Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples

IF 10.1 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Genome Biology Pub Date : 2024-10-30 DOI:10.1186/s13059-024-03230-w

Lu Yang, Xianyang Zhang, Jun Chen

引用次数: 0

Abstract

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.

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在分析人类群体样本时，Winsorization 可大大降低流行的差异表达方法的误报率

最近的一项研究发现，用于 RNA-seq 数据差异表达分析的两个主要工具 DESeq2 和 edgeR 的 I 型错误率严重偏高。在这里，我们展示了通过使用胜数化（winsorization）来适当处理 RNA-seq 数据中的异常值，DESeq2 和 edgeR 的 I 型错误率可以大大降低，而且在大型数据集上的功率与 Wilcoxon 秩和检验相当。因此，作为 Wilcoxon 秩和检验的替代方法，它们仍可用于大型 RNA-Seq 数据集的差异表达分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Genome Biology Biochemistry, Genetics and Molecular Biology-Genetics

CiteScore

21.00

自引率

3.30%

发文量

241

审稿时长

2 months

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