#' runCPH: runs cox proportional hazard models against patients with top and bottom n percent expression values
#'
#' The runCPH function will process three separate file one with sample ids one with survival data and one with
#' expression values. In test mode if a list of sample ids are supplied but not the other two files it assumes the
#' ids are from the TCGA SKCM data set and uses a cached version of the normalised expression values from the TCGA
#' SKCM data set and a currated set of survival data.
#'
#' It will also perform a limma differential gene expression test for the significance of the fold change between the
#' top and bottom n percent of expression values
#'
#' @param sampleDataFile name of the a delimited sample data file, first column should contain the
#' sample id's for samples to match in survival and expression data
#' @param survivalDataFile name of the delimited survival data file, first column should contain the
#' sample ids to match with sample data and expression data. If this file is not specified uses
#' default data created for TCGA SKCM samples
#' @param expressionDataFile name of the delimited expression data file, second row should contain the
#' sample ids as column headings to match with sample data and expression data. If this file is not specified uses
#' default data created for TCGA SKCM samples normalised expression values. NB when this file is read in
#' the first line will be skipped.
#' @param percExpr filter for minimum percentage of samples required to be expression a gene
#' to select genes for testing
#' @param exprRange top and bottom quantiles of expression values to select samples for testing
#' @param sep default separator for delimited survival and sample id files supplied
#' @param exprSep default separator for expression file supplied
#' @param outputDir location to write output files
#' @param analysisName stub to add to output file names
#' @param silent boolean return the full set of statistics as a data frame
#'
#' @return the function creates several csv output files with the Cox PH results, the Limma DGE results, a combined set
#' of both results and a set of those genes for which the DGE result is significant at 0.05 FDR
#'
#'
#' @export
runCPH <- function(exprRange=0.33,percExpr=0.8,sep=";",exprSep=sep,outputDir=getwd(),silent=F,
survData=NULL, expressionData=NULL,simple=T,
analysisName=paste("run",gsub("([ ]|[:])","_",format(Sys.time()))),scan=T,
geneList=NULL){
# check files
if(is.null(survData)){
stop("Sample data file not supplied using samples from survival data file")
}
if(is.null(expressionData)){
stop("Sample data file not supplied using samples from survival data file")
}
# Filter by samples in sample data just incase
exprData <- expressionData[,which(colnames(expressionData) %in% rownames(survData))]
exprSamples <- colnames(exprData)
survSamples <- rownames(survData)
# Sanity check the sample ids are the same
if(!identical(unique(sort(exprSamples)), sort(survSamples))){
stop(paste0("The samples in survival data do not match those in the expression and ",
"survival data files"))
}
if(!identical(sort(exprSamples), sort(survSamples))){
warning(paste0("The samples in survival potentially have more than one expression",
" sample"))
}
# perform the analysis
mcParam <- BiocParallel::MulticoreParam()
# filter out genes not expressed in percentage of samples
exprData <- as.matrix(exprData)
if(is.null(geneList)){
fGenes <- which(rowSums(apply(exprData,2,">",0))/ncol(exprData) > percExpr)
exprGenes <- rownames(exprData)[fGenes]
}else{
exprGenes <- geneList[which(geneList%in%rownames(exprData))]
}
# run cox proportional hazard on all expressed genes
resList <- BiocParallel::bplapply(exprGenes,function(geneid){
# get Hi samples expression >= n centile
hlim <- as.numeric(quantile(exprData[geneid,],1-exprRange))
hi <- survData[unique(substr(names(which(exprData[geneid,]>=hlim)),1,12)),]
hi$Class <- "high"
# get Lo samples expression <= 1-n centile
llim <- as.numeric(quantile(exprData[geneid,],exprRange))
lo <- survData[unique(substr(names(which(exprData[geneid,]<=llim)),1,12)),]
lo$Class <- "low"
# make survival data frame
survData2 <- rbind(hi,lo)
# set expression class
survData2$Class <- as.factor(survData2$Class)
# get max of either days to death or days to last follow up for time
# Censor the time data to generate survival data
survData2$survival <- survival::Surv(survData2$time,as.numeric(survData2$status))
# fit cox proportional hazards (non-parametric) survival model
coxfit <- survival::coxph(survival~Class,data=survData2)
fitdiff <- survival::survdiff(survival~Class,data=survData2)
hiExpr <- unname(exprData[geneid,which(exprData[geneid,]>=hlim)])
# the next line is fudge to fix an issue if all the low expression are zero
loExpr <- unname(exprData[geneid,which(exprData[geneid,]<=llim)])[1:length(hiExpr)]
list(surv=c(summary(coxfit)$conf.int,summary(coxfit)$sctest["pvalue"]),
dif=fitdiff, expr=c(lo=loExpr,hi=hiExpr))
}, BPPARAM=mcParam)
names(resList) <- exprGenes
if(simple){
return(resList)
}else{
# convert results to data frame
resSurv<- plyr::ldply(lapply(resList,"[[","surv"))
rownames(resSurv) <- resSurv[,1]
resSurv <- resSurv[,-1]
colnames(resSurv) <- c("HR","1/HR","lower95CI","upper95CI","pvalue")
# perform differential expression check using limma
# between high and low samples for all expressed genes
resExpr<- plyr::ldply(lapply(resList,"[[","expr"))
rownames(resExpr) <- resExpr[,1]
resExpr <- resExpr[,-1]
design <- model.matrix(~as.factor(substr(colnames(resExpr),1,2)))
dgeExpr <- edgeR::DGEList(resExpr,group=substr(colnames(resExpr),1,2))
dgeExpr <- edgeR::calcNormFactors(dgeExpr)
logCPM <- edgeR::cpm(dgeExpr,log=T,prior.count=2)
fit <- limma::lmFit(logCPM,design)
fit <- limma::eBayes(fit)
tt <- limma::topTable(fit,n=2e5)
# combine HR and DGE analysis
res <- merge(tt,resSurv,by="row.names")
# calculate corrected p.values for only those genes with significant DGE
resHiCon <- res[which(res$adj.P.Val<=0.05),]
resHiCon$HR.adjpvalue <- p.adjust(resHiCon$pvalue)
return(resHiCon)
}
}
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