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plotExogenous_functions.R
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plotExogenous_functions.R
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##########################################################################################
## ##
## Functions to combine pipeline runs for individual samples into something more useful ##
## ##
## Author: Rob Kitchen (r.r.kitchen@gmail.com) ##
## ##
## Version 4.6.3 (2016-10-08) ##
## ##
##########################################################################################
##
## check dependencies
##
baseURL = "https://cran.r-project.org"
if(!"Rgraphviz" %in% rownames(installed.packages())) { source("http://bioconductor.org/biocLite.R"); biocLite("Rgraphviz",ask=F) }
if(!"scales" %in% rownames(installed.packages())) { install.packages("scales",repos=baseURL) }
require(Rgraphviz)
require(scales)
##
## Find the relevant sample(s) under the specified path
##
## - TODO: recurse through successive directories?
##
SearchForSampleData = function(base.dir, directory=""){
to.return = NULL
dir.use = paste(base.dir, directory, sep = "/")
subdirs = dir(dir.use)
if("ExogenousRibosomalAlignmentResults.txt" %in% subdirs)
to.return = paste(dir.use,"ExogenousRibosomalAlignmentResults.txt",sep="")
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)
}
##
## Plot exogenous genomes
##
plotExogenousTaxonomyTrees = function(counts, cumcounts, what, output.dir, taxonomyInfo, fontScale=2, sampleGroups=NA, minPercent=0.5){
## 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] })
## 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()
}
}