Enrichment Analysis using Multiple Ontologies and False
Discovery Rate
Description
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.