Background bias in functional enrichment analysis: Insights from clusterProfiler
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[1] Xu S., Hu E., Cai Y., et al. (2024). Using clusterProfiler to characterize multiomics data. Nat. Protoc. 19:3292−320. DOI:10.1038/s41596-024-01020-z [2] Wadi L., Meyer M., Weiser J., et al. (2016). Impact of outdated gene annotations on pathway enrichment analysis. Nat. Methods 13:705−6. DOI:10.1038/nmeth.3963 [3] Yu G. (2024). Thirteen years of clusterProfiler. The Innovation 5:100722. DOI:10.1016/j.xinn.2024.100722 [4] Wijesooriya K., Jadaan S. A., Perera K. L., et al. (2022). Urgent need for consistent standards in functional enrichment analysis. PLOS Comput. Biol. 18:e1009935. DOI:10.1371/journal.pcbi.1009935 -
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Yu G. (2026). Background bias in functional enrichment analysis: Insights from clusterProfiler. The Innovation Life 4:100181. https://doi.org/10.59717/j.xinn-life.2026.100181
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Yu G. (2026). Background bias in functional enrichment analysis: Insights from clusterProfiler. The Innovation Life 4:100181. https://doi.org/10.59717/j.xinn-life.2026.100181
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Figure 1.
Comparison of enrichment p-values under different background universes and strategies
