Exaggerated false positives by popular differential expression methods when analyzing human population samples
Yumei Li; Xinzhou Ge; Fanglue Peng; Wei Li; Jingyi Jessica Li · 2022 · Genome biology
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
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 s
Abstract by Yumei Li; Xinzhou Ge; Fanglue Peng; Wei Li; Jingyi Jessica Li, Genome biology (2022) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1186/s13059-022-02648-4