Enrichment Analysis Using Multiple Ontologies and False Discovery Rate
Overview
Background - Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. Results - mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. Conclusions - mulea is distributed as a CRAN R package. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.
- Updated citation to the BMC Bioinformatics article.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-07-10
- ERROR12 OK, 1 ERROR2026-07-08
- OK13 OK2026-06-09
Show 6 earlier changesHide 6 earlier changes
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-04-22
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-04-16
- NOTE13 OK, 1 NOTE2026-03-28
- OK14 OK2026-03-10
Documentation
- Help pages
- 15
- Examples that run
- 100%12 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Repository
- PRs merged (all time)
- 83%
- Median days to close, 50 most recently updated closed issues
- 6
- Median days to close, 50 most recently updated closed PRs
- 0
Stars over time
Forks over time
Releases over time
Issues over time
PRs over time
6 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/eltebioinformatics/mulea on 2026-10-18.
The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel
Dependencies
Installing mulea adds 113 packages (60 with compiled code, 84 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB. 1 more not on CRAN is not measured.
Code & Tests
Datasets
People & History
Package metadata
- First published
- 2024-05-25
- Total releases
- 4 / 2 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 4.0.0
- Download size
- 970 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("mulea")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1186/s12859-024-05948-7.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.