R/assign.cv.output.R

In wevanjohnson/ASSIGN: Adaptive Signature Selection and InteGratioN (ASSIGN)

#' Cross validation output
#' 
#' The assign.cv.output function outputs the summary results and plots for the
#' cross validation done on the training dataset.
#' 
#' The assign.cv.output function is suggested to run after the
#' assign.preprocess, assign.mcmc and assign.summary function. For the cross
#' validation, The Y argument in the assign.mcmc function is the output value
#' "trainingData_sub" from the assign.preprocess function.
#' 
#' @param processed.data The list object returned from the assign.preprocess
#' function.
#' @param mcmc.pos.mean.trainingData The list object returned from the
#' assign.mcmc function. Notice that for cross validation, the Y argument in
#' the assign.mcmc function should be set as the training dataset.
#' @param trainingData The genomic measure matrix of training samples (i.g.,
#' gene expression matrix). The dimension of this matrix is probe number x
#' sample number. The default is NULL.
#' @param trainingLabel The list linking the index of each training sample to a
#' specific group it belongs to.
#' @param adaptive_B Logicals. If TRUE, the model adapts the
#' baseline/background (B) of genomic measures for the test samples. The
#' default is FALSE.
#' @param adaptive_S Logicals. If TRUE, the model adapts the signatures (S) of
#' genomic measures for the test samples. The default is FALSE.
#' @param mixture_beta Logicals. If TRUE, elements of the pathway activation
#' matrix are modeled by a spike-and-slab mixuture distribution. The default is
#' TRUE.
#' @param outputDir The path to the directory to save the output files. The
#' path needs to be quoted in double quotation marks.
#' @return The assign.cv.output returns one .csv file containing one/multiple
#' pathway activity for each individual training samples, scatter plots of
#' pathway activity for each individual pathway in all the training samples,
#' and heatmap plots for the gene expression signatures for each individual
#' pathways.
#' @author Ying Shen
#' @examples
#' 
#' \dontshow{
#' setwd(tempdir())
#' tempdir <- tempdir()
#' data(trainingData1)
#' data(testData1)
#' data(geneList1)
#' 
#' trainingLabel1 <- list(control = list(bcat=1:10, e2f3=1:10, myc=1:10, 
#'                                       ras=1:10, src=1:10), 
#'                        bcat = 11:19, e2f3 = 20:28, myc= 29:38, 
#'                        ras = 39:48, src = 49:55)
#'                        
#' processed.data <- assign.preprocess(trainingData=trainingData1, 
#' testData=testData1, trainingLabel=trainingLabel1, geneList=geneList1)
#' mcmc.chain <- assign.mcmc(Y=processed.data$trainingData_sub, 
#' Bg = processed.data$B_vector, X=processed.data$S_matrix, 
#' Delta_prior_p = processed.data$Pi_matrix, iter = 20, adaptive_B=TRUE, 
#' adaptive_S=FALSE, mixture_beta=TRUE)
#' mcmc.pos.mean <- assign.summary(test=mcmc.chain, burn_in=10, iter=20, 
#' adaptive_B=FALSE, adaptive_S=FALSE, mixture_beta=TRUE)
#' }
#' assign.cv.output(processed.data=processed.data, 
#' mcmc.pos.mean.trainingData=mcmc.pos.mean, trainingData=trainingData1,
#' trainingLabel=trainingLabel1, 
#' adaptive_B=FALSE, adaptive_S=FALSE, mixture_beta=TRUE, outputDir=tempdir)
#' 
#' @export assign.cv.output
assign.cv.output <- function(processed.data, mcmc.pos.mean.trainingData, trainingData, trainingLabel, adaptive_B=FALSE, adaptive_S=FALSE, mixture_beta=TRUE, outputDir){
  cat("Outputing results...\n")
  
  if (mixture_beta){
    coef_train = mcmc.pos.mean.trainingData$kappa_pos
  } else {
    coef_train = mcmc.pos.mean.trainingData$beta_pos
  }
  
  cwd <- getwd()
  setwd(outputDir)
  rownames(coef_train) <- names(processed.data$trainingData_sub)
  colnames(coef_train) <- names(trainingLabel)[-1]
  write.csv(coef_train, file="pathway_activity_trainingset.csv")
  
  #heatmaps of each pathway
  heatmap.train(diffGeneList=processed.data$diffGeneList, trainingData, trainingLabel)
  
  #provide test labels for model validation
  scatter.plot.train(coef_train, trainingData, trainingLabel)
  
  setwd(cwd)
}