ggeaGraph: GGEA graphs of consistency between regulation and expression

In EnrichmentBrowser: Seamless navigation through combined results of set-based and network-based enrichment analysis

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 <[e-mail]>

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()

EnrichmentBrowser documentation built on Dec. 12, 2020, 2 a.m.