EnrichmentBrowser (version 1.0.3)

ggea.graph: GGEA graphs of consistency between regulation and expression

Description

Gene graph enrichment analysis (GGEA) is a network-based enrichment analysis method implemented in the EnrichmentBrowser package. The idea of GGEA is to evaluate the consistency of known regulatory interactions with the observed gene expression data. A GGEA graph for a gene set of interest displays the consistency of each interaction in the network that involves a gene set member. Nodes (genes) are colored according to expression (up-/down-regulated) and edges (interactions) are colored according to consistency, i.e. how well the interaction type (activation/inhibition) is reflected in the correlation of the expression of both interaction partners.

Usage

ggea.graph(gs, grn, eset, org=NA, alpha=0.05, beta=1, max.edges=50, cons.thresh=0.7) ggea.graph.legend()

Arguments

gs

Gene set under investigation. This should be a character vector of KEGG gene IDs.

grn

Gene regulatory network. 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.

eset

Expression set. An object of class 'ExpressionSet' containing the gene expression set. See 'read.eset' and 'probe.2.gene.eset' for required annotations in the pData and fData slot.

org

Organism under investigation in KEGG three letter code, e.g. ‘hsa’ for ‘homo sapiens’. Used to map gene IDs to gene symbols as displayed in KEGG pathway maps. If not annotated, gene IDs are plotted as default.

alpha

Statistical significance level. Defaults to 0.05.

beta

Log2 fold change significance level. Defaults to 1 (2-fold).

max.edges

Maximum number of edges that should be displayed. Defaults to 50.

cons.thresh

Consistency threshold. Graphical parameter that correspondingly increases line width of edges with a consistency above the chosen threshold (defaults to 0.7).

Value

None, plots to a graphics device.

See Also

nbea to perform network-based enrichment analysis. ea.browse for exploration of resulting gene sets.

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) 

    # (3a) 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)

    # (3b) compiling gene regulatory network from KEGG pathways
    # hsa.grn <- compile.grn.from.kegg("hsa")
    pwys <- system.file("extdata/hsa_kegg_pwys.zip", package="EnrichmentBrowser")
    hsa.grn <- compile.grn.from.kegg(pwys)

    # plot consistency graph
    ggea.graph(gs=hsa.gs[["hsa05416_Viral_myocarditis"]], 
                                                grn=hsa.grn, eset=gene.eset)

    # get legend
    ggea.graph.legend()