R/summarizeResults.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

#' Summarize edgeR analysis
#'
#' Summarize edgeR analysis: diagnotic plots, dispersions plot, summary of the independent filtering, export results...
#'
#' @param out.edgeR the result of \code{run.edgeR()}
#' @param group factor vector of the condition from which each sample belongs
#' @param counts matrix of raw counts
#' @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.edgeR()} and (ii) a table summarizing the number of differentially expressed features
#' @author Hugo Varet

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

  if (!I("tables" %in% dir())) dir.create("tables", showWarnings=FALSE)
  
  complete <- exportResults.edgeR(out.edgeR=out.edgeR, group=group, counts=counts, alpha=alpha)
  
  # small table with number of differentially expressed features
  nDiffTotal <- nDiffTotal(complete=complete, alpha=alpha)
  cat("Number of features down/up and total:\n")
  print(nDiffTotal, quote=FALSE)
  
  return(list(complete=complete))
}