Enrichment Analysis using Multiple Ontologies and FDR Correction
Description
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. 'mulea' employs an innovative empirical
false discovery rate (eFDR) correction method, specifically designed for
interconnected biological data, to accurately identify significant terms
within diverse ontologies. Beyond conventional tools, 'mulea' incorporates a
wide range of ontologies encompassing Gene Ontology, pathways, regulatory
elements, genomic locations, and protein domains. This flexibility empowers
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 standardized GMT format) for 27
model organisms, covering 16 databases and various identifiers.
Additionally, the 'muleaData' ExperimentData Bioconductor package simplifies
access to these 879 pre-defined ontologies. Furthermore, 'mulea''s
architecture allows for easy integration of user-defined ontologies,
expanding its applicability across diverse research areas.