R/rep_de.R

In Coraline66/Deseq: DESeq2 Gene Differential Analysis

#' DESeq2 Analysis of Resampled Subsets
#'
#' Note that the length of exported list should be equal to the number of rows 
#' of matrix sam times the number of rows of matrix samp(element of samp if it is
#' a list). The results for the subsets of the first new truth set are stored in
#' "rep_de(n, sam, samp, datatype)[[k]], k=1, 2, ..., dim(samp[[1]])[1]", i.e., 
#' the first "dim(samp[[1]])[1]" elements of the returned result of function 
#' "rep_de()". Similarly, "rep_de(n, sam, samp, datatype)[[k]], 
#' k=dim(samp[[1]])[1]+1, ..., 2*(dim(samp[[1]])[1])" contains the information
#' corresponding to the second new truth set, etc.
#' @import DESeq2
#' @param n integer. The number of samples for subsequent analysis.
#'          Note that n equals 2 times the number of columns of matrix samp for 
#'          datatype "luad" & "brca", while it equals the number of columns
#'          of matrix samp(element of samp if it is a list) for datatype "sarc". 
#' @param sam matrix obtained from first-step resampling which indicates the 
#'            new truth sets.
#' @param samp matrix or a list of matrices obtained from second-step resampling. 
#'        It consists of the indices of subsets generated from the new truth sets.
#' @param datatype character indicating which type of data is involved. 
#'                  "luad", "brca" or "sarc".
#' @description This function performs DESeq2 differential analysis to a group 
#'              of subsets of the samples to investigate the effect of replicates.
#' @return a list that is composed of the results of subgroup DESeq2 analysis.
#'         A criterion of abs(log2-FoldChange) >  1 and adjusted P-value < 0.05 
#'         is utilized during the filtering process.
#' @export
#' @examples
#' rep_de(n=4, sam=sam, samp=samp, datatype="luad")

rep_de <- function(n, sam, samp, datatype) {
    # Perform Deseq2 differential analysis to randomly generated subgroups
    in_de <- vector(mode="list")
    for (j in 1:(dim(sam)[1])) {
        for (i in 1:(dim(samp[[j]])[1])) {
            in_de[[i+(j-1)*(dim(sam)[1])]] <- deseq2(n, i, samp[[j]], datatype)
        }
    }

    # Return Data
    return(in_de)
}