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

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

{"title":"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","authors":"Lu Yang, Xianyang Zhang, Jun Chen","doi":"10.1186/s13059-024-03230-w","DOIUrl":null,"url":null,"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.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"5 1","pages":""},"PeriodicalIF":10.1000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples\",\"authors\":\"Lu Yang, Xianyang Zhang, Jun Chen\",\"doi\":\"10.1186/s13059-024-03230-w\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":12611,\"journal\":{\"name\":\"Genome Biology\",\"volume\":\"5 1\",\"pages\":\"\"},\"PeriodicalIF\":10.1000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genome Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1186/s13059-024-03230-w\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOTECHNOLOGY & APPLIED MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13059-024-03230-w","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

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。

查看原文本刊更多论文

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

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

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

期刊介绍： Genome Biology stands as a premier platform for exceptional research across all domains of biology and biomedicine, explored through a genomic and post-genomic lens. With an impressive impact factor of 12.3 (2022),* the journal secures its position as the 3rd-ranked research journal in the Genetics and Heredity category and the 2nd-ranked research journal in the Biotechnology and Applied Microbiology category by Thomson Reuters. Notably, Genome Biology holds the distinction of being the highest-ranked open-access journal in this category. Our dedicated team of highly trained in-house Editors collaborates closely with our esteemed Editorial Board of international experts, ensuring the journal remains on the forefront of scientific advances and community standards. Regular engagement with researchers at conferences and institute visits underscores our commitment to staying abreast of the latest developments in the field.