EnrichmentBrowser (version 2.2.2)

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 explore it.

Usage

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

Arguments

res

Enrichment analysis result list (as returned by the functions sbea and nbea).

nr.show

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

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.

html.only

Logical. Should the html file only be written (without opening the browser to view the result page)? Defaults to FALSE.

signif.only

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

Value

gs.ranking: DataFrame 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

    
    # real data
    # (1) reading the expression data from file
    exprs.file <- system.file("extdata/exprs.tab", package="EnrichmentBrowser")
    pdat.file <- system.file("extdata/pData.tab", package="EnrichmentBrowser")
    fdat.file <- system.file("extdata/fData.tab", package="EnrichmentBrowser")
    probe.eset <- read.eset(exprs.file, pdat.file, fdat.file)
    gene.eset <- probe.2.gene.eset(probe.eset) 
    gene.eset <- de.ana(gene.eset)
    annotation(gene.eset) <- "hsa"

    # artificial enrichment analysis results
    gs <- make.example.data(what="gs", gnames=featureNames(gene.eset))
    ea.res <- make.example.data(what="ea.res", method="ora", eset=gene.eset, gs=gs)

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