Exaggerated false positives by popular differential expression methods when analyzing human population samples
Yumei Li, Xinzhou Ge, Fanglue Peng, Wei Li, Jingyi Jessica Li
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.
Topics & Concepts
Wilcoxon signed-rank testFalse discovery rateBiologyBonferroni correctionFalse positive paradoxMultiple comparisons problemPopulationStatistical hypothesis testingPermutation (music)Computational biologyRank (graph theory)StatisticsGeneticsEvolutionary biologyMathematicsGeneCombinatoricsSociologyDemographyPhysicsAcousticsMann–Whitney U testGene expression and cancer classificationMolecular Biology Techniques and ApplicationsCancer-related molecular mechanisms research