R/runassignGFRN.R

In ASSIGN: Adaptive Signature Selection and InteGratioN (ASSIGN)

#' Run optimized single pathway ASSIGN
#'
#' This function runs eight ASSIGN runs based on the pathway optimizations
#' from the paper. You can run all eight pathways in serial, or call this
#' function and specify the run parameter to run a specific pathway.
#' Some ASSIGN parameters can be customized using this function. The default
#' values were used in the analysis for the paper.
#'
#' @param indata The list of data frames from ComBat.step2
#' @param run specifies the pathways to predict. The default list will
#' cause all eight pathways to be run in serial. Specify a pathway ("akt",
#' "bad", "egfr", etc.) or list of pathways to run those pathways only.
#' @param optimized_geneList a list of custom optimized gene lists for the gfrn
#' pathways either created manually or output by optimizeGFRN
#' @param use_seed Set the seed before running ASSIGN. This will make the result
#' consistent between runs. The default is 1234. Set use_seed as FALSE to not
#' set a seed.
#' @param sigma_sZero Each element of the signature matrix (S) is modeled by a
#' spike-and-slab mixture distribution. Sigma_sZero is the variance of the
#' spike normal distribution. The default is 0.05.
#' @param sigma_sNonZero Each element of the signature matrix (S) is modeled by
#' a spike-and-slab mixture distribution. Sigma_sNonZero is the variance of the
#' slab normal distribution. The default is 0.5.
#' @param S_zeroPrior Logicals. If TRUE, the prior distribution of signature
#' follows a normal distribution with mean zero. The default is FALSE.
#' @param iter The number of iterations in the MCMC. The default is 100000.
#' @param burn_in The number of burn-in iterations. These iterations are
#' discarded when computing the posterior means of the model parameters. The
#' default is 50000.
#' @param exclude_common_genes Remove commonly differentially expressed genes
#' for overexpression signatures. The default is FALSE.
#' @param adaptive_S Logical. If TRUE, the model adapts the signatures (S) of
#' genomic measures for the test samples. The default for GFRN analysis is TRUE.
#' @param ECM Logicals. If TRUE, ECM algorithm, rather than Gibbs sampling, is
#' applied to approximate the model parameters. The default is FALSE.
#' @return Data is output to the current working directory in a results
#' directory.
#'
#' @examples
#' \dontrun{
#' testData <- read.table(paste0("https://drive.google.com/uc?authuser=0&",
#'                               "id=1mJICN4z_aCeh4JuPzNfm8GR_lkJOhWFr",
#'                               "&export=download"),
#'                        sep='\t', row.names=1, header=1)
#' combat.data <- ComBat.step2(testData, pcaPlots = TRUE)
#' runassignGFRN(combat.data)
#' }
#'
#' @export runassignGFRN
#'
runassignGFRN <- function(indata, run=c("akt", "bad", "egfr", "her2", "igf1r",
                                        "krasgv", "raf"),
                          optimized_geneList=NULL, use_seed=1234,
                          sigma_sZero=0.05, sigma_sNonZero=0.5,
                          S_zeroPrior=FALSE, iter=100000, burn_in=50000,
                          exclude_common_genes=FALSE, adaptive_S=TRUE, ECM=FALSE) {

  #list of anchor genes
  anchorGeneList <- list(akt = "AKT1", bad = "BAD", egfr = "EGFR",
                         her2 = "ERBB2", igf1r = "IGF1R", krasgv = "KRAS",
                         raf = "RAF1")

  #list of corresponding controls for each pathway
  gfpList <- list(akt = "gfp", bad = "gfp", egfr = "egfr_gfp", her2 = "gfp",
                  igf1r = "gfp", krasgv = "kras_gfp", raf = "gfp")

  if (is.null(optimized_geneList)) {
    utils::data("gfrn_geneList", package = "ASSIGN", envir = environment())
    gfrn_geneList <- get("gfrn_geneList", envir = environment())
    optimized_geneList <- list(akt = c(gfrn_geneList$akt_up[1:10],
                                       gfrn_geneList$akt_down[1:10]),
                               bad = c(gfrn_geneList$bad_up[1:125],
                                       gfrn_geneList$bad_down[1:125]),
                               egfr = c(gfrn_geneList$egfr_up[1:25],
                                        gfrn_geneList$egfr_down[1:25]),
                               her2 = c(gfrn_geneList$her2_up[1:5],
                                        gfrn_geneList$her2_down[1:5]),
                               igf1r = c(gfrn_geneList$igf1r_up[1:50],
                                         gfrn_geneList$igf1r_down[1:50]),
                               krasgv = c(gfrn_geneList$krasgv_up[1:100],
                                          gfrn_geneList$krasgv_down[1:100]),
                               raf = c(gfrn_geneList$raf_up[1:175],
                                       gfrn_geneList$raf_down[1:175]))
  }

  for (curr_path in run) {
    trainingLabel <- list()
    trainingLabel[["control"]] <- list()
    trainingLabel[["control"]][[curr_path]] <- seq_len(
      ncol(indata[[gfpList[[curr_path]]]]))
    trainingLabel[[curr_path]] <- (ncol(indata[[gfpList[[curr_path]]]]) + 1):
      (ncol(indata[[gfpList[[curr_path]]]]) + ncol(indata[[curr_path]]))

    if (!(anchorGeneList[curr_path] %in% rownames(indata[["test"]]))) {
      warning(anchorGeneList[curr_path], " not in input data. No anchor gene ",
              "will be used.")
      anchorGeneList[curr_path] <- list(NULL)
    }

    excludeGeneList <- NULL
    if (exclude_common_genes) {
      excludegenes <- get("excludegenes", envir = environment())
      excludeGeneList <- list()
      excludeGeneList[curr_path] <- list(excludegenes)
    }

    if (use_seed) {
      set.seed(use_seed)
    }

    assign.wrapper(trainingData = cbind(indata[[gfpList[[curr_path]]]],
                                        indata[[curr_path]]),
                   testData = indata[["test"]],
                   anchorGenes = anchorGeneList[curr_path],
                   excludeGenes = excludeGeneList,
                   trainingLabel = trainingLabel,
                   geneList = optimized_geneList[curr_path],
                   n_sigGene = NA,
                   adaptive_B = TRUE,
                   adaptive_S = adaptive_S,
                   mixture_beta = FALSE,
                   S_zeroPrior = S_zeroPrior,
                   outputDir = paste(curr_path, "_",
                                     length(optimized_geneList[[curr_path]]),
                                     "_gene_list", sep = ""),
                   sigma_sZero = sigma_sZero, sigma_sNonZero = sigma_sNonZero,
                   iter = iter, burn_in = burn_in, ECM = ECM)
  }
}