comb.ea.results {EnrichmentBrowser} | R Documentation |
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.
comb.ea.results( res.list, rank.col=config.ebrowser("GSP.COL"), decreasing=FALSE, rank.fun = c("comp.ranks", "rel.ranks", "abs.ranks"), comb.fun = c("mean", "median", "min", "max", "sum") )
res.list |
A list of enrichment analysis result lists (as returned
by the functions |
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
|
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. |
An enrichment analysis result list
that can be detailedly explored by calling ea.browse
and from
which a flat gene set ranking can be extracted by calling gs.ranking
.
Ludwig Geistlinger <Ludwig.Geistlinger@sph.cuny.edu>
# (1) expression data: # simulated expression values of 100 genes # in two sample groups of 6 samples each eset <- make.example.data(what="eset") eset <- de.ana(eset) # (2) gene sets: # draw 10 gene sets with 15-25 genes gs <- make.example.data(what="gs", gnames=names(eset)) # (3) make artificial enrichment analysis results: # 2 ea methods with 5 significantly enriched gene sets each ora.res <- make.example.data(what="ea.res", method="ora", eset=eset, gs=gs) gsea.res <- make.example.data(what="ea.res", method="gsea", eset=eset, gs=gs) # (4) combining the results res.list <- list(ora.res, gsea.res) comb.res <- comb.ea.results(res.list) # (5) result visualization and exploration gs.ranking(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 <- comb.ea.results(res.list, rank.fun=dummy.rank, comb.fun=wavg)