R/regSEA.R

In WTaoUMC/RegEnrich: Gene regulator enrichment analysis

# @rdname regSEA
# @export
setGeneric("regSEA", def = function(object, ...) standardGeneric("regSEA"))

.regSEA = function(object, namedScores, minSize = 5, maxSize = 5000,
                   pvalueCutoff = 0.05, nperm = 10000, ...) {
  network = .net(object)
  stopifnot(length(names(namedScores)) == length(namedScores))
  stopifnot(sum(is.na(namedScores)) == 0 & sum(is.infinite(namedScores)) == 0)
  ### scaled p values
  pos0 = is.infinite(-log10(namedScores))
  namedScores[pos0] = min(namedScores[!pos0])
  
  namedScores1 = stats::setNames(as.vector(scale(-log10(namedScores))),
                                 names(namedScores))
  
  if (utils::packageVersion("fgsea") >= as.package_version("1.13.2")) {
    fgseaFun = fgseaSimple
  } else {
    fgseaFun = fgsea
  }
  regseaRes = fgseaFun(pathways = network, stats = namedScores1,
                       minSize = minSize, maxSize = maxSize, nperm = nperm,
                       ...)
  
  # consider only one side
  regseaRes$pval = ifelse(sign(regseaRes$ES) >= 0, regseaRes$pval,
                          1 - regseaRes$pval)
  regseaRes$nMoreExtreme = ifelse(sign(regseaRes$ES) >= 0,
                                  regseaRes$nMoreExtreme, nperm - regseaRes$nMoreExtreme)
  regseaRes$padj = stats::p.adjust(regseaRes$pval, method = "BH")
  regseaRes = regseaRes[order(regseaRes$pval), ]
  colnames(regseaRes)[1] = "regulator"
  
  regseaRes = as_tibble(regseaRes)
  
  # The results to show
  new(Class = "Enrich", 
      topResult = filter(regseaRes, padj < pvalueCutoff & ES > 0), 
      allResult = regseaRes,
      gene = names(namedScores), namedScores = namedScores,
      type = "GSEA")
}

# Enrichment analysi by gene set enrichment analysis (GSEA).
# @param object a \code{topNetwork} object, the result returned by
# \code{topNet} funciton.
# @param namedScores A named numeric vector of scores,
# the names of the scores are the genes to perform enrichment analysis.
# And the names should be the same as in the topNetwork object.
# Here the scores are p-value of each gene.
# @param minSize integer, the minimum number (default 5) of target genes.
# @param maxSize integer, the maximum number (default 5000) of target genes.
# @param pvalueCutoff numeric, the cutoff for adjusted enrichment p value.
# This is used for obtaining the `topResult` slot in the final `Enrich`
# object. Default is 0.05.
# @param nperm integer, number of permutations. The minimial possible
# nominal p-value is about 1/nperm. The default is 10000.
# @param ... The rest parameters in \code{\link{fgsea}} function.
#
# @return a list of two elements: a table with GSEA results (see
# \code{\link{fgsea}}) and a ggplot object.
# @include regenrichClasses.R
# @import fgsea
# @rdname regSEA
# @seealso \code{\link{regFET}}
# @export
setMethod("regSEA", signature = "TopNetwork", definition = .regSEA)