Exaggerated false positives by popular differential expression methods when analyzing human population samples
University of California, Irvine · University of California, Los Angeles · +1 more institution
Indexed incrossrefdoajpubmed
Abstract
When identifying differentially expressed genes between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. Expanding the analysis to limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test, we found that FDR control is often failed except for the Wilcoxon rank-sum test. Particularly, the actual FDRs of DESeq2 and edgeR sometimes exceed 20% when the target FDR is 5%. Based on these results, for population-level RNA-seq studies with large sample sizes, we recommend the Wilcoxon rank-sum test.
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5Topics & keywords
Topics
Keywords
- Wilcoxon signed-rank test
- False discovery rate
- Biology
- Bonferroni correction
- False positive paradox
- Multiple comparisons problem
- Population
- Statistical hypothesis testing
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Funding
- NSNational Science FoundationAwards: 2113754, 1846216, DMS-2113754
- APAlfred P. Sloan Foundation
- WMW. M. Keck Foundation
- NINational Institutes of HealthAwards: R01CA228140, R01GM120507, R35GM140888, R01CA193466
- JAJohnson and JohnsonAward: WiSTEM2D Award
- NCNational Cancer InstituteAwards: R01CA193466, R01CA228140
- NINational Institute of General Medical SciencesAwards: R35GM140888, R01GM120507
- DODivision of Mathematical SciencesAward: 2113754
- DODivision of Biological InfrastructureAward: 1846216
- DGDavid Geffen School of Medicine, University of California, Los Angeles