R/assign.output.R

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

#' Prediction/validation output for test data
#'
#' The assign.output function outputs the summary results and plots for
#' prediction/validation for the test dataset.
#'
#' The assign.output function is suggested to run after the assign.preprocess,
#' assign.mcmc and assign.summary functions. For the prediction/validation in
#' the test dataset, The Y argument in the assign.mcmc function is the output
#' value "testData_sub" from the assign.preprocess function.
#'
#' @param processed.data The list object returned from the assign.preprocess
#' function.
#' @param mcmc.pos.mean.testData The list object returned from the assign.mcmc
#' function. Notice that for prediction/validation in the test dataset, the Y
#' argument in the assign.mcmc function should be set as the test 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.
#' @param testData The genomic measure matrix of test samples (i.g., gene
#' expression matrix). The dimension of this matrix is probe number x sample
#' number.
#' @param trainingLabel The list linking the index of each training sample to a
#' specific group it belongs to.
#' @param testLabel The vector of the phenotypes/labels of the test samples.
#' @param geneList The list that collects the signature genes of one/multiple
#' pathways. Every component of this list contains the signature genes
#' associated with one pathway.
#' @param adaptive_B Logicals. If TRUE, the model adapts the
#' baseline/background (B) of genomic measures for the test samples. The
#' default is TRUE.
#' @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 mixture 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.output returns one .csv file containing one/multiple
#' pathway activity for each individual test samples, scatter plots of pathway
#' activity for each individual pathway in all the test samples, and heatmap
#' plots for the gene expression of the prior signature and posterior signatures
#' (if adaptive_S equals TRUE) of each individual pathway in the test samples.
#' @author Ying Shen
#' @examples
#' \dontshow{
#' tempdir <- file.path(tempdir(), "assign_output")
#'
#' 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)
#' testLabel1 <- rep(c("subtypeA","subtypeB"),c(53,58))
#'
#' processed.data <- assign.preprocess(
#'   trainingData=trainingData1, testData=testData1,
#'   trainingLabel=trainingLabel1, geneList=geneList1)
#'
#' mcmc.chain <- assign.mcmc(
#'   Y=processed.data$testData_sub, Bg = processed.data$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.output(processed.data = processed.data,
#'               mcmc.pos.mean.testData = mcmc.pos.mean,
#'               trainingData = trainingData1, testData = testData1,
#'               trainingLabel = trainingLabel1, testLabel = testLabel1,
#'               geneList = NULL, adaptive_B = TRUE, adaptive_S = FALSE,
#'               mixture_beta = TRUE, outputDir = tempdir)
#'
#' @export assign.output
assign.output <- function(processed.data, mcmc.pos.mean.testData, trainingData,
                          testData, trainingLabel, testLabel, geneList,
                          adaptive_B=TRUE, adaptive_S=FALSE, mixture_beta=TRUE,
                          outputDir) {
  message("Outputing results...")

  if (mixture_beta) {
    coef_test <- mcmc.pos.mean.testData$kappa_pos
  } else {
    coef_test <- mcmc.pos.mean.testData$beta_pos
  }

  if (!dir.exists(outputDir)) {
    dir.create(outputDir)
  }

  if (any(file.exists(
    file.path(outputDir, "pathway_activity_testset.csv"),
    file.path(outputDir, "signature_heatmap_trainingset.pdf"),
    file.path(outputDir, "signature_heatmap_testset_prior.pdf"),
    file.path(outputDir, "signature_heatmap_testset_posterior.pdf"),
    file.path(outputDir, "pathway_activity_scatterplot_testset.pdf"),
    file.path(outputDir, "pathway_activity_boxplot_testset.pdf")))) {
    stop("Output files already exist. Delete the following files to run assign.cv.output():\n",
         file.path(outputDir, "pathway_activity_testset.csv"), "\n",
         file.path(outputDir, "signature_heatmap_trainingset.pdf"), "\n",
         file.path(outputDir, "signature_heatmap_testset_prior.pdf"), "\n",
         file.path(outputDir, "signature_heatmap_testset_posterior.pdf"), "\n",
         file.path(outputDir, "pathway_activity_scatterplot_testset.pdf"), "\n",
         file.path(outputDir, "pathway_activity_boxplot_testset.pdf"))
  }

  if (is.null(geneList)) {
    pathName <- names(trainingLabel)[-1]
  } else {
    pathName <- names(geneList)
  }

  rownames(coef_test) <- colnames(processed.data$testData_sub)
  colnames(coef_test) <- pathName
  utils::write.csv(coef_test, file = file.path(outputDir, "pathway_activity_testset.csv"))

  #heatmaps of each pathway
  if (!is.null(trainingData) & !is.null(trainingLabel)) {
    heatmap.train(diffGeneList = processed.data$diffGeneList, trainingData,
                  trainingLabel, outPath = file.path(outputDir, "signature_heatmap_trainingset.pdf"))
  }
  heatmap.test.prior(diffGeneList = processed.data$diffGeneList, testData,
                     trainingLabel, testLabel, coef_test, geneList,
                     outPath = file.path(outputDir, "signature_heatmap_testset_prior.pdf"))
  if (adaptive_S) {
    heatmap.test.pos(testData = processed.data$testData_sub,
                     Delta_pos = mcmc.pos.mean.testData$Delta_pos,
                     trainingLabel, testLabel, Delta_cutoff = 0.95,
                     coef_test, geneList,
                     outPath = file.path(outputDir, "signature_heatmap_testset_posterior.pdf"))
  }

  #provide test labels for model validation
  scatter.plot.test(coef_test, trainingLabel, testLabel, geneList,
                    outPath = file.path(outputDir, "pathway_activity_scatterplot_testset.pdf"))
  if (!is.null(testLabel)) {
    box.plot.test(coef_test, trainingLabel, testLabel, geneList,
                  outPath = file.path(outputDir, "pathway_activity_boxplot_testset.pdf"))
  }
}