R/run.DESeq2_Salmon.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

#' Wrapper to run DESeq2
#'
#' Wrapper to run DESeq2: create the \code{DESeqDataSet}, normalize data, estimate dispersions, statistical testing...
#'
#' @param counts \code{matrix} of raw counts
#' @param target target \code{data.frame} of the project
#' @param varInt name of the factor of interest (biological condition)
#' @param batch batch effect to take into account (\code{NULL} by default)
#' @param locfunc \code{"median"} (default) or \code{"shorth"} to estimate the size factors
#' @param fitType mean-variance relationship: "parametric" (default) or "local"
#' @param pAdjustMethod p-value adjustment method: \code{"BH"} (default) or \code{"BY"} for instance
#' @param independentFiltering \code{TRUE} or \code{FALSE} to perform the independent filtering or not
#' @param alpha significance threshold to apply to the adjusted p-values
#' @param ... optional arguments to be passed to \code{nbinomWaldTest()}
#' @return A list containing the \code{dds} object (\code{DESeqDataSet} class), the \code{results} objects (\code{DESeqResults} class) and the vector of size factors
#' @author Aditya Narayan Sarangi
#library(tximport)


run.DESeq2_trans <- function(counts, target, varInt, batch=NULL,
                       locfunc="median", fitType="parametric", pAdjustMethod="BH",alpha=0.05, ...){
  # building dds object
  dds <- DESeqDataSetFromTximport(txi=counts, colData=target, 
                                design=formula(paste("~", ifelse(!is.null(batch), paste(batch,"+"), ""), varInt)))
  cat("Design of the statistical model:\n")
  cat(paste(as.character(design(dds)),collapse=" "),"\n")           
  
  # normalization
  dds <- estimateSizeFactors(dds,locfunc=eval(as.name(locfunc)))
  cat("\nNormalization factors:\n")
  print(sizeFactors(dds))
  
  # estimating dispersions
  dds <- estimateDispersions(dds, fitType=fitType)
  
  # statistical testing: perform all the comparisons between the levels of varInt
  dds <- nbinomWaldTest(dds, ...)



  results <- list()
  for (comp in combn(nlevels(colData(dds)[,varInt]), 2, simplify=FALSE)){
    levelRef <- levels(colData(dds)[,varInt])[comp[1]]
    levelTest <- levels(colData(dds)[,varInt])[comp[2]]
    results[[paste0(levelTest,"_vs_",levelRef)]] <- results(dds, contrast=c(varInt, levelTest, levelRef),
                                                            pAdjustMethod=pAdjustMethod, alpha=alpha)
    cat(paste("Comparison", levelTest, "vs", levelRef, "done\n"))
  }
  
  return(list(dds=dds,results=results,sf=sizeFactors(dds)))
}
#' Wrapper to run DESeq2
#'
#' Wrapper to run DESeq2: create the \code{DESeqDataSet}, normalize data, estimate dispersions, statistical testing...
#'
#' @param counts \code{matrix} of raw counts
#' @param target target \code{data.frame} of the project
#' @param varInt name of the factor of interest (biological condition)
#' @param batch batch effect to take into account (\code{NULL} by default)
#' @param locfunc \code{"median"} (default) or \code{"shorth"} to estimate the size factors
#' @param fitType mean-variance relationship: "parametric" (default) or "local"
#' @param pAdjustMethod p-value adjustment method: \code{"BH"} (default) or \code{"BY"} for instance
#' @param independentFiltering \code{TRUE} or \code{FALSE} to perform the independent filtering or not
#' @param alpha significance threshold to apply to the adjusted p-values
#' @param ... optional arguments to be passed to \code{nbinomWaldTest()}
#' @return A list containing the \code{dds} object (\code{DESeqDataSet} class), the \code{results} objects (\code{DESeqResults} class) and the vector of size factors
#' @author Aditya Narayan Sarangi
#library(tximport)


run.DESeq2_trans <- function(counts, target, varInt, batch=NULL,
                       locfunc="median", fitType="parametric", pAdjustMethod="BH",alpha=0.05, ...){
  # building dds object
  dds <- DESeqDataSetFromTximport(txi=counts, colData=target, 
                                design=formula(paste("~", ifelse(!is.null(batch), paste(batch,"+"), ""), varInt)))
  cat("Design of the statistical model:\n")
  cat(paste(as.character(design(dds)),collapse=" "),"\n")           
  
  # normalization
  dds <- estimateSizeFactors(dds,locfunc=eval(as.name(locfunc)))
  cat("\nNormalization factors:\n")
  print(sizeFactors(dds))
  
  # estimating dispersions
  dds <- estimateDispersions(dds, fitType=fitType)
  
  # statistical testing: perform all the comparisons between the levels of varInt
  dds <- nbinomWaldTest(dds, ...)



  results <- list()
  for (comp in combn(nlevels(colData(dds)[,varInt]), 2, simplify=FALSE)){
    levelRef <- levels(colData(dds)[,varInt])[comp[1]]
    levelTest <- levels(colData(dds)[,varInt])[comp[2]]
    results[[paste0(levelTest,"_vs_",levelRef)]] <- results(dds, contrast=c(varInt, levelTest, levelRef),
                                                            pAdjustMethod=pAdjustMethod, alpha=alpha)
    cat(paste("Comparison", levelTest, "vs", levelRef, "done\n"))
  }
  
  return(list(dds=dds,results=results,sf=sizeFactors(dds)))
}