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Integrated DPC native and triplet pipeline density estimation functions #31

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Integrated DPC native and triplet pipeline density estimation functions #31

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c51c8df
Integrated DPC native and triplet pipeline density estimation functions
sdentro Oct 5, 2024
d6fc65d
Adding code from triplet pipeline that wasnt previously in the package
sdentro Oct 10, 2024
5b204d0
new file with functions that contain code from previous scripts
sdentro Oct 10, 2024
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5 changes: 5 additions & 0 deletions NAMESPACE
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Original file line number Diff line number Diff line change
Expand Up @@ -46,3 +46,8 @@ importFrom(utils,read.csv)
importFrom(utils,read.table)
importFrom(utils,write.csv)
importFrom(utils,write.table)
export(determine_num_triplets)
export(get.subsample.indices)
export(combine_triplet_runs)
export(run_dpc_core_triplet)
export(run_parallel_density_est)
55 changes: 45 additions & 10 deletions R/AssignMutations.R
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Expand Up @@ -373,9 +373,10 @@ mutation_assignment_em = function(GS.data, mutCount, WTCount, subclonal.fraction
#' @param GS.data Output from the MCMC chain containing mutation assignments, cluster positions and cluster weights
#' @param density.smooth Parameter that determines the amount of smoothing applied when establishing multi-dimensional density
#' @param opts List with parameters, including donorname (samplename), individual samplenames (subsamples) and iterations and burnin
#' @param outfilesuffix Addition to the output filenames, this is useful when running this function on various subsets of the data. Leave at "" when not doing multiple runs
#' @return List with various standardised clustering results, including cluster positions, assignments and probabilities
#' @author dw9, sd11
multiDimensionalClustering = function(mutation.copy.number, copyNumberAdjustment, GS.data, density.smooth, opts) {
multiDimensionalClustering = function(mutation.copy.number, copyNumberAdjustment, GS.data, density.smooth, opts, outfilesuffix="") {
#
# Uses clustering in multi dimensions to obtain a likelihood across all iterations for each mutation
# The cluster where a mutation is assigned most often is deemed the most likeli destination.
Expand Down Expand Up @@ -456,8 +457,18 @@ multiDimensionalClustering = function(mutation.copy.number, copyNumberAdjustment
localMins = localMins + hypercube.size
localOptima = array(rep(range[,1],each=no.subsamples),dim(localMins))
localOptima = localOptima + array(rep((range[,2] - range[,1])/(gridsize-1),each=no.subsamples),dim(localMins)) * localMins
write.table(cbind(localOptima,above95confidence),paste(new_output_folder,"/",samplename,"_localMultidimensionalOptima_",density.smooth,".txt",sep=""),quote=F,sep="\t",row.names=F,col.names = c(paste(samplename,subsamples,sep=""),"above95percentConfidence"))
write.table(localOptima[above95confidence,,drop=F],paste(new_output_folder,"/",samplename,"_localHighConfidenceMultidimensionalOptima_",density.smooth,".txt",sep=""),quote=F,sep="\t",row.names=F,col.names = paste(samplename,subsamples,sep=""))
write.table(cbind(localOptima,above95confidence),
paste(new_output_folder,"/",samplename,outfilesuffix,"_localMultidimensionalOptima_",density.smooth,".txt",sep=""),
quote=F,
sep="\t",
row.names=F,
col.names = c(paste(samplename,subsamples,sep=""),"above95percentConfidence"))
write.table(localOptima[above95confidence,,drop=F],
paste(new_output_folder,"/",samplename,outfilesuffix,"_localHighConfidenceMultidimensionalOptima_",density.smooth,".txt",sep=""),
quote=F,
sep="\t",
row.names=F,
col.names = paste(samplename,subsamples,sep=""))

no.optima = nrow(localOptima)

Expand Down Expand Up @@ -586,7 +597,11 @@ multiDimensionalClustering = function(mutation.copy.number, copyNumberAdjustment
for(i in 1:no.optima){
sampled.thetas[[i]] = array(NA,c(length(sampledIters),totals[i],no.subsamples))
for(s in 1:length(sampledIters)){
sampled.thetas[[i]][s,,] = GS.data$pi.h[sampledIters[s],S.i[sampledIters[s],most.likely.cluster==i],]
if(sum(most.likely.cluster==i)>1) {
sampled.thetas[[i]][s,,] = GS.data$pi.h[sampledIters[s],S.i[sampledIters[s],most.likely.cluster==i],]
} else {
sampled.thetas[[i]][s,,] = array(GS.data$pi.h[sampledIters[s],S.i[sampledIters[s],most.likely.cluster==i],],c(totals[i],no.subsamples))
}
}
for(s in 1:no.subsamples){
median.sampled.vals = sapply(1:length(sampledIters),function(x){median(sampled.thetas[[i]][x,,s])})
Expand All @@ -599,18 +614,20 @@ multiDimensionalClustering = function(mutation.copy.number, copyNumberAdjustment
#out = cbind(data[[1]][,1:2],mutation.preferences,most.likely.cluster)
#names(out)[(ncol(out)-no.optima):ncol(out)] = c(paste("prob.cluster",1:no.optima,sep=""),"most.likely.cluster")
out = cbind(mutation.preferences,most.likely.cluster)
names(out) = c(paste("prob.cluster",1:no.optima,sep=""),"most.likely.cluster")
colnames(out) = c(paste("prob.cluster",1:no.optima,sep=""),"most.likely.cluster")

cluster.locations = cbind(1:ncol(mutation.preferences), quantiles[,,2], colSums(mutation.preferences), table(factor(most.likely.cluster,levels = 1:no.optima)))
write.table(cluster.locations,
paste(new_output_folder,"/",samplename,"_optimaInfo_",density.smooth,".txt",sep=""),
paste(new_output_folder,"/",samplename,outfilesuffix,"_optimaInfo_",density.smooth,".txt",sep=""),
col.names = c("cluster.no", paste(samplename, subsamples, sep=""), "estimated.no.of.mutations", "no.of.mutations.assigned"),
row.names=F,
sep="\t",
quote=F)

write.table(out,paste(new_output_folder,"/",samplename,"_DP_and_cluster_info_",density.smooth,".txt",sep=""),sep="\t",row.names=F,quote=F)
write.table(CIs,paste(new_output_folder,"/",samplename,"_confInts_",density.smooth,".txt",sep=""),col.names = paste(rep(paste(samplename,subsamples,sep=""),each=2),rep(c(".lower.CI",".upper.CI"),no.subsamples),sep=""),row.names=F,sep="\t",quote=F)
write.table(out,paste(new_output_folder,"/",samplename,outfilesuffix,"_DP_and_cluster_info_",density.smooth,".txt",sep=""),sep="\t",row.names=F,quote=F)
write.table(CIs,paste(new_output_folder,"/",samplename,outfilesuffix,"_confInts_",density.smooth,".txt",sep=""),
col.names = paste(rep(paste(samplename,subsamples,sep=""),each=2),rep(c(".lower.CI",".upper.CI"),no.subsamples),sep=""),
row.names=F,sep="\t",quote=F)
}else{
most.likely.cluster = rep(1,no.muts)
cluster.locations = matrix(NA, nrow=1, ncol=length(subsamples)+3)
Expand All @@ -620,7 +637,25 @@ multiDimensionalClustering = function(mutation.copy.number, copyNumberAdjustment
mutation.preferences = data.frame(rep(1, no.muts))
#cluster.locations = as.data.frame(cluster.locations)
#colnames(cluster.locations) = c("cluster.no", paste(samplename, subsamples, sep=""), "estimated.no.of.mutations", "no.of.mutations.assigned")
warning("No local optima found")
write.table(cluster.locations,
paste(new_output_folder,"/",samplename,outfilesuffix,"_optimaInfo_",density.smooth,".txt",sep=""),
col.names = c("cluster.no", paste(samplename, subsamples, sep=""), "estimated.no.of.mutations", "no.of.mutations.assigned"),
row.names=F,
sep="\t",
quote=F)
#out = cbind(data[[1]][,1:2], data.frame(rep(1, no.muts)), data.frame(rep(1, no.muts)))
out = cbind(data.frame(rep(1, no.muts)), data.frame(rep(1, no.muts)))
no.optima = 1
#names(out)[(ncol(out)-no.optima):ncol(out)] = c(paste("prob.cluster",1:no.optima,sep=""),"most.likely.cluster")
colnames(out) = c(paste("prob.cluster",1:no.optima,sep=""),"most.likely.cluster")
write.table(out,paste(new_output_folder,"/",samplename,outfilesuffix,"_DP_and_cluster_info_",density.smooth,".txt",sep=""),sep="\t",row.names=F,quote=F)

CIs = as.data.frame(array(localOptima[1], c(no.optima,no.subsamples*2)))
CIs = CIs[,rep(1:no.subsamples,each=2) + rep(c(0,no.subsamples),no.subsamples)]
write.table(CIs,
paste(new_output_folder,"/",samplename,outfilesuffix,"_confInts_",density.smooth,".txt",sep=""),
col.names = paste(rep(paste(samplename,subsamples,sep=""),each=2),rep(c(".lower.CI",".upper.CI"),no.subsamples),sep=""),
row.names=F,sep="\t",quote=F)
}

# Remove the estimated number of SNVs per cluster from the cluster locations table
Expand All @@ -644,7 +679,7 @@ multiDimensionalClustering = function(mutation.copy.number, copyNumberAdjustment
assignment.likelihood = sapply(1:no.muts,function(m,c,i){ m[i,c[i]] },m=mutation.preferences, c=most.likely.cluster)

no.optima = nrow(cluster.locations)
pdf(paste(new_output_folder,"/",samplename,"_most_likely_cluster_assignment_",density.smooth,".pdf",sep=""),height=4,width=4)
pdf(paste(new_output_folder,"/",samplename,outfilesuffix,"_most_likely_cluster_assignment_",density.smooth,".pdf",sep=""),height=4,width=4)
#its hard to distinguish more than 8 different colours
max.cols = 8
cols = rainbow(min(max.cols,no.optima))
Expand Down
2 changes: 1 addition & 1 deletion R/DensityEstimator.R
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Expand Up @@ -106,7 +106,7 @@ Gibbs.subclone.density.est <- function(burden, GS.data, pngFile, density.smooth

V.h.cols <- GS.data$V.h
if("pi.h" %in% names(GS.data)){
if(is.na(indices)){
if(any(is.na(indices))){
pi.h.cols <- GS.data$pi.h
}else{
pi.h.cols <- GS.data$pi.h[,,indices]
Expand Down
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