C-T.17: Ten common mistakes that could ruin your enrichment analysis!
Functional or Pathway Enrichment Analysis (FEA or PEA) is a cornerstone of modern genomics, essential for providing biological meaning from high-dimensional omics datasets. Biological insights on regulation of pathways like cellular metabolism, signaling and immune responses can be obtained by FEA. Despite its ubiquity, with over 170,000 related publications from 2022 to 2026, the reliability of FEA remains compromised by widespread methodological flaws. This indicates that the reproducibility crisis is prevalent in bioinformatics and genomics. 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. Some critical issues related to statistically flawed and lower reproducibility of enrichment studies include the lack of context-specific background correction in gene list analysis, lack of p-value correction when carrying out parallel testing, and a general lack of methodological details. Critical examination of hundreds of published articles describing the use of FEA, including articles from respected high-impact journals like Nature Communications and Nucleic Acid Research, had high error rates for essential parameters. In our article, we outline the top ten mistakes that undermine the effectiveness of FEA, which we have commonly observed in published research articles. We also share practical solutions that can be implemented to mitigate non-reproducible methodologies. We aim to tackle the reproducibility crisis in the life sciences sector and subsequently improve the statistical rigour and biological validity of enrichment analysis results in life sciences research.
Co-authors: Dr Mark Ziemann, Dr Matthew McKenzie
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