ORA.barplot: Visualize the over-representation analysis

In dongminjung/PPInfer: Inferring functionally related proteins using protein interaction networks

Description

For the functional enrichment analysis, we can visualize the result from the over-representation analysis.

Usage

ORA.barplot(object, category, size, count, pvalue, top = 10,
            sort = NULL, decreasing = FALSE, p.adjust.methods = NULL,
            numChar = NULL, title = NULL, transparency = 0.5,
            plot = TRUE)

Arguments

object	a table with category, size, count and p-value of gene sets
category	name of gene sets
size	size of gene sets
count	count of gene sets
pvalue	p-value of gene sets
top	the number of top categories (default: 10)
sort	a variable used for sorting data
decreasing	logical indicating whether ascending or descending order (default: FALSE)
p.adjust.methods	a correction method
numChar	the maximal number of characters of the name of gene sets
title	title for the plot
transparency	transparency (default: 0.5)
plot	return plot when plot is true, otherwise return table (default: TRUE)

Value

ORA barplot

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

p.adjust, ggplot2

Examples

data(examplePathways)
data(exampleRanks)
geneNames <- names(exampleRanks)
set.seed(1)
gene.id <- sample(geneNames, 100)
result.ORA <- ORA(examplePathways, gene.id)
ORA.barplot(result.ORA, category = "Category", size = "Size",
            count = "Count", pvalue = "pvalue", sort = "pvalue")

dongminjung/PPInfer documentation built on May 15, 2019, 10:41 a.m.