R/rankGeneDrugPerturbation.R

In ToxicoGx: Analysis of Large-Scale Toxico-Genomic Data

# Rank genes based on drug effect
#
# A helper function called from within `drugPerturbationSig`. This is intended
#   for developer use only; if you aren't debugging the package, this should
#   not be used.
#
# @param data: gene expression data matrix
# @param drug: single or vector of drug(s) of interest; if a vector of drugs is
#    provided, they will be considered as being the same drug and will be
#    jointly analyszed
# @param drug.id: drug used in each experiment
# @param drug.concentration: drug concentration used in each experiment
# @param type: cell or tissue type for each experiment
# @param xp: type of experiment (perturbation or control)
# @param batch: experiment batches
# @param duration: The duration of the experiment, in a consistent unit
# @param single.type: Should the statitsics be computed for each cell/tissue
#   type separately?
# @param nthread: number of parallel threads (bound to the maximum number of
#   cores available)
#
# @return [list] of \code{data.frame}s with the statistics for each gene, for
#   each type
#
# @keywords internal
# @export
rankGeneDrugPerturbation <-
  function(data, drug, drug.id, drug.concentration, type, xp, batch, duration,
           single.type=FALSE, nthread=1, verbose=FALSE) {

    if (nthread != 1) {
      availcore <- parallel::detectCores()
      if (missing(nthread) || nthread < 1 || nthread > availcore) {
        nthread <- availcore
      }
      else{
      }
    }

    #### DIMENSIONALITY CHECK
    if (any(c(length(drug.id), length(drug.concentration), length(type), length(xp), length(batch), length(duration)) != nrow(data))) {
      stop("length of drug.id, drug.concentration, type, xp, duration and batch should be equal to the number of rows of data!")
    }
    names(drug.id) <- names(drug.concentration) <- names(type) <- names(batch) <- names(duration) <- rownames(data)
    if (!all(complete.cases(type, xp, batch, duration))) {
      stop("type, batch, duration and xp should not contain missing values!")
    }

    # Returns a matrix of NAs if there is no viability values for the requested dose levels
    if (length(unique(xp)) < 2 ) {
      nc <- c("estimate", "se", "n", "tstat", "fstat", "pvalue")
      rest <- matrix(NA, nrow=nrow(data), ncol=length(nc), dimnames=list(rownames(data), nc))
      rest <- cbind(rest, "fdr"=p.adjust(rest[ , "pvalue"], method="fdr"))
      res <- c(NULL, list(rest))
      names(res) <- list("all"=type)
      return(res)
    }

    res.type <- NULL

    ## build input matrix
    inpumat <- NULL

    ## for each batch/vehicle of perturbations+controls (test within each batch/vehicle to avoid batch effect)
    ubatch <- sort(unique(batch[!is.na(xp) & xp == "perturbation"]))
    names(ubatch) <- paste0("batch", ubatch)


    for (bb in seq_len(length(ubatch))) {
      ## identify the perturbations and corresponding control experiments
      xpix <- rownames(data)[complete.cases(batch, xp) & batch == ubatch[bb] & xp == "perturbation"]

      ctrlix <- rownames(data)[complete.cases(batch, xp) & batch == ubatch[bb] & xp == "control"]

      if (all(!is.na(c(xpix, ctrlix))) && length(xpix) > 0 && length(ctrlix) > 0) {
        if (!all(is.element(ctrlix, rownames(data)))) {
          stop("data for some control experiments are missing!")
        }
        if (verbose) {
          cat(sprintf("type %s: batch %i/%i -> %i vs %i\n", utype[bb], bb, length(ubatch), length(xpix), length(ctrlix)))
        }
        ## transformation of drug concentrations values - decision made to keep in µm
        conc <- drug.concentration # / 10^6
        inpumat <- rbind(inpumat, data.frame("treated"=c(rep(1, length(xpix)), rep(0, length(ctrlix))), "type"=c(type[xpix], type[ctrlix]), "batch"=paste("batch", c(batch[xpix], batch[ctrlix]), sep=""), "concentration"=c(conc[xpix], conc[ctrlix]), "duration"= c(duration[xpix], duration[ctrlix])))
      }
    }


    inpumat[ , "type"] <- factor(inpumat[ , "type"], ordered=FALSE)
    inpumat[ , "batch"] <- factor(inpumat[ , "batch"], ordered=FALSE)

    if (nrow(inpumat) < 3 || length(sort(unique(inpumat[ , "concentration"]))) < 2){ #|| length(unique(inpumat[ , "duration"])) < 2) {
      ## not enough experiments in drug list
      warning(sprintf("Not enough data for drug(s) %s", paste(drug, collapse=", ")))
      return(list("all.type"=NULL, "single.type"=NULL))
    }

    res <- NULL
    utype <- sort(unique(as.character(inpumat[ , "type"])))
    ltype <- list("all"=utype)

    if(single.type) {
      ltype <- c(ltype, as.list(utype))
      names(ltype)[-1] <- utype
    }

    for(ll in seq_along(ltype)) {

      ## select the type of cell line/tissue of interest
      inpumat2 <- inpumat[!is.na(inpumat[ , "type"]) & is.element(inpumat[ , "type"], ltype[[ll]]), , drop=FALSE]
      inpumat2 <- inpumat2[complete.cases(inpumat2), , drop=FALSE]

      if (nrow(inpumat2) < 3 || length(sort(unique(inpumat2[ , "concentration"]))) < 2) {
        ## not enough experiments in data
        nc <- c("estimate", "se", "n", "tstat", "fstat", "pvalue")
        rest <- matrix(NA, nrow=nrow(data), ncol=length(nc), dimnames=list(rownames(data), nc))

      } else {
        ## test perturbation vs control
        if(nthread > 1) {
          ## parallel threads
          splitix <- parallel::splitIndices(nx=ncol(data), ncl=nthread)
          ##TODO:: Can we reimplement this without using length?
          splitix <- splitix[vapply(splitix, length, FUN.VALUE=numeric(1)) > 0]
          mcres <- BiocParallel::bplapply(splitix, function(x, data, inpumat) {
            res <- t(apply(data[rownames(inpumat), x, drop=FALSE], 2, ToxicoGx::geneDrugPerturbation, concentration=inpumat[ , "concentration"], type=inpumat[ , "type"], batch=inpumat[ , "batch"], duration=inpumat[,"duration"]))
            return(res)
          }, data=data, inpumat=inpumat2)
          rest <- do.call(rbind, mcres)
        } else {
          rest <- t(apply(data[rownames(inpumat2), , drop=FALSE], 2, ToxicoGx::geneDrugPerturbation, concentration=inpumat2[ , "concentration"], type=inpumat2[ , "type"], batch=inpumat2[ , "batch"], duration=inpumat2[,"duration"]))
        }
      }
      rest <- cbind(rest, "fdr"=p.adjust(rest[ , "pvalue"], method="fdr"))
      res <- c(res, list(rest))
    }
    names(res) <- names(ltype)
    return(res)
  }