ORA.barplot: Visualize the over-representation analysis

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

Description

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

Usage

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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

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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")

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