enrich.net: Visualize network for the functional enrichment analysis

In PPInfer: Inferring functionally related proteins using protein interaction networks

Description

The connection between nodes depends on the proportion of overlapping genes between two categories.

Usage

enrich.net(x, gene.set, node.id, node.name = node.id, pvalue,
           n = 50, numChar = NULL, pvalue.cutoff = 0.05,
           edge.cutoff = 0.05, degree.cutoff = 0,
           edge.width = function(x) {10*x^2},
           node.size = function(x) {2.5*log10(x)},
           group = FALSE, group.color = c('red', 'green'),
           group.shape = c('circle', 'square'),
           legend.parameter = list('topright'),
           show.legend = TRUE, ...)

Arguments

x	a result with category and p-value of gene sets
gene.set	gene sets which is already used for functional enrichment
node.id	name of gene sets
node.name	label of nodes in the network (default: node.id)
pvalue	pvalues for categories
n	number of top categories (default: 50)
numChar	the maximal number of characters of the label of gene sets
pvalue.cutoff	nodes with p-values which are greater than pvalue.cutoff are removed (default: 0.05)
edge.cutoff	edges with the proportion which is less than edge.cutoff are removed (default: 0.05)
degree.cutoff	nodes with the degrees which are less than degree.cutoff are removed (default: 0)
edge.width	width of edges
node.size	size of nodes
group	variable for group
group.color	color for group (default: red and green for 2 groups)
group.shape	shape for group (default: circle and square for 2 groups)
legend.parameter	list of parametres for the legend
show.legend	show the legend (default: TRUE)
...	additional parameters for the igraph

Value

plot for the network. The size of nodes is proportional to the size of gene sets. The more significant categories are, the less transparent their nodes are.

Author(s)

Dongmin Jung, Xijin Ge

References

Yu G, Wang L, Yan G and He Q (2015). "DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis." Bioinformatics, 31(4), pp. 608-609.

See Also

igraph

Examples

data(examplePathways)
data(exampleRanks)
set.seed(1)
result.GSEA <- fgsea(examplePathways, exampleRanks, nperm = 1000)
enrich.net(result.GSEA, examplePathways, node.id = 'pathway',
           pvalue = 'pval', edge.cutoff = 0.6, degree.cutoff = 1,
           n = 50, vertex.label.cex = 0.75, show.legend = FALSE,
           edge.width = function(x) {5*sqrt(x)},
           layout = igraph::layout.kamada.kawai)

PPInfer documentation built on Nov. 8, 2020, 7:52 p.m.