combResults {EnrichmentBrowser} | R Documentation

## Combining enrichment analysis results

### Description

Different enrichment analysis methods usually result in different gene set rankings for the same dataset. This function allows to combine results from the different set-based and network-based enrichment analysis methods. This includes the computation of average gene set ranks across methods.

### Usage

    combResults(res.list, rank.col = configEBrowser("PVAL.COL"),
      decreasing = FALSE, rank.fun = c("comp.ranks", "rel.ranks",
      "abs.ranks"), comb.fun = c("mean", "median", "min", "max", "sum"))

### Arguments

`res.list` | A list of enrichment analysis result lists (as returned by the functions `sbea` and `nbea`).
`rank.col` | Rank column. Column name of the enrichment analysis result table that should be used to rank the gene sets. Defaults to the gene set p-value column, i.e. gene sets are ranked according to gene set significance.
`decreasing` | Logical. Should smaller (decreasing=FALSE, default) or larger (decreasing=TRUE) values in rank.col be ranked better? In case of gene set p-values the smaller the better, in case of gene set scores the larger the better.
`rank.fun` | Ranking function. Used to rank gene sets according to the result table of individual enrichment methods (as returned from the `gsRanking` function). This is typically done according to gene set p-values, but can also take into account gene set scores/statistics, especially in case of gene sets with equal p-value. Can be either one of the predefined functions ('comp.ranks', 'rel.ranks', 'abs.ranks') or a user-defined function. Defaults to 'comp.ranks', i.e. competitive (percentile) ranks are computed by calculating for each gene set the percentage of gene sets with a p-value as small or smaller. Alternatively, 'rel.ranks', i.e. relative ranks are computed in 2 steps: Ranks are assigned according to distinct gene set p-value *categories*, i.e. gene sets with equal p-value obtain the *same* rank. Thus, the gene sets with lowest p-value obtain rank 1, and so on. As opposed to absolute ranks (rank.fun = 'abs.ranks'), which are returned from step 1, relative ranks are then computed by dividing the absolute rank by number of distinct p-value categories and multiplying with 100 (= percentile rank).
`comb.fun` | Rank combination function. Used to combine gene set ranks across methods. Can be either one of the predefined functions (mean, median, max, min, sum) or a user-defined function. Defaults to 'sum', i.e. the rank sum across methods is computed.

### Value

An enrichment analysis result list that can be detailedly explored by calling `eaBrowse` and from which a flat gene set ranking can be extracted by calling `gsRanking`.

### Author(s)

Ludwig Geistlinger <Ludwig.Geistlinger@sph.cuny.edu>

### See Also

`sbea`, `nbea`, `eaBrowse`

### Examples

        # (1) expression data:
        # simulated expression values of 100 genes
        # in two sample groups of 6 samples each
        se <- makeExampleData(what="SE")
        se <- deAna(se)

        # (2) gene sets:
        # draw 10 gene sets with 15-25 genes
        gs <- makeExampleData(what="gs", gnames=names(se))

        # (3) make artificial enrichment analysis results:
        # 2 ea methods with 5 significantly enriched gene sets each
        ora.res <- makeExampleData(what="ea.res", method="ora", se=se, gs=gs)
        gsea.res <- makeExampleData(what="ea.res", method="gsea", se=se, gs=gs)

        # (4) combining the results
        res.list <- list(ora.res, gsea.res)
        comb.res <- combResults(res.list)

        # (5) result visualization and exploration
        gsRanking(comb.res)

        # user-defined ranking and combination functions
        # (a) dummy ranking, give 1:nrow(res.tbl)
        dummy.rank <- function(res.tbl) seq_len(nrow(res.tbl))

        # (b) weighted average for combining ranks
        wavg <- function(r) mean(c(1,2) * r)

        comb.res <- combResults(res.list, rank.fun=dummy.rank, comb.fun=wavg)

[Package EnrichmentBrowser version 2.12.1 Index]