思考问题的熊
(2022-03-20 16:35):
#paper Li, Yumei, Xinzhou Ge, Fanglue Peng, Wei Li, and Jingyi Jessica Li. “Exaggerated False Positives by Popular Differential Expression Methods When Analyzing Human Population Samples.” Genome Biology 23, no. 1 (March 15, 2022): 79. https://doi.org/10.1186/s13059-022-02648-4.
前几天发表在 Genome Biology 的一篇论文，算是比较严谨地论证了在大样本量RNA-seq差异分析时，今后即便不考虑速度因素，也应该抛弃DEseq2和edgeR转而使用朴实无华的Wilcoxon秩和检验。
更具体的内容已经写成推送发出来了，感兴趣可以再看看。
IF:10.100Q1
Genome biology,
2022-03-15.
DOI: 10.1186/s13059-022-02648-4
PMID: 35292087
PMCID:PMC8922736
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.