R/run.edgeR.r

In sarangian/deaRscripts: Differential Expression Analysis based on the read count data generated by either of Salmon, Kallisto, featureCounts and perform statistical analysis to discover quantitative changes in expression levels between two different experimental groups

library('edgeR')
#' Wrapper to run edgeR
#'
#' Wrapper to run edgeR: create the \code{dge} object, normalize data, estimate dispersions, statistical testing...
#'
#' @param counts \code{matrix} of counts
#' @param target target \code{data.frame} of the project
#' @param varInt name of the factor of interest (biological condition)
#' @param condRef reference biological condition
#' @param batch batch effect to take into account (\code{NULL} by default)
#' @param cpmCutoff counts-per-million cut-off to filter low counts
#' @param normalizationMethod normalization method: \code{"TMM"} (default), \code{"RLE"} (DESeq) or \code{"upperquartile"}
#' @param pAdjustMethod p-value adjustment method: \code{"BH"} (default) or \code{"BY"}
#' @param ... optional arguments to be passed to \code{glmFit()}
#' @return A list containing the \code{dge} object and the \code{results} object
#' @author Hugo Varet

run.edgeR <- function(counts, target, varInt, condRef, batch=NULL, cpmCutoff=1, 
                      normalizationMethod="TMM", pAdjustMethod="BH", ...){
  
  # filtering: select features which contain at least 
  # minReplicates (smallest number of replicates) with
  # at least cpmCutoff counts per million
  minReplicates <- min(table(target[,varInt]))
  print (minReplicates)
  
  fcounts <- counts[rowSums(cpm(counts) >= cpmCutoff) >= minReplicates,]
  cat("Number of features discarded by the filtering:\n")
  cat(nrow(counts)-nrow(fcounts),"\n")
  
  print (varInt)
  # building dge object
  design <- formula(paste("~", ifelse(!is.null(batch), paste(batch,"+"), ""), varInt))

  #design <- model.matrix(~ conditions)
  dge <- DGEList(counts=fcounts, group=target[,varInt])
  dge$design <- model.matrix(design, data=target)

  print (dge$design)
  cat("\nDesign of the statistical model:\n")
  cat(paste(as.character(design),collapse=" "),"\n")					  
  
  # normalization
  dge <- calcNormFactors(dge, method=normalizationMethod)
  cat("\nNormalization factors:\n")
  print(dge$samples$norm.factors)

  # estimating dispersions
  dge <- estimateGLMCommonDisp(dge, dge$design)
  dge <- estimateGLMTrendedDisp(dge, dge$design)
  dge <- estimateGLMTagwiseDisp(dge, dge$design)

  # statistical testing: perform all the comparisons between the levels of varInt
  fit <- glmFit(dge, dge$design, ...)
  cat(paste("Coefficients of the model:",paste(colnames(fit$design),collapse="  ")),"\n")
  colsToTest <- grep(varInt,colnames(fit$design))
  namesToTest <- paste0(gsub(varInt,"",colnames(fit$design)[colsToTest]),"_vs_",condRef)
  results <- list()
  # testing coefficients individually (tests againts the reference level)
  for (i in 1:length(colsToTest)){
    cat(paste0("Comparison ",gsub("_"," ",namesToTest[i]),": testing coefficient ",colnames(fit$design)[colsToTest[i]]),"\n")
    lrt <- glmLRT(fit, coef=colsToTest[i])
    results[[namesToTest[i]]] <- topTags(lrt,n=nrow(dge$counts),adjust.method=pAdjustMethod,sort.by="none")$table
  }
  # defining contrasts for the other comparisons (if applicable)
  if (length(colsToTest)>=2){
    colnames <- gsub(varInt,"",colnames(fit$design))
    for (comp in combn(length(colsToTest),2,simplify=FALSE)){ 
  	contrast <- numeric(ncol(dge$design))
  	contrast[colsToTest[comp[1:2]]] <- c(-1,1)
  	namecomp <- paste0(colnames[colsToTest[comp[2]]],"_vs_",colnames[colsToTest[comp[1]]])
  	cat(paste0("Comparison ",gsub("_"," ",namecomp),": testing contrast (",paste(contrast,collapse=", "),")"),"\n")
      lrt <- glmLRT(fit, contrast=contrast)
      results[[namecomp]] <- topTags(lrt,n=nrow(dge$counts),adjust.method=pAdjustMethod,sort.by="none")$table
    }
  }
  
  return(list(dge=dge,results=results,lrt=lrt))
}