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mergePipelineRuns_functions.R
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mergePipelineRuns_functions.R
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##########################################################################################
## ##
## Functions to combine pipeline runs for individual samples into something more useful ##
## ##
## Author: Rob Kitchen ([email protected]) ##
## ##
## Version 4.6.3 (2016-10-08) ##
## ##
##########################################################################################
##
## Main function to read and plot exceRpt output in a given directory
##
processSamplesInDir = function(data.dir, output.dir=data.dir, scriptDir="~/Dropbox/Work/YALE/exRNA/exceRpt"){
## Look for samples to merge
printMessage(c("Searching for valid exceRpt pipeline output in ",data.dir))
samplePathList = unique(SearchForSampleData(data.dir,""))
## get sample names and remove duplicates:
sampleIDs = sapply(samplePathList, function(path){ tmp=unlist(strsplit(path,"/")); tmp[length(tmp)] })
samplePathList = samplePathList[!duplicated(sampleIDs)]
## -- Kill script if we do not have any samples to process
NumberOfCompatibleSamples = length(samplePathList)
stopifnot(NumberOfCompatibleSamples > 0)
printMessage(c("Found ",NumberOfCompatibleSamples," valid samples"))
## reads, normalises, and saves individual sample results
sampleIDs = readData(samplePathList, output.dir)
## do we have sample groups?
sampleGroups = data.frame(sampleID=sampleIDs, sampleGroup=rep("noGroup",length(sampleIDs)),stringsAsFactors=F)
if("exceRpt_sampleGroupDefinitions.txt" %in% dir(output.dir)){
## read new groups
groups.tmp = read.table(paste(output.dir,"/exceRpt_sampleGroupDefinitions.txt",sep=""), stringsAsFactors=F,header=T)
## remove samples that are not present in this sample set
groups.tmp = groups.tmp[groups.tmp$sampleID %in% sampleGroups$sampleID, ]
## apply new sample groups to these samples
sampleGroups[match(groups.tmp$sampleID, sampleGroups$sampleID), ]$sampleGroup = groups.tmp$sampleGroup
## write the table back in case there are unassigned / new samples
write.table(sampleGroups, file=paste(output.dir,"/exceRpt_sampleGroupDefinitions.txt",sep=""), sep="\t",row.names=F,col.names=T,quote=F)
}else{
# if not, write a template
write.table(sampleGroups, file=paste(output.dir,"/exceRpt_sampleGroupDefinitions.txt",sep=""), sep="\t",row.names=F,col.names=T,quote=F)
}
## if there's only one sample group, don't bother
if(length(unique(sampleGroups$sampleGroup))==1){
sampleGroups = NA
}
## plot the data
#PlotData(sampleIDs, output.dir, taxonomyPath=paste(scriptDir,"/NCBI_Taxonomy.RData",sep=""), sampleGroups)
PlotData(sampleIDs, output.dir, sampleGroups)
## output warnings
w = warnings()
if(!is.null(w)){
printMessage("Warning messages:")
print(w)
}
}
##
## check dependencies
##
lib="~/R/packages"
lib="/efs/bin/R/R_Packages/"
if(!file.exists(lib)){ dir.create(lib) }
.libPaths(c(lib, .libPaths()))
#baseURL = "https://cran.us.r-project.org"
baseURL = "https://cran.r-project.org"
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager",lib=lib,repos=baseURL)
if(!"plyr" %in% rownames(installed.packages())) { install.packages("plyr",lib=lib,repos=baseURL) }
if(!"gplots" %in% rownames(installed.packages())) { install.packages("gplots",lib=lib,repos=baseURL) }
if(!"marray" %in% rownames(installed.packages())) { BiocManager::install("marray",lib=lib,ask=F) }
if(!"reshape2" %in% rownames(installed.packages())) { install.packages("reshape2",lib=lib,repos=baseURL) }
if(!"tidyverse" %in% rownames(installed.packages())) { install.packages("tidyverse",lib=lib,repos=baseURL) }
if(!"tools" %in% rownames(installed.packages())) { install.packages("tools",lib=lib,repos=baseURL) }
if(!"Rgraphviz" %in% rownames(installed.packages())) { BiocManager::install("Rgraphviz",lib=lib,ask=F) }
if(!"scales" %in% rownames(installed.packages())) { install.packages("scales",lib=lib,repos=baseURL) }
## update
update.packages(repos=baseURL,lib=lib,ask=F)
## load
require(plyr, lib.loc=lib)
require(gplots, lib.loc=lib)
require(marray, lib.loc=lib)
require(reshape2, lib.loc=lib)
require(tidyverse, lib.loc=lib)
require(tools, lib.loc=lib)
require(Rgraphviz, lib.loc=lib)
require(scales, lib.loc=lib)
##
## Function to recursively search a given directory for pipeline output
##
SearchForSampleData = function(base.dir, directory=""){
to.return = NULL
dir.use = paste(base.dir, directory, sep = "/")
subdirs = dir(dir.use)
if(length(subdirs) > 0){
i.stats = grep("\\.stats$", subdirs, perl=T)
i.zip = grep("\\.zip$", subdirs, perl=T)
i.tar = grep("\\.tgz$|\\.tar.gz$", subdirs, perl=T)
if(length(i.stats) > 0){ ## handle decompressed pipeline output
tmp = gsub("\\.stats$","",subdirs[i.stats])
to.return = c(to.return, paste(dir.use,tmp,sep="/"))
}
else if(length(i.zip) > 0){ ## handle zipped pipeline output
for(x in i.zip){
tmp.contents = unzip(paste(dir.use,subdirs[x],sep="/"), list=T)[,1]
if(length(grep("\\.stats$", tmp.contents, perl=T)) > 0){
try(unzip(paste(dir.use,subdirs[x],sep="/"), exdir=gsub("\\.zip","",file_path_as_absolute(paste(dir.use,subdirs[x],sep="/"))), overwrite=FALSE), silent=T)
to.return = c(to.return, paste(dir.use,gsub("\\.zip$","",subdirs[x]),sep="/",gsub("\\.stats$","",tmp.contents[grep("\\.stats$", tmp.contents)])))
}
}
}
else if(length(i.tar) > 0){ ## handle [tar] gzipped pipeline output
for(x in i.tar){
tmp.dir = paste(dir.use,subdirs[x],sep="/")
tmp.contents = untar(tmp.dir, list=T, tar="tar")
if(length(grep("\\.stats$", tmp.contents, perl=T)) > 0){
try(untar(tmp.dir, exdir=gsub("\\.tgz$|\\.tar.gz$","",file_path_as_absolute(tmp.dir)), tar="tar"), silent=T)
to.return = c(to.return, paste(dir.use, gsub("\\.tgz$|\\.tar.gz$","",subdirs[x]), gsub("\\.stats$","",tmp.contents[grep("\\.stats$", tmp.contents)]),sep="/"))
}
}
}
else{
## handle unknown directories
i.known = c(i.stats,i.zip,i.tar)
if(length(i.known) == 0){ # there are no .stats, .zip, or .tgz/.tar.gz files in the directory!
i.unknown = 1:length(subdirs)
}else{
i.unknown = (1:length(subdirs))[-i.known]
}
if(length(i.unknown) > 0){
for(x in subdirs[i.unknown]){
to.return = c(to.return, SearchForSampleData(dir.use, x))
}
}
}
return(unique(to.return))
}
}
##
## Prints the given message with a timestamp
##
printMessage = function(message=""){
cat(as.character(Sys.time()),": ",paste(message,sep=""),"\n",sep="")
}
##
## Plots a taxonomy tree with a given set of weights
##
plotTree = function(rEG, taxonomyInfo, counts_uniq, counts_cum, title="", what){
## node parameters
nNodes = length(nodes(rEG))
nA <- list()
nA$shape = rep("circle",nNodes)
nA$fixedSize<-rep(FALSE, nNodes)
nA$height <- nA$width <- rescale(sqrt(counts_cum/10), to=c(0.25,7))
nA$color <- rep(rgb(0,0,0,0.25),nNodes)
nA$style <- rep("bold", nNodes)
if(what == "exogenousRibosomal"){
nA$fillcolor <- sapply(counts_uniq*10, function(val){ if(val>100){val=100}; rgb(100-val,100,100-val,maxColorValue=100)})
}else{
nA$fillcolor <- sapply(counts_uniq*10, function(val){ if(val>100){val=100}; rgb(100-val,100-val,100,maxColorValue=100)})
}
newNodeIDs = sapply(taxonomyInfo[match(as.numeric(nodes(rEG)), taxonomyInfo$ID), ]$name, function(id){ newID=unlist(strsplit(id," ")); if(length(newID) == 1){id}else{paste(newID[1], "\n", paste(newID[-1],collapse=" "), sep="") }})
nA$label <- paste(newNodeIDs,"\n",round(counts_cum*10)/10,"%",sep="")
nA <- lapply(nA, function(x) { names(x) <- nodes(rEG); x})
## edge parameters
eA <- list(arrowsize=rep(0.1,length(names(rEG@edgeData))), arrowhead=rep("none",length(names(rEG@edgeData))))
eA <- lapply(eA, function(x) { names(x) <- names(rEG@edgeData); x})
## layout the graph
tmp = layoutGraph(rEG, nodeAttrs=nA, edgeAttrs=eA)
## hack to make sure the node labels are visible!
sizes = rescale(tmp@renderInfo@nodes$rWidth, to=c(0.2,1.5))
names(sizes) = nodes(rEG)
nodeRenderInfo(tmp) <- list(cex=sizes)
graphRenderInfo(tmp) <- list(main=title)
## plot the graph
renderGraph(tmp)
}
#
# ##
# ##
# ##
# getIntermediateNodeIDs = function(nodeID, edges, intermediates){
# parentID = edges[edges$tax_id == nodeID, ]$parent_tax_id
# if(nodeID == parentID){
# # this is root, add it just in case
# intermediates = c(intermediates, parentID)
# }
#
# if(parentID %in% intermediates | nodeID == parentID){
# return(intermediates)
# }else{
# return(getIntermediateNodeIDs(parentID, edges, c(intermediates, parentID)))
# }
# }
##
## Plot exogenous genomes
##
plotExogenousTaxonomyTrees = function(counts, cumcounts, what, output.dir, taxonomyInfo, fontScale=2, sampleGroups=NA, minPercent=0.5){
# counts = exprs.exogenousGenomes_specific
# cumcounts = exprs.exogenousGenomes_cumulative
# taxonomyInfo = taxonomyInfo.exogenous_genomes
#
# counts = exprs.exogenousRibosomal_specific
# cumcounts = exprs.exogenousRibosomal_cumulative
# taxonomyInfo = taxonomyInfo.exogenous_rRNA
## add direct count to the cumulative counts matrix
cumcounts = cumcounts+counts
#counts.norm = t(t(counts*100)/colSums(counts))
counts.norm = apply(counts, 2, function(col){ col*100/sum(col) })
cumcounts.norm = apply(cumcounts, 2, function(col){ col*100/col[1] })
dim(counts)
## remove nodes with < 0.1% of all reads
#minPercent = 1
keepRows = which(apply(counts.norm, 1, max) >= minPercent)
keepRows = sort(unique(c(keepRows, which(apply(cumcounts.norm, 1, max) >= minPercent))))
# use only paths through the tree that capture above a certain fraction of reads
counts = counts[keepRows, , drop=F]
cumcounts = cumcounts[keepRows, , drop=F]
nrow(counts)
#data_uniq = counts.norm[keepRows, , drop=F]
#data_cum = cumcounts.norm[keepRows, , drop=F]
#nrow(data_cum)
## Re-scale the node percentages after trimming branches to make the numbers make more sense - shouldn't make much diff to the cumcounts
data_uniq = apply(counts, 2, function(col){ col*100/sum(col) })
data_cum = apply(cumcounts, 2, function(col){ col*100/col[1] })
#if("significantDEX" %in% names(combinedSamples)){
# significant = combinedSamples$significantDEX[keepRows]
# foldChange = combinedSamples$foldChange[keepRows]
#}
## remove edges with no useable counts (based on minPercent threshold)
taxonomyInfo = taxonomyInfo[taxonomyInfo$ID %in% rownames(data_cum), ]
## Build the graph object
rEG <<- new("graphNEL", nodes=as.character(taxonomyInfo$ID), edgemode="directed")
trim <- function (x) gsub("^\\s+|\\s+$", "", x)
apply(taxonomyInfo[-1,], 1, function(row){
from = trim(as.character(row[4]));
if(from %in% taxonomyInfo$ID){ rEG <<- addEdge(trim(as.character(row[4])), trim(as.character(row[3])), rEG, 1) }
NULL })
data_uniq = data_uniq[match(taxonomyInfo$ID, rownames(data_uniq)), , drop=F]
data_cum = data_cum[match(taxonomyInfo$ID, rownames(data_cum)), , drop=F]
data_uniq[is.na(data_uniq)] = 0
data_cum[is.na(data_cum)] = 0
##
## Write to PDF
##
## plot an average tree over all samples
printMessage(c("Plotting a taxonomy tree based on the average of all samples "))
pdf(file=paste(output.dir,"/exceRpt_",what,"_TaxonomyTrees_aggregateSamples.pdf",sep=""),height=7,width=15)
plotTree(rEG, taxonomyInfo, apply(data_uniq, 1, max), rowMeans(data_cum), what=what)
dev.off()
## plot samples individually
printMessage(c("Plotting a separate taxonomy tree for each sample"))
pdf(file=paste(output.dir,"/exceRpt_",what,"_TaxonomyTrees_perSample.pdf",sep=""), height=7, width=15)
for(i in 1:ncol(data_uniq))
plotTree(rEG, taxonomyInfo, data_uniq[,i], data_cum[,i], title=paste(colnames(data_uniq)[i]," (total reads: ",cumcounts[1,i],")", sep=""), what=what)
dev.off()
## if there are groups of samples
if(is.data.frame(sampleGroups)){
printMessage(c("Plotting a separate taxonomy tree for each sample-group"))
pdf(file=paste(output.dir,"/exceRpt_",what,"_TaxonomyTrees_perGroup.pdf",sep=""), height=7, width=15)
for(thisgroup in levels(as.factor(sampleGroups$sampleGroup))){
tmpDat_uniq = rowMeans(data_uniq[, match(sampleGroups[sampleGroups$sampleGroup %in% thisgroup, ]$sampleID, colnames(data_uniq)), drop=F])
tmpDat_cum = rowMeans(data_cum[, match(sampleGroups[sampleGroups$sampleGroup %in% thisgroup, ]$sampleID, colnames(data_cum)), drop=F])
plotTree(rEG, taxonomyInfo, tmpDat_uniq, tmpDat_cum, title=paste(thisgroup,sep=""), what=what)
}
dev.off()
}
}
##
##
##
readData = function(samplePathList, output.dir){
##
## Create objects to contain the data
##
sample.data = vector(mode="list",length=length(samplePathList))
allIDs.calibrator = NULL
allIDs.miRNA = NULL
allIDs.tRNA = NULL
allIDs.piRNA = NULL
allIDs.gencode = NULL
allIDs.circularRNA = NULL
allIDs.exogenous_miRNA = NULL
allIDs.exogenous_rRNA = NULL
allIDs.exogenous_genomes = NULL
taxonomyInfo.exogenous_rRNA = NULL
taxonomyInfo.exogenous_genomes = NULL
stats_cols = c("input","successfully_clipped","passed_quality_filter","failed_homopolymer_filter","passed_phiX_filter",
"calibrator","reads_used_for_alignment","genome","miRNA_sense","miRNA_antisense",
"miRNAprecursor_sense","miRNAprecursor_antisense","tRNA_sense","tRNA_antisense","piRNA_sense","piRNA_antisense",
"gencode_sense","gencode_antisense","circularRNA_sense","circularRNA_antisense","not_mapped_to_genome_or_libs","
repetitiveElements","endogenous_gapped","input_to_exogenous_miRNA","exogenous_miRNA","input_to_exogenous_rRNA",
"exogenous_rRNA","input_to_exogenous_genomes","exogenous_genomes")
#mapping.stats = matrix(0,nrow=length(samplePathList),ncol=30, dimnames=list(1:length(samplePathList), c("input","successfully_clipped","failed_quality_filter","failed_homopolymer_filter","calibrator","UniVec_contaminants","rRNA","reads_used_for_alignment","genome","miRNA_sense","miRNA_antisense","miRNAprecursor_sense","miRNAprecursor_antisense","tRNA_sense","tRNA_antisense","piRNA_sense","piRNA_antisense","gencode_sense","gencode_antisense","circularRNA_sense","circularRNA_antisense","not_mapped_to_genome_or_libs","repetitiveElements","endogenous_gapped","input_to_exogenous_miRNA","exogenous_miRNA","input_to_exogenous_rRNA","exogenous_rRNA","input_to_exogenous_genomes","exogenous_genomes")))
mapping.stats = matrix(0,nrow=length(samplePathList),ncol=length(stats_cols), dimnames=list(1:length(samplePathList),stats_cols))
qc.results = matrix(0,nrow=length(samplePathList),ncol=5, dimnames=list(1:length(samplePathList), c("InputReads","GenomeReads","TranscriptomeReads","TranscriptomeGenomeRatio","TranscriptomeComplexity")))
maxReadLength = 10000
read.lengths = matrix(0,nrow=length(samplePathList),ncol=maxReadLength+1,dimnames=list(1:length(samplePathList), 0:maxReadLength))
##
## Loop through all samples and read the pipeline output
##
printMessage(c("Reading sample data..."))
removeSamples = NULL
for(i in 1:length(samplePathList)){
## Parse the sampleID from the path:
tmp = unlist(strsplit(samplePathList[i], "/"))
thisSampleID = tmp[length(tmp)]
## Get timings and check this sample finished successfully
tmp.stats = read.table(paste(samplePathList[i],".stats",sep=""), stringsAsFactors=F, fill=T, header=F, sep="\t",skip=0,comment.char="")
#x.start = grep("#STATS",tmp.stats[,1])
#x.end = grep("#END OF STATS",tmp.stats[,1])
#if(length(x.start) > 0 && length(x.end) > 0){
# tmp.start = strptime(unlist(strsplit(tmp.stats[x.start[1],1],"Run started at "))[2],"%Y-%m-%d--%H:%M:%S")
# tmp.end = strptime(unlist(strsplit(tmp.stats[x.end[1],1],"Run completed at "))[2],"%Y-%m-%d--%H:%M:%S")
# runTiming = data.frame(start=tmp.start, completed=tmp.end, duration=difftime(tmp.end,tmp.start), duration_secs=as.numeric(difftime(tmp.end,tmp.start,units="secs")))
# continue = T
#}else{
# continue = F
# removeSamples = c(removeSamples, i)
# printMessage(c("[",i,"/",length(samplePathList),"] WARNING: Incomplete run for sample \'",thisSampleID,"\', ignoring"))
#}
runTiming = 0
continue = T
#if(continue == T){
if(T){
##
## Read sample mapping stats
##
tmp.stats = read.table(paste(samplePathList[i],".stats",sep=""), stringsAsFactors=F, fill=T, header=F, sep="\t",skip=0)
#tmp.stats[tmp.stats[,1] %in% "clipped", 1] = "successfully_clipped"
#mapping.stats[i, match(tmp.stats[,1], colnames(mapping.stats))] = as.numeric(tmp.stats[,2])
mapping.stats[i, match(tmp.stats[,1], colnames(mapping.stats))] = as.numeric(tmp.stats[,2])
rownames(mapping.stats)[i] = thisSampleID
##
## Read the QC result
##
adapterConfidence = NA
qcOutcome = NA
if(file.exists(paste(samplePathList[i],".qcResult",sep=""))){
tmp.qc = read.table(paste(samplePathList[i],".qcResult",sep=""), stringsAsFactors=F, fill=T, header=F, sep=" ",skip=0)
if(tmp.qc[1,1] == "Adapter_confidence:"){
adapterConfidence = tmp.qc[1,2]
tmp.qc = tmp.qc[-1,]
}
qcOutcome = tmp.qc[1,2]
qc.results[i, match(gsub(":$","",tmp.qc[-1,1]), colnames(qc.results))] = as.numeric(tmp.qc[-1,2])
#qc.results[i, ] = as.numeric(tmp.qc[-1,2])
rownames(qc.results)[i] = thisSampleID
}
##
## Read the adapter sequence
##
adapterSeq = NA
if(paste(thisSampleID,".adapterSeq",sep="") %in% dir(samplePathList[i])){
tmp.seq = try(read.table(paste(samplePathList[i],"/",thisSampleID,".adapterSeq",sep="")), silent=T)
if(class(tmp.seq) == "try-error"){
adapterSeq = NA
}else{
adapterSeq = as.character(tmp.seq[1,1])
}
}
##
## Read the calibrator counts, if available
##
calibratorCounts = NULL
if(paste(thisSampleID,".clipped.trimmed.filtered.calibratormapped.counts",sep="") %in% dir(samplePathList[i])){
calibratorCounts = try(read.table(paste(samplePathList[i],"/",thisSampleID,".clipped.trimmed.filtered.calibratormapped.counts",sep=""), stringsAsFactors=F)[,2:1], silent=T)
if(class(calibratorCounts) == "try-error"){
calibratorCounts = NULL
}else{
colnames(calibratorCounts) = c("calibratorID","readCount")
}
}
##
## Read the clipped read lengths
##
if(length(grep(".readLengths.txt$", dir(samplePathList[i]))) == 1){
tmp = read.table(paste(samplePathList[i], dir(samplePathList[i])[grep(".readLengths.txt$", dir(samplePathList[i]))], sep="/"))
read.lengths[i, 1:ncol(tmp)] = as.numeric(tmp[2,])
rownames(read.lengths)[i] = thisSampleID
}
##
## Read sample data
##
availableFiles = dir(samplePathList[i])
miRNA_sense=miRNA_antisense = tRNA_sense=tRNA_antisense = piRNA_sense=piRNA_antisense = gencode_sense=gencode_antisense = circRNA_sense=circRNA_antisense = NULL
if("readCounts_miRNAmature_sense.txt" %in% availableFiles){
miRNA_sense = read.table(paste(samplePathList[i],"readCounts_miRNAmature_sense.txt",sep="/"), header=T, sep="\t", comment.char="", stringsAsFactors=F,colClasses=c("character","numeric","numeric","numeric","numeric"), row.names=1)
miRNA_sense = cbind(miRNA_sense, ID=sapply(rownames(miRNA_sense), function(id){ multiID = unlist(strsplit(id,"\\|")); multiIDs = sapply(multiID, function(idPart){unlist(strsplit(idPart,":"))[1]}); if(length(multiIDs) == 1){ multiIDs }else{ paste(sort(multiIDs),collapse="|") } }))
}
if("readCounts_miRNAmature_antisense.txt" %in% availableFiles){
miRNA_antisense = read.table(paste(samplePathList[i],"readCounts_miRNAmature_antisense.txt",sep="/"), header=T, sep="\t", comment.char="", stringsAsFactors=F,colClasses=c("character","numeric","numeric","numeric","numeric"), row.names=1)
miRNA_antisense = cbind(miRNA_antisense, ID=sapply(rownames(miRNA_antisense), function(id){ multiID = unlist(strsplit(id,"\\|")); multiIDs = sapply(multiID, function(idPart){unlist(strsplit(idPart,":"))[1]}); if(length(multiIDs) == 1){ multiIDs }else{ paste(sort(multiIDs),collapse="|") } }))
}
if("readCounts_tRNA_sense.txt" %in% availableFiles){
tmp = read.table(paste(samplePathList[i],"readCounts_tRNA_sense.txt",sep="/"), header=T, sep="\t", comment.char="", stringsAsFactors=F,colClasses=c("character","numeric","numeric","numeric","numeric"), row.names=1)
tmp = cbind(tmp, ID=sapply(rownames(tmp), function(id){ unlist(strsplit(id,"-"))[2] }))
tRNA_sense = ddply(tmp, "ID", function(mat){ c(as.numeric(mat[1,1:2]),sum(mat$multimapAdjustedReadCount),sum(mat$multimapAdjustedBarcodeCount)) })
colnames(tRNA_sense)[-1] = colnames(tmp)[1:4]
tRNA_sense = tRNA_sense[order(tRNA_sense$multimapAdjustedReadCount,decreasing=T), ]
}
if("readCounts_tRNA_antisense.txt" %in% availableFiles){
tmp = read.table(paste(samplePathList[i],"readCounts_tRNA_antisense.txt",sep="/"), header=T, sep="\t", comment.char="", stringsAsFactors=F,colClasses=c("character","numeric","numeric","numeric","numeric"), row.names=1)
tmp = cbind(tmp, ID=sapply(rownames(tmp), function(id){ unlist(strsplit(id,"-"))[2] }))
tRNA_antisense = ddply(tmp, "ID", function(mat){ c(as.numeric(mat[1,1:2]),sum(mat$multimapAdjustedReadCount),sum(mat$multimapAdjustedBarcodeCount)) })
colnames(tRNA_antisense)[-1] = colnames(tmp)[1:4]
tRNA_antisense = tRNA_antisense[order(tRNA_antisense$multimapAdjustedReadCount,decreasing=T), ]
}
if("readCounts_piRNA_sense.txt" %in% availableFiles){
piRNA_sense = read.table(paste(samplePathList[i],"readCounts_piRNA_sense.txt",sep="/"), header=T, sep="\t", comment.char="", stringsAsFactors=F,colClasses=c("character","numeric","numeric","numeric","numeric"), row.names=1)
}
if("readCounts_piRNA_antisense.txt" %in% availableFiles){
piRNA_antisense = read.table(paste(samplePathList[i],"readCounts_piRNA_antisense.txt",sep="/"), header=T, sep="\t", comment.char="", stringsAsFactors=F,colClasses=c("character","numeric","numeric","numeric","numeric"), row.names=1)
}
makeGeneID = function(id){
bits=unlist(strsplit(id,":")); geneNameBits=unlist(strsplit(bits[3],"-"));
geneName = geneNameBits[1]
if(length(geneNameBits) > 2){ geneName=paste(geneNameBits[-length(geneNameBits)],collapse="-") }
paste(geneName,bits[2],sep=":")
}
if("readCounts_gencode_sense.txt" %in% availableFiles){
tmp = read.table(paste(samplePathList[i],"readCounts_gencode_sense.txt",sep="/"), header=T, sep="\t", comment.char="", stringsAsFactors=F,colClasses=c("character","numeric","numeric","numeric","numeric"), row.names=1)
tmp = cbind(tmp, ID=sapply(rownames(tmp), makeGeneID))
gencode_sense = ddply(tmp, "ID", function(mat){ c(as.numeric(mat[1,1:2]),sum(mat$multimapAdjustedReadCount),sum(mat$multimapAdjustedBarcodeCount)) })
colnames(gencode_sense)[-1] = colnames(tmp)[1:4]
gencode_sense = gencode_sense[order(gencode_sense$multimapAdjustedReadCount,decreasing=T), ]
}
if("readCounts_gencode_antisense.txt" %in% availableFiles){
tmp = read.table(paste(samplePathList[i],"readCounts_gencode_antisense.txt",sep="/"), header=T, sep="\t", comment.char="", stringsAsFactors=F,colClasses=c("character","numeric","numeric","numeric","numeric"), row.names=1)
tmp = cbind(tmp, ID=sapply(rownames(tmp), makeGeneID))
gencode_antisense = ddply(tmp, "ID", function(mat){ c(as.numeric(mat[1,1:2]),sum(mat$multimapAdjustedReadCount),sum(mat$multimapAdjustedBarcodeCount)) })
colnames(gencode_antisense)[-1] = colnames(tmp)[1:4]
gencode_antisense = gencode_antisense[order(gencode_antisense$multimapAdjustedReadCount,decreasing=T), ]
}
if("readCounts_circRNA_sense.txt" %in% availableFiles){
circRNA_sense = read.table(paste(samplePathList[i],"readCounts_circRNA_sense.txt",sep="/"), header=T, sep="\t", comment.char="", stringsAsFactors=F,colClasses=c("character","numeric","numeric","numeric","numeric"), row.names=1)
}
if("readCounts_circRNA_antisense.txt" %in% availableFiles){
circRNA_antisense = read.table(paste(samplePathList[i],"readCounts_circRNA_antisense.txt",sep="/"), header=T, sep="\t", comment.char="", stringsAsFactors=F,colClasses=c("character","numeric","numeric","numeric","numeric"), row.names=1)
}
##
## Read exogenous miRNA alignments (if applicable)
##
exogenous_miRNA_sense = NA
exogenous_miRNA_IDs = NULL
if("EXOGENOUS_miRNA" %in% availableFiles){
tmp.dir = paste(samplePathList[i],"EXOGENOUS_miRNA",sep="/")
if("readCounts_miRNAmature_sense.txt" %in% dir(tmp.dir)){
exogenous_miRNA_sense = read.table(paste(tmp.dir,"readCounts_miRNAmature_sense.txt",sep="/"), header=T, sep="\t", comment.char="", stringsAsFactors=F,colClasses=c("character","numeric","numeric","numeric","numeric"), row.names=1)
exogenous_miRNA_IDs = rownames(exogenous_miRNA_sense)
}
if("readCounts_miRNAmature_antisense.txt" %in% dir(tmp.dir)){
exogenous_miRNA_antisense = read.table(paste(tmp.dir,"readCounts_miRNAmature_antisense.txt",sep="/"), header=T, sep="\t", comment.char="", stringsAsFactors=F,colClasses=c("character","numeric","numeric","numeric","numeric"), row.names=1)
}
}
##
## Read exogenous rRNA alignments (if applicable)
##
exogenous_rRNA = NA
exogenous_rRNA_IDs = NULL
if("EXOGENOUS_rRNA" %in% availableFiles){
tmp.dir = paste(samplePathList[i],"EXOGENOUS_rRNA",sep="/")
if("ExogenousRibosomalAlignments.result.taxaAnnotated.txt" %in% dir(tmp.dir)){
exogenous_rRNA = try(read.table(paste(tmp.dir,"/ExogenousRibosomalAlignments.result.taxaAnnotated.txt",sep=""), sep="\t", stringsAsFactors = F, quote="", comment.char="",header=T), silent=T)
if(class(exogenous_rRNA) == "try-error"){
exogenous_rRNA = NULL
}#else{
# colnames(exogenous_rRNA) = c("indent","distFromRoot","level","name","uniqueReads","allSumReads")
#}
## remove the 'Bacteria' stick insect!
i.toRemove = which(exogenous_rRNA$name == "Bacteria" & exogenous_rRNA$level == "genus")
if(length(i.toRemove) > 0)
exogenous_rRNA = exogenous_rRNA[-i.toRemove, ]
exogenous_rRNA_IDs = exogenous_rRNA$name
taxonomyInfo.exogenous_rRNA = unique(rbind(taxonomyInfo.exogenous_rRNA, exogenous_rRNA[,1:5]))
}
}
##
## Read exogenous genome alignments (if applicable)
##
exogenous_genomes = NA
exogenous_genomes_IDs = NULL
if("EXOGENOUS_genomes" %in% availableFiles){
tmp.dir = paste(samplePathList[i],"EXOGENOUS_genomes",sep="/")
if("ExogenousGenomicAlignments.result.taxaAnnotated.txt" %in% dir(tmp.dir)){
exogenous_genomes = read.table(paste(tmp.dir,"/ExogenousGenomicAlignments.result.taxaAnnotated.txt",sep=""), sep="\t", stringsAsFactors = F, quote="", comment.char="", header=T)
if(class(exogenous_genomes) == "try-error"){
exogenous_genomes = NULL
}#else{
# colnames(exogenous_genomes) = c("indent","distFromRoot","level","name","uniqueReads","allSumReads")
#}
i.toRemove = which(exogenous_genomes$name == "Bacteria" & exogenous_genomes$level == "genus")
if(length(i.toRemove) > 0)
exogenous_genomes = exogenous_genomes[-i.toRemove, ]
exogenous_genomes_IDs = exogenous_genomes$name
taxonomyInfo.exogenous_genomes = unique(rbind(taxonomyInfo.exogenous_genomes, exogenous_genomes[,1:5]))
}
}
# Update list of detected smallRNA IDs
allIDs.calibrator = unique(c(allIDs.calibrator, as.character(calibratorCounts$calibratorID)))
allIDs.miRNA = unique(c(allIDs.miRNA, as.character(miRNA_sense$ID)))
allIDs.tRNA = unique(c(allIDs.tRNA, as.character(tRNA_sense$ID)))
allIDs.piRNA = unique(c(allIDs.piRNA, rownames(piRNA_sense)))
allIDs.gencode = unique(c(allIDs.gencode, as.character(gencode_sense$ID)))
allIDs.circularRNA = unique(c(allIDs.circularRNA, rownames(circRNA_sense)))
allIDs.exogenous_miRNA = unique(c(allIDs.exogenous_miRNA, exogenous_miRNA_IDs))
allIDs.exogenous_rRNA = unique(c(allIDs.exogenous_rRNA, exogenous_rRNA_IDs))
allIDs.exogenous_genomes = unique(c(allIDs.exogenous_genomes, exogenous_genomes_IDs))
sample.data[[i]] = list("miRNA_sense"=miRNA_sense,"miRNA_antisense"=miRNA_antisense, "tRNA_sense"=tRNA_sense,"tRNA_antisense"=tRNA_antisense, "piRNA_sense"=piRNA_sense,"piRNA_antisense"=piRNA_antisense, "gencode_sense"=gencode_sense,"gencode_antisense"=gencode_antisense, "circRNA_sense"=circRNA_sense,"circRNA_antisense"=circRNA_antisense, "exogenous_miRNA_sense"=exogenous_miRNA_sense, "exogenous_rRNA"=exogenous_rRNA, "exogenous_genomes"=exogenous_genomes, "adapterSeq"=adapterSeq, "adapterConfidence"=adapterConfidence, "qcOutcome"=qcOutcome, "runTiming"=runTiming, "calibratorCounts"=calibratorCounts)
names(sample.data)[i] = thisSampleID
printMessage(c("[",i,"/",length(samplePathList),"] Added sample \'",thisSampleID,"\'"))
}
}
##
## Remove failed/incomplete samples
##
stopifnot(length(removeSamples) < length(sample.data))
if(length(removeSamples) > 0){
read.lengths = read.lengths[-removeSamples, ]
sample.data = sample.data[-removeSamples]
mapping.stats = mapping.stats[-removeSamples,]
qc.results = qc.results[-removeSamples,]
}
##
## Trim read-length matrix
##
read.lengths = read.lengths[,0:(max(as.numeric(colnames(read.lengths[, colSums(read.lengths) > 0, drop=F])))+1), drop=F]
#read.lengths = read.lengths[,colSums(read.lengths) > 0, drop=F]
##
## Collect IDs
##
allIDs = list("calibrator"=allIDs.calibrator, "miRNA_sense"=allIDs.miRNA, "tRNA_sense"=allIDs.tRNA, "piRNA_sense"=allIDs.piRNA, "gencode_sense"=allIDs.gencode, "circRNA_sense"=allIDs.circularRNA, "exogenous_miRNA"=allIDs.exogenous_miRNA, "exogenous_rRNA"=allIDs.exogenous_rRNA, "exogenous_genomes"=allIDs.exogenous_genomes)
##
## Convert sample data to large per-smallRNA expression matrices
##
printMessage("Creating raw read-count matrices for available libraries")
#run.duration = data.frame(runDuration_string=rep("",length(sample.data)), runDuration_secs=rep(0,length(sample.data)),stringsAsFactors = F)
run.duration = data.frame(runDuration_secs=rep(0,length(sample.data)),stringsAsFactors = F)
rownames(run.duration) = names(sample.data)
exprs.calibrator = matrix(0,ncol=length(sample.data),nrow=length(allIDs$calibrator), dimnames=list(allIDs$calibrator, names(sample.data)))
exprs.miRNA = matrix(0,ncol=length(sample.data),nrow=length(allIDs$miRNA_sense), dimnames=list(allIDs$miRNA_sense, names(sample.data)))
exprs.tRNA = matrix(0,ncol=length(sample.data),nrow=length(allIDs$tRNA_sense), dimnames=list(allIDs$tRNA_sense, names(sample.data)))
exprs.piRNA = matrix(0,ncol=length(sample.data),nrow=length(allIDs$piRNA_sense), dimnames=list(allIDs$piRNA_sense, names(sample.data)))
exprs.gencode = matrix(0,ncol=length(sample.data),nrow=length(allIDs$gencode_sense), dimnames=list(allIDs$gencode_sense, names(sample.data)))
exprs.circRNA = matrix(0,ncol=length(sample.data),nrow=length(allIDs$circRNA_sense), dimnames=list(allIDs$circRNA_sense, names(sample.data)))
exprs.exogenous_miRNA = matrix(0,ncol=length(sample.data),nrow=length(allIDs$exogenous_miRNA), dimnames=list(allIDs$exogenous_miRNA, names(sample.data)))
exprs.exogenousRibosomal_specific = exprs.exogenousRibosomal_cumulative = NULL
exprs.exogenousGenomes_specific = exprs.exogenousGenomes_cumulative = NULL
if(is.null(taxonomyInfo.exogenous_rRNA)){
tmp.nrow = 0
}else{
tmp.nrow = nrow(taxonomyInfo.exogenous_rRNA)
}
exprs.exogenousRibosomal_specific = matrix(0,ncol=length(sample.data),nrow=tmp.nrow, dimnames=list(taxonomyInfo.exogenous_rRNA$ID, names(sample.data)))
exprs.exogenousRibosomal_cumulative = matrix(0,ncol=length(sample.data),nrow=tmp.nrow, dimnames=list(taxonomyInfo.exogenous_rRNA$ID, names(sample.data)))
if(is.null(taxonomyInfo.exogenous_genomes)){
tmp.nrow = 0
}else{
tmp.nrow = nrow(taxonomyInfo.exogenous_genomes)
}
exprs.exogenousGenomes_specific = matrix(0,ncol=length(sample.data),nrow=tmp.nrow, dimnames=list(taxonomyInfo.exogenous_genomes$ID, names(sample.data)))
exprs.exogenousGenomes_cumulative = matrix(0,ncol=length(sample.data),nrow=tmp.nrow, dimnames=list(taxonomyInfo.exogenous_genomes$ID, names(sample.data)))
for(i in 1:length(sample.data)){
#run.duration[i,] = sample.data[[i]]$runTiming[1,4,drop=F]
if(!is.null(nrow(sample.data[[i]]$calibratorCounts)))
exprs.calibrator[match(sample.data[[i]]$calibratorCounts$calibratorID, rownames(exprs.calibrator)),i] = as.numeric(sample.data[[i]]$calibratorCounts$readCount)
exprs.miRNA[match(sample.data[[i]]$miRNA_sense$ID, rownames(exprs.miRNA)),i] = as.numeric(sample.data[[i]]$miRNA_sense$multimapAdjustedReadCount)
exprs.tRNA[match(sample.data[[i]]$tRNA_sense$ID, rownames(exprs.tRNA)),i] = as.numeric(sample.data[[i]]$tRNA_sense$multimapAdjustedReadCount)
exprs.piRNA[match(rownames(sample.data[[i]]$piRNA_sense), rownames(exprs.piRNA)),i] = as.numeric(sample.data[[i]]$piRNA_sense$multimapAdjustedReadCount)
exprs.gencode[match(sample.data[[i]]$gencode_sense$ID, rownames(exprs.gencode)),i] = as.numeric(sample.data[[i]]$gencode_sense$multimapAdjustedReadCount)
exprs.circRNA[match(rownames(sample.data[[i]]$circRNA_sense), rownames(exprs.circRNA)),i] = as.numeric(sample.data[[i]]$circRNA_sense$multimapAdjustedReadCount)
## Exogenous miRNA
if(!is.null(nrow(sample.data[[i]]$exogenous_miRNA)))
exprs.exogenous_miRNA[match(rownames(sample.data[[i]]$exogenous_miRNA), rownames(exprs.exogenous_miRNA)),i] = as.numeric(sample.data[[i]]$exogenous_miRNA$multimapAdjustedReadCount)
## Exogenous rRNA
if(!is.null(nrow(sample.data[[i]]$exogenous_rRNA))){
exprs.exogenousRibosomal_specific[match(sample.data[[i]]$exogenous_rRNA$ID, rownames(exprs.exogenousRibosomal_specific)),i] = as.numeric(sample.data[[i]]$exogenous_rRNA$readCount_direct)
exprs.exogenousRibosomal_cumulative[match(sample.data[[i]]$exogenous_rRNA$ID, rownames(exprs.exogenousRibosomal_cumulative)),i] = as.numeric(sample.data[[i]]$exogenous_rRNA$readCount_inherited)
}
## Exogenous Genomes
if(!is.null(nrow(sample.data[[i]]$exogenous_genomes))){
exprs.exogenousGenomes_specific[match(sample.data[[i]]$exogenous_genomes$ID, rownames(exprs.exogenousGenomes_specific)),i] = as.numeric(sample.data[[i]]$exogenous_genomes$readCount_direct)
exprs.exogenousGenomes_cumulative[match(sample.data[[i]]$exogenous_genomes$ID, rownames(exprs.exogenousGenomes_cumulative)),i] = as.numeric(sample.data[[i]]$exogenous_genomes$readCount_inherited)
}
}
##
## Calculate the total number of mapped reads to the rRNA, genome, and exogenous sequences
##
mapping.stats[is.na(mapping.stats)] = 0
mapping.stats = as.data.frame(mapping.stats)
libSizes = list()
libSizes$input = mapping.stats[,colnames(mapping.stats) %in% c("input")]
libSizes$successfully_clipped = mapping.stats[,colnames(mapping.stats) %in% c("successfully_clipped")]
libSizes$reads_used_for_alignment = mapping.stats[,colnames(mapping.stats) %in% c("reads_used_for_alignment")]
libSizes$all = rowSums(mapping.stats[,colnames(mapping.stats) %in% c("rRNA","genome","miRNA_exogenous_sense"),drop=F])
libSizes$endogenous = rowSums(mapping.stats[,colnames(mapping.stats) %in% c("rRNA","genome"),drop=F])
libSizes$genome = mapping.stats[,colnames(mapping.stats) %in% "genome"]
libSizes$smRNA = mapping.stats[,grep("sense",colnames(mapping.stats))]
libSizes$miRNA = colSums(exprs.miRNA)
libSizes$exogenous_miRNA = colSums(exprs.exogenous_miRNA)
#libSizes$exogenous_rRNA = exprs.exogenousRibosomal_cumulative[rownames(exprs.exogenousRibosomal_cumulative)=="1",]
#libSizes$exogenous_genomes = exprs.exogenousGenomes_cumulative[rownames(exprs.exogenousGenomes_cumulative)=="1",]
##
## Save the raw count data
##
printMessage("Saving raw data to disk")
#save(exprs.miRNA, exprs.tRNA, exprs.piRNA, exprs.gencode, exprs.circRNA, exprs.exogenous_miRNA, exprs.exogenous_genomes, mapping.stats, libSizes, read.lengths, file=paste(output.dir, "exceRpt_smallRNAQuants_ReadCounts.RData", sep="/"))
save(exprs.miRNA, exprs.tRNA, exprs.piRNA, exprs.gencode, exprs.circRNA, exprs.exogenous_miRNA, exprs.exogenousRibosomal_specific, exprs.exogenousRibosomal_cumulative, taxonomyInfo.exogenous_rRNA, exprs.exogenousGenomes_specific, exprs.exogenousGenomes_cumulative, taxonomyInfo.exogenous_genomes, mapping.stats, qc.results, libSizes, read.lengths, run.duration, exprs.calibrator, file=paste(output.dir, "exceRpt_smallRNAQuants_ReadCounts.RData", sep="/"))
if(nrow(exprs.calibrator) > 0)
write.table(exprs.calibrator, file=paste(output.dir, "exceRpt_CALIBRATOR_ReadCounts.txt", sep="/"), sep="\t", col.names=NA, quote=F)
if(nrow(exprs.miRNA) > 0)
write.table(exprs.miRNA, file=paste(output.dir, "exceRpt_miRNA_ReadCounts.txt", sep="/"), sep="\t", col.names=NA, quote=F)
if(nrow(exprs.tRNA) > 0)
write.table(exprs.tRNA, file=paste(output.dir, "exceRpt_tRNA_ReadCounts.txt", sep="/"), sep="\t", col.names=NA, quote=F)
if(nrow(exprs.piRNA) > 0)
write.table(exprs.piRNA, file=paste(output.dir, "exceRpt_piRNA_ReadCounts.txt", sep="/"), sep="\t", col.names=NA, quote=F)
if(nrow(exprs.gencode) > 0)
write.table(exprs.gencode, file=paste(output.dir, "exceRpt_gencode_ReadCounts.txt", sep="/"), sep="\t", col.names=NA, quote=F)
if(nrow(exprs.circRNA) > 0)
write.table(exprs.circRNA, file=paste(output.dir, "exceRpt_circularRNA_ReadCounts.txt", sep="/"), sep="\t", col.names=NA, quote=F)
if(nrow(exprs.exogenous_miRNA) > 0)
write.table(exprs.exogenous_miRNA, file=paste(output.dir, "exceRpt_exogenous_miRNA_ReadCounts.txt", sep="/"), sep="\t", col.names=NA, quote=F)
if(!is.null(exprs.exogenousRibosomal_specific)){
if(nrow(exprs.exogenousRibosomal_specific) > 0){
tmp = cbind(taxonomyInfo.exogenous_rRNA[match(rownames(exprs.exogenousRibosomal_specific), taxonomyInfo.exogenous_rRNA$ID), ], exprs.exogenousRibosomal_specific)
write.table(tmp, file=paste(output.dir, "exceRpt_exogenousRibosomal_taxonomySpecific_ReadCounts.txt", sep="/"), sep="\t", row.names=F, quote=F)
tmp = cbind(taxonomyInfo.exogenous_rRNA[match(rownames(exprs.exogenousRibosomal_cumulative), taxonomyInfo.exogenous_rRNA$ID), ], exprs.exogenousRibosomal_cumulative)
write.table(tmp, file=paste(output.dir, "exceRpt_exogenousRibosomal_taxonomyCumulative_ReadCounts.txt", sep="/"), sep="\t", row.names=F, quote=F)
}
}
if(!is.null(exprs.exogenousGenomes_specific)){
if(nrow(exprs.exogenousGenomes_specific) > 0){
tmp = cbind(taxonomyInfo.exogenous_genomes[match(rownames(exprs.exogenousGenomes_specific), taxonomyInfo.exogenous_genomes$ID), ], exprs.exogenousGenomes_specific)
write.table(tmp, file=paste(output.dir, "exceRpt_exogenousGenomes_taxonomySpecific_ReadCounts.txt", sep="/"), sep="\t", row.names=F, quote=F)
tmp = cbind(taxonomyInfo.exogenous_genomes[match(rownames(exprs.exogenousGenomes_cumulative), taxonomyInfo.exogenous_genomes$ID), ], exprs.exogenousGenomes_cumulative)
write.table(tmp, file=paste(output.dir, "exceRpt_exogenousGenomes_taxonomyCumulative_ReadCounts.txt", sep="/"), sep="\t", row.names=F, quote=F)
}
}
write.table(read.lengths, file=paste(output.dir, "exceRpt_ReadLengths.txt", sep="/"), sep="\t", col.names=NA, quote=F)
##
## Keep a record of the adapter sequences for QC
##
adapterSeq = unlist(lapply(sample.data, function(l){ l$adapterSeq }))
write.table(as.data.frame(adapterSeq), file=paste(output.dir, "exceRpt_adapterSequences.txt", sep="/"), sep="\t", col.names=NA, quote=F)
##
## Write the numbers of reads mapping at each stage and the QC results
##
write.table(mapping.stats, file=paste(output.dir,"exceRpt_readMappingSummary.txt",sep="/"), sep="\t", col.names=NA, quote=F)
write.table(qc.results, file=paste(output.dir,"exceRpt_QCresults.txt",sep="/"), sep="\t", col.names=NA, quote=F)
##
## Calculate reads per million (RPM)
##
printMessage("Normalising to RPM")
#libSize.use = libSizes$all
#libSize.use = libSizes$miRNA
libSize.use = libSizes$genome
exprs.miRNA.rpm = t(10^6 * t(exprs.miRNA) / libSize.use)
exprs.tRNA.rpm = t(10^6 * t(exprs.tRNA) / libSize.use)
exprs.piRNA.rpm = t(10^6 * t(exprs.piRNA) / libSize.use)
exprs.gencode.rpm = t(10^6 * t(exprs.gencode) / libSize.use)
exprs.circRNA.rpm = t(10^6 * t(exprs.circRNA) / libSize.use)
exprs.exogenous_miRNA.rpm = t(10^6 * t(exprs.exogenous_miRNA) / libSizes$exogenous_miRNA)
exprs.exogenousRibosomal_specific.rpm = exprs.exogenousRibosomal_specific
exprs.exogenousRibosomal_cumulative.rpm = exprs.exogenousRibosomal_cumulative
if(!is.null(exprs.exogenousRibosomal_specific)){
if(nrow(exprs.exogenousRibosomal_specific) > 0){
exprs.exogenousRibosomal_specific.rpm = t(10^6 * t(exprs.exogenousRibosomal_specific) / libSizes$exogenous_rRNA)
exprs.exogenousRibosomal_cumulative.rpm = t(10^6 * t(exprs.exogenousRibosomal_cumulative) / libSizes$exogenous_rRNA)
}
}
exprs.exogenousGenomes_specific.rpm = exprs.exogenousGenomes_specific
exprs.exogenousGenomes_cumulative.rpm = exprs.exogenousGenomes_cumulative
if(!is.null(exprs.exogenousGenomes_specific)){
if(nrow(exprs.exogenousGenomes_specific) > 0){
exprs.exogenousGenomes_specific.rpm = t(10^6 * t(exprs.exogenousGenomes_specific) / libSizes$exogenous_genomes)
exprs.exogenousGenomes_cumulative.rpm = t(10^6 * t(exprs.exogenousGenomes_cumulative) / libSizes$exogenous_genomes)
}
}
##
## Save the RPM normalised data
##
printMessage("Saving normalised data to disk")
save(exprs.miRNA.rpm, exprs.tRNA.rpm, exprs.piRNA.rpm, exprs.gencode.rpm, exprs.circRNA.rpm, exprs.exogenous_miRNA.rpm, exprs.exogenousRibosomal_specific.rpm, exprs.exogenousRibosomal_cumulative.rpm, exprs.exogenousGenomes_specific.rpm, exprs.exogenousGenomes_cumulative.rpm, file=paste(output.dir, "exceRpt_smallRNAQuants_ReadsPerMillion.RData", sep="/"))
if(nrow(exprs.miRNA.rpm) > 0)
write.table(exprs.miRNA.rpm, file=paste(output.dir, "exceRpt_miRNA_ReadsPerMillion.txt", sep="/"), sep="\t", col.names=NA, quote=F)
if(nrow(exprs.tRNA.rpm) > 0)
write.table(exprs.tRNA.rpm, file=paste(output.dir, "exceRpt_tRNA_ReadsPerMillion.txt", sep="/"), sep="\t", col.names=NA, quote=F)
if(nrow(exprs.piRNA.rpm) > 0)
write.table(exprs.piRNA.rpm, file=paste(output.dir, "exceRpt_piRNA_ReadsPerMillion.txt", sep="/"), sep="\t", col.names=NA, quote=F)
if(nrow(exprs.gencode.rpm) > 0)
write.table(exprs.gencode.rpm, file=paste(output.dir, "exceRpt_gencode_ReadsPerMillion.txt", sep="/"), sep="\t", col.names=NA, quote=F)
if(nrow(exprs.circRNA.rpm) > 0)
write.table(exprs.circRNA.rpm, file=paste(output.dir, "exceRpt_circularRNA_ReadsPerMillion.txt", sep="/"), sep="\t", col.names=NA, quote=F)
if(nrow(exprs.exogenous_miRNA.rpm) > 0)
write.table(exprs.exogenous_miRNA.rpm, file=paste(output.dir, "exceRpt_exogenous_miRNA_ReadsPerMillion.txt", sep="/"), sep="\t", col.names=NA, quote=F)
if(!is.null(exprs.exogenousRibosomal_specific)){
if(nrow(exprs.exogenousRibosomal_specific) > 0){
tmp = cbind(taxonomyInfo.exogenous_rRNA[match(rownames(exprs.exogenousRibosomal_specific.rpm), taxonomyInfo.exogenous_rRNA$ID), ], exprs.exogenousRibosomal_specific.rpm)
write.table(tmp, file=paste(output.dir, "exceRpt_exogenousRibosomal_taxonomySpecific_ReadsPerMillion.txt", sep="/"), sep="\t", row.names=F, quote=F)
tmp = cbind(taxonomyInfo.exogenous_rRNA[match(rownames(exprs.exogenousRibosomal_cumulative.rpm), taxonomyInfo.exogenous_rRNA$ID), ], exprs.exogenousRibosomal_cumulative.rpm)
write.table(tmp, file=paste(output.dir, "exceRpt_exogenousRibosomal_taxonomyCumulative_ReadsPerMillion.txt", sep="/"), sep="\t", row.names=F, quote=F)
}
}
if(!is.null(exprs.exogenousGenomes_specific)){
if(nrow(exprs.exogenousGenomes_specific) > 0){
tmp = cbind(taxonomyInfo.exogenous_genomes[match(rownames(exprs.exogenousGenomes_specific.rpm), taxonomyInfo.exogenous_genomes$ID), ], exprs.exogenousGenomes_specific.rpm)
write.table(tmp, file=paste(output.dir, "exceRpt_exogenousGenomes_taxonomySpecific_ReadsPerMillion.txt", sep="/"), sep="\t", row.names=F, quote=F)
tmp = cbind(taxonomyInfo.exogenous_genomes[match(rownames(exprs.exogenousGenomes_cumulative.rpm), taxonomyInfo.exogenous_genomes$ID), ], exprs.exogenousGenomes_cumulative.rpm)
write.table(tmp, file=paste(output.dir, "exceRpt_exogenousGenomes_taxonomyCumulative_ReadsPerMillion.txt", sep="/"), sep="\t", row.names=F, quote=F)
}
}
return(rownames(mapping.stats))
}
##
##
##
PlotData = function(sampleIDs, output.dir, sampleGroups=NA, minPercent_exogenousRibosomal=0.5, minPercent_exogenousGenomes=0.5){
load(paste(output.dir, "exceRpt_smallRNAQuants_ReadCounts.RData", sep="/"))
load(paste(output.dir, "exceRpt_smallRNAQuants_ReadsPerMillion.RData", sep="/"))
##
## Order samples based on similarity of mapping statistics
##
sampleOrder = 1
if(nrow(mapping.stats) > 1){
h = hclust(dist(1-cor(t(mapping.stats))))
sampleOrder = h$order
}
##
## Open PDF for diagnostic plots
##
printMessage("Creating QC plots")
pdf(paste(output.dir,"exceRpt_DiagnosticPlots.pdf",sep="/"), height=10, width=20)
#tiff(paste(output.dir,"DiagnosticPlots.tiff",sep="/"))
if(ncol(read.lengths) > 1){
printMessage("Plotting read-length distributions")
##
## plot distribution of clipped read lengths - read count
##
tmp = melt(read.lengths); colnames(tmp) = c("sample","length","count")
if(is.data.frame(sampleGroups)){ tmp$sampleGroup = sampleGroups[match(tmp$sample, sampleGroups$sampleID), 2] }
maxX = min(c(100,max(tmp$length)))
p = ggplot(tmp, aes(x=length, y=count, colour=sample)) +geom_line(alpha=0.75) +xlab("read length (nt)") +ylab("# reads") +ggtitle("read-length distributions: raw read count") +scale_x_continuous(limits=c(15,maxX), minor_breaks=1:maxX, breaks=seq(15,maxX,by=5))
if(nrow(read.lengths) > 30){ p = p +guides(colour=FALSE) }
if(is.data.frame(sampleGroups)){ p = p +facet_wrap(~sampleGroup,ncol=1)}
print(p)
#ggplot(tmp, aes(x=as.factor(length), y=count)) +geom_violin()
#ggplot(tmp, aes(x=as.factor(length), y=count)) +geom_boxplot()
##
## plot distribution of clipped read lengths - fraction
##
tmp = melt(t(apply(read.lengths, 1, function(row){ row/sum(row) }))); colnames(tmp) = c("sample","length","fraction")
if(is.data.frame(sampleGroups)){ tmp$sampleGroup = sampleGroups[match(tmp$sample, sampleGroups$sampleID), 2] }
maxX = min(c(100,max(tmp$length)))
p = ggplot(tmp, aes(x=length, y=fraction, colour=sample)) +geom_line(alpha=0.75) +xlab("read length (nt)") +ylab("fraction of reads") +ggtitle("read-length distributions: normalised read fraction") +scale_x_continuous(limits=c(15,maxX), minor_breaks=1:maxX, breaks=seq(15,maxX,by=5))
if(nrow(read.lengths) > 30){ p = p +guides(colour=FALSE) }
if(is.data.frame(sampleGroups)){ p = p +facet_wrap(~sampleGroup,ncol=1)}
print(p)
}
##
## Plot run duration of each sample
##
if(F){
printMessage("Plotting run-duration")
tmp=melt(as.matrix(run.duration))
colnames(tmp) = c("sampleID","stuff","runDuration_seconds")
tmp = cbind(tmp, category=.bincode(tmp[,3], breaks=c(0,as.numeric(quantile(tmp[,3],probs=c(0.10,0.90,1))))))
tmp = cbind(tmp, colour=tmp$category)
tmp = cbind(tmp, inputReadCount=mapping.stats$input)
tmp$category[tmp$category == 1] = "fast"
tmp$category[tmp$category == 2] = "normal"
tmp$category[tmp$category == 3] = "slow"
tmp$colour[tmp$colour == 1] = "red"
tmp$colour[tmp$colour == 2] = "green"
tmp$colour[tmp$colour == 3] = "blue"
tmp$runDuration_minutes = tmp$runDuration_seconds/60
tmp$runDuration_hours = tmp$runDuration_minutes/60
p = ggplot(tmp, aes(x=sampleID,y=runDuration_hours,fill=colour)) +
geom_bar(stat="identity") +facet_grid(~category,scales="free_x",space="free_x") +
guides(fill=FALSE) +theme(axis.text.x=element_text(angle=60, hjust=1.0, vjust=1)) +
ggtitle("Duration of exceRpt run for each sample") +ylab("Run duration (hours)")
print(p)
if(is.data.frame(sampleGroups)){ tmp$sampleGroup = sampleGroups[match(tmp$sampleID, sampleGroups$sampleID), 2] }
p = ggplot(tmp, aes(x=inputReadCount,y=runDuration_hours,colour=colour)) +geom_point(size=5) +
guides(colour=FALSE) +
scale_y_log10(limits=c(0.1,10^ceiling(log10(max(tmp$runDuration_hours)))), breaks=c(0.1,1,10^seq(0:ceiling(log10(max(tmp$runDuration_hours)))))) +
scale_x_log10(limits=c(min(c(100000,10^floor(log10(min(tmp$inputReadCount+1))))),10^ceiling(log10(max(tmp$inputReadCount)))), breaks=10^seq(min(c(100000,floor(log10(min(tmp$inputReadCount+1))))),ceiling(log10(max(tmp$inputReadCount))))) +
ggtitle("Duration of exceRpt run per sequencing yield") +
ylab("Run duration (hours)") +xlab("Total number of reads input")
#if(is.data.frame(sampleGroups)){ p = p +facet_wrap(~sampleGroup,ncol=1)}
print(p)
}
##
## plot distribution of # mapped reads per sample
##
printMessage("Plotting # mapped reads")
tmp = log10(libSizes$all)
hist(tmp, breaks=seq(0,ceiling(max(tmp)), by=0.1), col="grey", border="white", xlab="log10(# mapped reads)", main="Library size (all mapped reads)", ylab="# samples")
##
## Plot the rRNA contamination
##
#par(mfrow=c(1,2))
#hist((mapping.stats$UniVec_contaminants / libSizes$all), breaks=seq(0,1,by=0.05), col="grey", border="white", xlim=c(0,1), main="UniVec contaminant signal",xlab="fraction contaminant reads",ylab="# samples")
#hist((mapping.stats$rRNA / libSizes$all), breaks=seq(0,1,by=0.05), col="grey", border="white", xlim=c(0,1), main="rRNA signal",xlab="fraction rRNA reads",ylab="# samples")
mapping.stats.orig = mapping.stats
mapping.stats = mapping.stats[,-grep("input_to_",colnames(mapping.stats))]
## remove the exogenous stuff from the stats if this wasn't used in the run
if(sum(mapping.stats[,22:26]) == 0)
mapping.stats = mapping.stats[, -c(22:26)]
##
## Plot heatmap of mapping percentages through the pipeline
##
printMessage("Plotting mapping stats heatmap (1/3)")
toplot = melt(as.matrix(mapping.stats / mapping.stats$input)); colnames(toplot) = c("Sample","Stage","ReadFraction")
toplot$Stage = with(toplot, factor(Stage, levels = rev(levels(Stage))))
toplot$Sample = factor(as.character(toplot$Sample), levels=rownames(mapping.stats)[sampleOrder])
if(is.data.frame(sampleGroups)){ toplot$sampleGroup = sampleGroups[match(toplot$Sample, sampleGroups$sampleID), 2] }
p = ggplot(toplot, aes(x=Sample, y=Stage, group=Sample, fill=ReadFraction, label=sprintf("%1.1f%%",ReadFraction*100))) +geom_tile() +scale_fill_gradient2(low="white",high="yellow",mid="steelblue", midpoint=0.5) +theme(axis.text.x=element_text(angle=40, hjust=1.0, vjust=1)) +ggtitle("fraction aligned reads (normalised by # input reads)")
if(nrow(mapping.stats) < 50){ p = p +geom_text(size=3) }
if(is.data.frame(sampleGroups)){ p = p +facet_grid(~sampleGroup, scales="free_x",space="free_x")}
print(p)
##
## Plot heatmap of mapping percentages through the pipeline
##
printMessage("Plotting mapping stats heatmap (2/3)")
if(max(mapping.stats$successfully_clipped) > 0){
tmp = mapping.stats
i.toFix = which(tmp$successfully_clipped == 0)
if(length(i.toFix) > 0)
tmp$successfully_clipped[i.toFix] = tmp$input[i.toFix]
toplot = melt(as.matrix(tmp / tmp$successfully_clipped)[,-1,drop=F]); colnames(toplot) = c("Sample","Stage","ReadFraction")
toplot$Stage = with(toplot, factor(Stage, levels = rev(levels(Stage))))
toplot$Sample = factor(as.character(toplot$Sample), levels=rownames(mapping.stats)[sampleOrder])