A recent study reported exaggerated false positives by popular differential expression methods when analyzing large population samples. We reproduce the differential expression analysis simulation results and identify a caveat in the data generation process. Data not truly generated under the null hypothesis led to incorrect comparisons of benchmark methods. We provide corrected simulation results that demonstrate the good performance of dearseq and argue against the superiority of the Wilcoxon rank-sum test as suggested in the previous study.

中文翻译：

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在半合成 RNA-seq 数据模拟中忽视归一化的影响会产生人为的假阳性


最近的一项研究报告称，在分析大量群体样本时，流行的差异表达方法夸大了假阳性。我们重现了差异表达分析模拟结果，并确定了数据生成过程中的一个注意事项。在原假设下未真正生成的数据会导致基准方法的比较不正确。我们提供了校正后的模拟结果，证明了 dearseq 的良好性能，并反驳了之前研究中建议的 Wilcoxon 秩和检验的优越性。