Combinatorics

Physics

Mann–Whitney U test

Sociology

False discovery rate

Permutation (music)

Population

Mathematics

Evolutionary biology

Biology

Statistical hypothesis testing

Gene

Statistics

Bonferroni correction

Demography

Rank (graph theory)

Acoustics

Multiple comparisons problem

Wilcoxon signed-rank test

Computational biology

Genetics

False positive paradox

Genome biology

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.

Exaggerated false positives by popular differential expression methods when analyzing human population samples