EnrichmentBrowser (version 2.2.2)

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,  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.

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) expression set: 
    # 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=featureNames(eset))

    # (3) compiling artificial regulatory network 
    grn <- make.example.data(what="grn", nodes=featureNames(eset))

    # (4) plot consistency graph
    ggea.graph(gs=gs[[1]], grn=grn, eset=eset)

    # (5) get legend
    ggea.graph.legend()