R/summarizeResults.DESeq2.r

In sarangian/RNASeqDEA: 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

#' Summarize DESeq2 analysis
#'
#' Summarize DESeq2 analysis: diagnotic plots, dispersions plot, summary of the independent filtering, export results...
#'
#' @param out.DESeq2 the result of \code{run.DESeq2()}
#' @param group factor vector of the condition from which each sample belongs
#' @param independentFiltering \code{TRUE} or \code{FALSE} to perform the independent filtering or not
#' @param cooksCutoff outliers detection threshold (TRUE to let DESeq2 choosing it or FALSE to disable the outliers detection)
#' @param alpha significance threshold to apply to the adjusted p-values
#' @param col colors for the plots
#' @param log2FClim numeric vector containing both upper and lower y-axis limits for all the MA-plots produced (NULL by default to set them automatically)
#' @param padjlim numeric value between 0 and 1 for the adjusted p-value upper limits for all the volcano plots produced (NULL by default to set them automatically)
#' @return A list containing: (i) a list of \code{data.frames} from \code{exportResults.DESeq2()}, (ii) the table summarizing the independent filtering procedure and (iii) a table summarizing the number of differentially expressed features
#' @author Hugo Varet

  summarizeResults.DESeq2 <- function(out.DESeq2, group, independentFiltering=TRUE, cooksCutoff=TRUE,
                                    alpha=0.05, col=c("lightblue","orange","MediumVioletRed","SpringGreen"),
                                    log2FClim=NULL, padjlim=NULL){

  if (!I("tables" %in% projectName)) dir.create("tables", showWarnings=FALSE)

  
  
  dds <- out.DESeq2$dds
  results <- out.DESeq2$results
  
  if (independentFiltering){
    tabIndepFiltering <- tabIndepFiltering(results)
    cat("Number of features discarded by the independent filtering:\n")
    print(tabIndepFiltering, quote=FALSE)
  } else{
    tabIndepFiltering <- NULL
  }
  
  # exporting results of the differential analysis
  complete <- exportResults.DESeq2(out.DESeq2, group=group, alpha=alpha)
  
  # small table with number of differentially expressed features
  nDiffTotal <- nDiffTotal(complete=complete, alpha=alpha)
  cat("\nNumber of features down/up and total:\n")
  print(nDiffTotal, quote=FALSE)
  
  return(list(complete=complete, tabIndepFiltering=tabIndepFiltering, nDiffTotal=nDiffTotal))
}