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.

This is a companion to the book Cook, D. and Laa, U. (2023) <https://dicook.github.io/mulgar_book/>
  "Interactively exploring high-dimensional data and models in R".  
  by Cook and Laa. It contains useful functions for processing data in preparation for 
  visualising with a tour. There are also several sample data sets.