enrich.net: Visualize network for the functional enrichment analysis

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/enrich.net.R

Description

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

Usage

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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

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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 May 2, 2018, 3:11 a.m.