ORA.barplot {PPInfer} | R Documentation |
For the functional enrichment analysis, we can visualize the result from the over-representation analysis.
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)
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) |
ORA barplot
Dongmin Jung, Xijin Ge
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.
p.adjust, ggplot2
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")