Ten common mistakes that could ruin your enrichment analysis.
Where this comes from
- Record sourced from PubMed, PMID 42018548.
- Also identified by DOI 10.1371/journal.pcbi.1014122 and PMC identifier 13102193.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Functional enrichment analysis (FEA) is an incredibly powerful way to summarise complex genomics data into information about the regulation of biological pathways including cellular metabolism, signalling and immune responses. About 10,000 scientific articles describe using FEA each year, making it among the most used techniques in bioinformatics. While FEA has become a routine part of workflows via myriad software packages and easy-to-use websites, mistakes can easily creep in due to poor tool design and unawareness among users of pitfalls. Here we outline ten mistakes that undermine the effectiveness of FEA which we commonly see in research articles. We provide practical advice on their mitigation.
Medical subject headings
- Computational Biology
- Genomics