R/2_getconsensus.R

In ewymathe/testALTREinstall: Workflow for Identifying Regulatory Elements from Chromatin Accessibility Assay Data

#' Given a GRangeslist object with peaks for each samples, determine
#' the consensus peaks (found in at least N replicates, where N is input
#'  by the user) for each sample type
#'
#' @param samplepeaks A GRangesList object comprising one GRanges object (peaks)
#'  for each sample (output of loadBEDFiles() function)

#' @param minreps minimum number of replicate samples that a peak should be
#'  contained in to be called as a consensus peak. This cutoff will be
#'   applied to both samples.
#'
#' @return a list comprising:
#' 1) a GRangeslist with one GRange for each sample type
#'  which contains consensus peaks
#' 2) a summary statistic table
#'
#' @examples
#' \dontrun{
#' dir <- system.file('extdata', package='ALTRE', mustWork=TRUE)
#' csvfile <- file.path(dir, 'lung.csv')
#' sampleinfo <- loadCSVFile(csvfile)
#' samplePeaks <- loadBedFiles(sampleinfo)
#' consPeaks <- getConsensusPeaks(samplepeaks = samplePeaks, minreps=2)
#'}
#' @export


getConsensusPeaks <- function(samplepeaks, minreps) {
  if (class(samplepeaks) != "GRangesList")
    stop("Peaks must be a GRangesList Object")

  sampnames <- names(samplepeaks)
  sampletypes <- sort(unique(gsub("_.*", "", sampnames)))

  conspeaks <- GRangesList()
  conspeaks_stats <- list()

  # Chromosomes to keep (used later to remove other chromosomes e.g. chrM)
  chrom_subset <- paste0('chr', c(1:22,'X','Y'))

  for (mytype in order(sampletypes)) {
    mytypepeaks <- samplepeaks[grep(sampletypes[mytype], sampnames)]

    # Concatenate all peaks pertaining to the same sample type and merge
    # peaks
    allregregions <- c(mytypepeaks[[1]])
    for (i in 2:length(mytypepeaks)) {
      allregregions <- c(allregregions, mytypepeaks[[i]])
    }
    reducedallregregions <- reduce(allregregions)

    # For each reduced peak, determine whether it was present in each
    # sample type
    for (i in 1:length(mytypepeaks)) {
      typespecific <- findOverlaps(reducedallregregions, mytypepeaks[[i]])
      newdataframe <- data.frame(i = matrix(nrow = length(reducedallregregions)))
      newdataframe[queryHits(typespecific), 1] <- "present"
      values(reducedallregregions) <- cbind(values(reducedallregregions),
                                            newdataframe)
    }
    colnames(values(reducedallregregions)) <- names(mytypepeaks)

    # Find regions that are present in at least N replicates (from user in
    # put minreps)
    reducedallregionsdata <- grangestodataframe(reducedallregregions)
    applymatrix <- as.matrix(reducedallregionsdata[4:ncol(reducedallregionsdata)])
    keepers <- which(apply(applymatrix,
                           1,
                           function(x) length(which(x == "present")) >= minreps))

    reducedallregionsdatakeepers <- reducedallregionsdata[keepers, ]

    # Convert back to GRanges object
    finalgranges <- GRanges(reducedallregionsdatakeepers$chr,
                            IRanges(reducedallregionsdatakeepers$start,
                                    reducedallregionsdatakeepers$stop))
    mcols(finalgranges)[1] <- sampletypes[mytype]
    colnames(mcols(finalgranges)) <- "sampletype"

   # Remove chromosomes that are not 1-22/X/Y
    finalgranges <- GenomeInfoDb::keepSeqlevels(finalgranges, chrom_subset)

    # Construct output
    conspeaks$mytype <- finalgranges
    names(conspeaks)[mytype] <- sampletypes[mytype]

    # Get some stats for the peaks (before/after merging)
    totconspeaks <- NROW(finalgranges)
    names(totconspeaks) <- sampletypes[mytype]
    totreppeaks <- c()
    for (numreps in 1:length(mytypepeaks)) {
      totreppeaks <- c(totreppeaks, NROW(mytypepeaks[[numreps]]))
      names(totreppeaks)[numreps] <- names(mytypepeaks)[numreps]
    }
    conspeaks_stats[[mytype]] <- c(totconspeaks, totreppeaks)
    names(conspeaks_stats)[[mytype]] <- sampletypes[mytype]
  }  # end looping through sampletypes

  maxreps <- max(c(length(conspeaks_stats[[1]]) - 1,
                   length(conspeaks_stats[[2]]) - 1))
  samp1 <- conspeaks_stats[[1]]
  samp2 <- conspeaks_stats[[2]]
  if (length(samp1) - 1 != maxreps) {
    samp1 <- c(samp1, rep(0, maxreps - length(samp1) + 1))
  }
  if (length(samp2) - 1 != maxreps) {
    samp2 <- c(samp2, rep(0, maxreps - length(samp2) + 1))
  }

  dfstats <- as.data.frame(cbind(c("ConsensusPeaks", paste0("rep", 1:maxreps)),
                                 samp1,
                                 samp2))
  colnames(dfstats) <- c("PeakType", names(conspeaks_stats))

  return(list(consPeaks = conspeaks, consPeaksStats = data.frame(dfstats)))
}