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...

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Bibliographic Details
Published in:Genome Biology 2024-10, Vol.25 (1), p.282-282, Article 282
Main Authors: Yang, Lu, Zhang, Xianyang, Chen, Jun
Format: Article
Language:English
Subjects:
Correspondence
data collection
False Positive Reactions
Gene Expression Profiling - methods
gene expression regulation
genome
human population
Humans
RNA-Seq - methods
sequence analysis
Sequence Analysis, RNA - methods
Software
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Summary: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.
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-024-03230-w