataname = "",
cutcount = "",
ncutcount = 0,
hotspotfile = "",
motiflist = "",
freqtablefile = NA,
nuccodes = NA,
sitefiledir = "",
cache = ".",
outputdir = "",
halfwidth = 50,
strandwisePlot = F,
logyrange = c(-2.2, 0.5),
range = NA,
graphout = F) {
ftable <- readFrequencyTable(freqtablefile)
if (!file.exists(cache) && cache != "") {
dir.create(cache, recursive = T)
}
if (cache == "") {
cache <- getwd()
}
if (!file.exists(outputdir) && cache != "") {
dir.create(outputdir, recursive = T)
}
if (outputdir == "") {
outputdir <- getwd()
}
begintime <- Sys.time()
# out<- lapply(1:length(motiflist$motif), function(ii) {
if (is.na(range[1])) {
range <- 1:length(motiflist$motif)
}
jj <- 0
result <- vector("list", length = length(motiflist$motif))
if (ncutcount == 0) {
stop("Error in drawMotifAggPlotOnMotifSets: ncutcount should not be zero.")
}
scalefactor <- 100000000 / ncutcount
cat(sprintf("num cutcount = %g, scale factor = %g\n", ncutcount, scalefactor))
runCalc <- function(ii, graphout = F) {
# if (ii==255) {
# browser();
# }
outputfilename <- make.names(sprintf("%s_%s_hw%dbp", motiflist$motif[ii], dataname, halfwidth))
jpgfile1 <- file.path(outputdir, sprintf("aggregation_%s_observed_expected_cuts.jpg", outputfilename))
cat(sprintf("No.%3d Motif:%s (%s %d bp)\n", ii, motiflist$motif[ii], motiflist$logo[ii], motiflist$motiflength[ii]))
# cat(sprintf('Center=%d', ii, motiflist$motif[ii], motiflist$logo[ii], motiflist$motiflength[ii]));
motif <- list(
name = as.character(motiflist$motif[ii]),
sitefile = file.path(sitefiledir, motiflist$filename[ii]),
motiflogo = as.character(motiflist$logo[ii]),
# logopic=NA,
motifcenter = motiflist$center[ii]
)
motifname <- motiflist$motif[ii]
HS <- list(
name = "Hotspot",
hotspotfile = hotspotfile,
cutcountfile = cutcount,
cutcountsignature = digest::digest(cutcount)
)
ChIP <- list( # Rep 1: GH1158 Rep 2: GH1159
name = "ChIP",
hotspotfile = hotspotfile
)
AggregationPlotData <- list(
motifinfo = motif,
dhsinfo = HS,
chipinfo = ChIP,
ftablefile = freqtablefile,
ftable = ftable,
nuccodes = nuccodes
)
ss <- DrawStamMotifAggPlotOnChIPBoundRegions(AggregationPlotData,
output = outputfilename,
halfwidth = halfwidth,
title = sprintf("%s (%s)", dataname, motifname),
yrange = logyrange,
cache = cache,
outputdir = outputdir,
strandwisePlot = strandwisePlot,
scalefactor = scalefactor,
graphout = graphout
)
# cat("warning: bfoot.R line 813:\n");
ss
}
if (MCCORES_MOTIF == 1) {
result <- lapply(range, function(x) {
runCalc(x, graphout = graphout)
}) # parallel::mclapply
} else {
# result<- foreach::foreach(x = range, .combine=c) %dopar% runCalc(x, graphout=graphout);
result <- parallel::mclapply(range, function(x) {
runCalc(x, graphout = graphout)
}, mc.cores = MCCORES_MOTIF)
# ); # parallel::mclapply
}
# cat("warning: bfoot.R line 825:\n");
rout <- do.call("rbind", result)
hotspotname <- gsub(".csv", "", basename(hotspotfile))
outputfilename <- file.path(outputdir, sprintf("../%s_on%s_%dbp_out.txt", dataname, hotspotname, halfwidth))
# browser();
write.table(rout, file = outputfilename, sep = "\t")
cat("warning: bfoot.R line 831:\n")
# }
rout
}
quicksummaryListOrString <- function(obj) {
if (is.character(obj)) {
r <- digest::digest(obj)
} else {
r <- digest::digest(c(unlist(lapply(obj, nrow))))
}
r
}
DrawStamMotifAggPlotOnChIPBoundRegions <- function(S, tag = "", motifdir = c(1, -1), output = "", halfwidth = 20, movingaverage = F, plot1yrange = 0, yrange = c(-2.5, 1.5), title = "", cache = ".", outputdir = ".", strandwisePlot = F, scalefactor = 1, graphout = F) {
S$cutcountname <- S$dhsinfo$name
S$DHS <- S$dhsinfo$hotspotfile
S$cutcount <- S$dhsinfo$cutcountfile
S$cutcountsignature <- S$dhsinfo$cutcountsignature
S$CHIP <- S$chipinfo$hotspotfile
S$chipname <- S$chipinfo$name
S$motif <- S$motifinfo$sitefile
S$output <- paste(S$output, tag, sep = "")
# dlog("DrawStamMotifAggPlotOnChIPBoundRegions():\n");
# print(S$motif);
# print(yrange);
savefile <- file.path(cache, sprintf("%s_%s_motifplotresult.dat", make.names(S$motifinfo$name), digest::digest(c(S$motif, S$CHIP, S$DHS, S$cutcountsignature, halfwidth))))
readHotspotRegions <- function(filename) {
dhs <- data.frame(data.table::fread(filename))
names(dhs) <- c("ID", "chr", "st", "ed", "MaxD", "AveD", "Zscore", "pvalue")
dhs <- dhs[dhs$chr != "chrM", ] # eliminate chrM regions from the analysis
dhs
}
if (!file.exists(savefile) || BFOOT_DEBUG) {
motif_chip_dhs <- NULL
motif <- data.frame(data.table::fread(S$motif))[, -1]
motif <- motif[, 1:5]
istart <- match("start", names(motif))
if (istart == 2) {
names(motif)[(istart - 1):(istart + 3)] <- c("chr", "st", "ed", "dir", "value")
direction <- motifdir
direction[direction == 1] <- "+"
direction[direction == -1] <- "-"
motif <- motif[is.element(motif$dir, direction), ]
} else {
names(motif) <- c("chr", "st", "ed", "value", "dir")
motif <- motif[is.element(motif$dir, motifdir), ]
}
motifwidth <- motif$ed[1] - motif$st[1] + 1
result <- NULL
if (is.na(S$DHS) || is.null(S$DHS) || (S$DHS == "")) {
motif_chip_dhs <- motif
} else {
dhs <- readHotspotRegions(S$DHS)
if (S$DHS == S$CHIP) {
chip <- dhs
} else {
chip <- data.frame(data.table::fread(S$CHIP))
if (ncol(chip) > 7) {
names(chip) <- c("ID", "chr", "st", "ed", "MaxD", "AveD", "Zscore", "pvalue")
} else if (ncol(chip) == 3) {
names(chip) <- c("chr", "st", "ed")
}
}
if (nrow(motif) > 0) {
# dlog(">>>>>\n");
motif_chip <- CompareHotspotsFaster(motif, chip, mc.cores = MCCORES)
# dlog("CompareHotspots>>>>>\n");
if (S$DHS == S$CHIP) {
motif_chip_dhs <- motif_chip
} else {
motif_chip_dhs <- CompareHotspotsFaster(motif_chip, dhs, mc.cores = MCCORES)
}
}
if (nrow(motif_chip_dhs) > MAX_MOTIF_SITES) {
set.seed(1)
motif_chip_dhs <- motif_chip_dhs[sample(nrow(motif_chip_dhs), MAX_MOTIF_SITES), ]
}
}
# browser();
# if (nrow(motif_chip_dhs) < 1) {
# browser();
# }
result <- StamMotifAggPlot(motif_chip_dhs, S, output = output, halfwidth = halfwidth, movingaverage = movingaverage, yrange = yrange, plot1yrange = plot1yrange, title = title, cache = cache, outputdir = outputdir, strandwisePlot = strandwisePlot, scalefactor = scalefactor, graphout = graphout)
save(result, file = savefile)
# load('/home/baeks/Data/project/saoriATAC/analysis_LTED2/bagfoot_by_clusters/T47D_8clusters/cl1/CACHE_T47D_Control/ZNF740_10bp_6369db133f60569f7f1c39ad4005bec7_motifplotresult.dat');
# cat(sprintf("#sites = %d\n", nrow(motif_chip_dhs)));
} else {
cat(sprintf("loading %s\n", savefile))
load(savefile)
}
# cat('line 941\n');
result
} ###### DrawStamMotifAggPlotOnChIPBoundRegions
calcNormalizedExpectedObservedCounts <- function(sites, S, cache, basehalfwidth, halfwidth, scalefactor, motifbuffer = 2, halfflankingregionwidth = 50, heatmapout = "") {
getSubCount <- function(from, to) {
subrange_st <- motifcenter + ((basehalfwidth) + from)
subrange_ed <- motifcenter + ((basehalfwidth) + to)
isForward <- counts$sites$dir == 1 | counts$sites$dir == "+"
st <- ifelse(isForward, counts$region$st + subrange_st - 1, counts$region$ed - subrange_ed + 1)
ed <- ifelse(isForward, counts$region$st + subrange_ed - 1, counts$region$ed - subrange_st + 1)
subobserved <- counts$observed[, (subrange_st:subrange_ed) - 1]
# browser();
if (length(isForward) == 1) {
if (isForward) {
subobserved <- counts$observed[, (subrange_st:subrange_ed) + 1]
}
} else {
subobserved[isForward, ] <- counts$observed[isForward, (subrange_st:subrange_ed) + 1]
}
subexpected <- counts$expected[, subrange_st:subrange_ed]
subcounts <- list(sites = counts$sites, dir = counts$sites$dir, region = data.frame(chr = counts$region$chr, st = st, ed = ed), observed = subobserved * scalefactor, expected = subexpected * scalefactor)
}
average_aggregation <- function(heatmap) {
sum1 <- apply(heatmap, 1, sum)
notnull <- !is.na(sum1)
num <- sum(notnull)
apply(heatmap[notnull, ], 2, sum) / num
}
calc_logratio <- function(cc) {
if (is.null(dim(cc$expected)) && length(cc$expected) > 0) {
mexp <- cc$expected
} else {
rowNA1 <- is.na(apply(cc$expected, 1, sum))
mexp <- average_aggregation(cc$expected[!rowNA1, ])
}