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

Description Usage Arguments Value Author(s) See Also Examples

View source: R/ggeaGraph.R

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

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    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 data given as an object of class SummarizedExperiment.

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.

Author(s)

Ludwig Geistlinger <[email protected]>

See Also

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

Examples

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

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

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

EnrichmentBrowser documentation built on Nov. 29, 2017, 2 a.m.