Publication Date
3-15-2022
Journal
Genome Biology
DOI
10.1186/s13059-022-02648-4
PMID
35292087
PMCID
PMC8922736
PubMedCentral® Posted Date
3-15-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
Computational Biology, Gene Expression Profiling, Humans, RNA-Seq, Sample Size, Sequence Analysis, RNA
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.