RcppExports.R

# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

calcGseaStatCumulativeBatch <- function(stats, gseaParam, pathwayScores, pathwaysSizes, iterations, seed, scoreType) {
    .Call('_fgsea_calcGseaStatCumulativeBatch', PACKAGE = 'fgsea', stats, gseaParam, pathwayScores, pathwaysSizes, iterations, seed, scoreType)
}

#' Calculates GSEA statistic values for all the prefixes of a gene set
#'
#' Takes \emph{O(k^\{3/2\})} time, where \emph{k} is a size of `selectedSize`.
#' @param stats Named numeric vector with gene-level statistics
#'     sorted in decreasing order (order is not checked)
#' @param selectedStats indexes of selected genes in a `stats` array
#' @param gseaParam GSEA weight parameter (0 is unweighted, suggested value is 1)
#' @return Numeric vector of GSEA statistics for all prefixes of selectedStats.
#' @export
#' @examples
#' data(exampleRanks)
#' data(examplePathways)
#' ranks <- sort(exampleRanks, decreasing=TRUE)
#' es <- calcGseaStatCumulative(ranks, na.omit(match(examplePathways[[1]], names(ranks))), 1)
calcGseaStatCumulative <- function(stats, selectedStats, gseaParam, scoreType = "std") {
    .Call('_fgsea_calcGseaStatCumulative', PACKAGE = 'fgsea', stats, selectedStats, gseaParam, scoreType)
}

#' Calculates GSEA statistic valus for all gene sets in `selectedStats` list.
#'
#' Takes \emph{O(n + mKlogK)} time, where n is the number of genes, m is the number of gene sets,
#' and k is the mean gene set size.
#' @param stats Numeric vector of gene-level statistics sorted in decreasing order
#' @param selectedGenes List of integer vector with integer gene IDs (from 1 to n)
#' @param geneRanks Integer vector of gene ranks
#' @return Numeric vector of GSEA statistics of the same length as `selectedGenes` list
calcGseaStatBatchCpp <- function(stats, selectedGenes, geneRanks) {
    .Call('_fgsea_calcGseaStatBatchCpp', PACKAGE = 'fgsea', stats, selectedGenes, geneRanks)
}

#' Calculates low GSEA p-values for a given gene set size using the multilevel split Monte Carlo approach.
#'
#' @param enrichmentScores A vector of enrichment scores, for which p-values should be calculated
#' @param ranks An integer vector with the gene-level statistics
#' @param pathwaySize A scalar with the size of the gene set
#' @param seed Random seed
#' @param eps P-values below eps aren't calculated
#' @param sign Controls whether ES^+ or ES score is used
#' @param moveScale Controls the number of MCMC iterations on each level
#' @param logStatus Controls whether debug output should be shown
#' @return table with p-values and estimation errors
#' @keyword internal
fgseaMultilevelCpp <- function(enrichmentScores, ranks, pathwaySize, sampleSize, seed, eps, sign, moveScale = 1.0, logStatus = FALSE) {
    .Call('_fgsea_fgseaMultilevelCpp', PACKAGE = 'fgsea', enrichmentScores, ranks, pathwaySize, sampleSize, seed, eps, sign, moveScale, logStatus)
}

gesecaCpp <- function(E, inpScores, genesetSize, sampleSize, seed, eps) {
    .Call('_fgsea_gesecaCpp', PACKAGE = 'fgsea', E, inpScores, genesetSize, sampleSize, seed, eps)
}