ggeaGraph {EnrichmentBrowser} | R Documentation

## 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

    ggeaGraph(gs, grn, se, alpha = 0.05, beta = 1, max.edges = 50,
      cons.thresh = 0.7, show.scores = FALSE)

    ggeaGraphLegend()

### Arguments

`gs` | Gene set under investigation. This should be a character vector of 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.
`se` | 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).
`show.scores` | Logical. Should consistency scores of the edges be displayed? Defaults to FALSE.

### Value

None, plots to a graphics device.

### Author(s)

Ludwig Geistlinger <Ludwig.Geistlinger@sph.cuny.edu>

### See Also

`nbea` to perform network-based enrichment analysis. `eaBrowse` for exploration of resulting gene sets.

### Examples

        # (1) expression data:
        # simulated expression values of 100 genes
        # in two sample groups of 6 samples each
        se <- makeExampleData(what="SE")
        se <- deAna(se)

        # (2) gene sets:
        # draw 10 gene sets with 15-25 genes
        gs <- makeExampleData(what="gs", gnames=names(se))

        # (3) compiling artificial regulatory network
        grn <- makeExampleData(what="grn", nodes=names(se))

        # (4) plot consistency graph
        ggeaGraph(gs=gs[[1]], grn=grn, se=se)

        # (5) get legend
        ggeaGraphLegend()

[Package EnrichmentBrowser version 2.12.1 Index]