A recent study found severely inflated type I error rates for DESeq2 and edgeR, two dominant tools used for differential expression analysis of RNA-seq data. Here, we show that by properly addressing the outliers in the RNA-Seq data using winsorization, the type I error rate of DESeq2 and edgeR can be substantially reduced, and the power is comparable to Wilcoxon rank-sum test for large datasets. Therefore, as an alternative to Wilcoxon rank-sum test, they may still be applied for differential expression analysis of large RNA-Seq datasets.

中文翻译：

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在分析人类样本时，Winsorization 通过流行的差异表达方法大大减少了假阳性


最近的一项研究发现，DESeq2 和 edgeR 的 I 型错误率严重膨胀，这是用于 RNA-seq 数据差异表达分析的两种主要工具。在这里，我们表明，通过使用 winsorization 正确处理 RNA-Seq 数据中的异常值，可以大大降低 DESeq2 和 edgeR 的 I 型错误率，并且其功效与大型数据集的 Wilcoxon 秩和检验相当。因此，作为 Wilcoxon 秩和检验的替代方案，它们仍可用于大型 RNA-Seq 数据集的差异表达分析。