EnrichmentBrowser (version 1.0.3)

ea.browse: Exploration of enrichment analysis results

Description

Functions to extract a flat gene set ranking from an enrichment analysis result object and to detailedly explored it.

Usage

ea.browse(res, nr.show=-1, set.view=TRUE, graph.view=NULL)
    gs.ranking(res, signif.only=TRUE)

Arguments

res

Enrichment analysis result object as returned by the functions ‘sbea’ and ‘nbea’.

nr.show

Number of gene sets to show. As default all statistical significant gene sets are displayed.

set.view

Logical. Should a set-based summary (includes a report, plots, and browsing in KEGG) be created for the result? Defaults to TRUE.

graph.view

Optional. Should a graph-based summary (reports and visualizes consistency of regulations) be created for the result? If specified, it needs to be a gene regulatory network, i.e. either an absolute file path to a tabular file or a character matrix with exactly *THREE* cols; 1st col = IDs of regulating genes; 2nd col = corresponding regulated genes; 3rd col = regulation effect; Use '+' and '-' for activation/inhibition.

signif.only

Logical. Display only those gene sets in the ranking which satisfy the significance level? Defaults to TRUE.

Value

gs.ranking: data.frame with gene sets ranked by the corresponding p-value;ea.browse: none, opens the browser to explore results.

See Also

sbea, nbea, comb.ea.results

Examples

    # (1) reading the expression data from file
    exprs.file <- system.file("extdata/ALL_exprs.tab", package="EnrichmentBrowser")
    pdat.file <- system.file("extdata/ALL_pData.tab", package="EnrichmentBrowser")
    fdat.file <- system.file("extdata/ALL_fData.tab", package="EnrichmentBrowser")
    probe.eset <- read.eset(exprs.file, pdat.file, fdat.file)

    # (2) summarizing probe expression on gene level
    gene.eset <- probe.2.gene.eset(probe.eset) 

    # (3) getting all human KEGG gene sets
    # hsa.gs <- get.kegg.genesets("hsa")
    gs.file <- system.file("extdata/hsa_kegg_gs.gmt", package="EnrichmentBrowser")
    hsa.gs <- parse.genesets.from.GMT(gs.file)

    # (4) performing the enrichment analysis
    ea.res <- sbea(method="ora", eset=gene.eset, gs=hsa.gs, perm=0)

    # (5) result visualization and exploration
    gs.ranking(ea.res)
    ea.browse(ea.res)