ggeaGraph {EnrichmentBrowser} | R Documentation |
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.
ggeaGraph(gs, grn, se, alpha = 0.05, beta = 1, max.edges = 50, cons.thresh = 0.7, show.scores = FALSE) ggeaGraphLegend()
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
|
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. |
None, plots to a graphics device.
Ludwig Geistlinger <Ludwig.Geistlinger@sph.cuny.edu>
nbea
to perform network-based enrichment analysis.
eaBrowse
for exploration of resulting gene sets.
# (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()