Hypothesis
The standard differential-expression tools control the false discovery rate on population-level human RNA-seq data as they do on small controlled designs.
What failed
Permuting condition labels in an immunotherapy RNA-seq dataset made DESeq2 and edgeR report more differentially expressed genes than the real labels did. Across population-level datasets, DESeq2, edgeR, limma-voom and the non-parametric dearseq all failed to control the false discovery rate, which for DESeq2 and edgeR sometimes exceeded 20% at a 5% target. NOISeq controlled it better but had much lower power than the Wilcoxon rank-sum test.
Broken assumption
The parametric models behind DESeq2 and edgeR hold for large human population cohorts.
Why
The authors trace the failure to the parametric assumptions these tools make, which population-scale samples violate, and recommend the Wilcoxon rank-sum test instead.
From the published paper: Li et al. Genome biology, 2022. ↗CC BY