Winsorization greatly reduces false positives by popular differential expression methods when analyzing human
Lu Yang1,2, Xianyang Zhang3, Jun Chen4,5
1Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA.
Genome Biology
|October 31, 2024
Summary
Winsorization addresses outliers in RNA-sequencing (RNA-seq) data, reducing inflated error rates in differential expression analysis tools like DESeq2 and edgeR. This method makes these tools viable for large datasets, comparable to the Wilcoxon rank-sum test.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- RNA-sequencing (RNA-seq) is crucial for gene expression analysis.
- DESeq2 and edgeR are widely used for differential expression analysis.
- Previous studies indicated inflated Type I error rates in these tools.
Purpose of the Study:
- To investigate the impact of outlier data on differential expression analysis.
- To evaluate the effectiveness of winsorization in correcting inflated error rates.
- To determine if corrected tools are suitable for large RNA-seq datasets.
Main Methods:
- RNA-seq data analysis using DESeq2 and edgeR.
- Implementation of winsorization to handle data outliers.
- Comparison of error rates and statistical power with and without winsorization.
- Benchmarking against the Wilcoxon rank-sum test.
Main Results:
- Winsorization significantly reduced Type I error rates for DESeq2 and edgeR.
- The statistical power of winsorized DESeq2 and edgeR was comparable to the Wilcoxon rank-sum test.
- The corrected tools demonstrated improved reliability for large-scale RNA-seq analyses.
Conclusions:
- Proper handling of outliers via winsorization is essential for accurate differential expression analysis.
- DESeq2 and edgeR, when applied with winsorization, are reliable alternatives for large RNA-seq datasets.
- This approach enhances the utility of popular bioinformatics tools in genomic research.
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