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Exaggerated false positives by popular differential expression methods when analyzing human population samples.

Yumei Li¹, Xinzhou Ge², Fanglue Peng³

¹Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, 92697, USA.

|March 16, 2022

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This summary is machine-generated.

Popular RNA-sequencing analysis tools like DESeq2 and edgeR show high false discovery rates in human population studies. The Wilcoxon rank-sum test offers better false discovery rate control, making it a recommended alternative for large-scale RNA-seq analysis.

Area of Science:

Bioinformatics
Genomics
Statistical analysis

Background:

RNA sequencing (RNA-seq) is crucial for understanding gene expression.
Accurate identification of differentially expressed genes is vital in population studies.
Common bioinformatics tools may exhibit limitations in controlling false discovery rates.

Purpose of the Study:

To evaluate the false discovery rate (FDR) control of popular bioinformatics methods for human population RNA-seq data.
To identify reliable statistical methods for differential gene expression analysis in large sample size studies.

Main Methods:

Permutation analysis was used to assess FDR.
Multiple bioinformatics tools were compared: DESeq2, edgeR, limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test.

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Analysis focused on human population RNA-seq samples.

Main Results:

DESeq2 and edgeR demonstrated unexpectedly high FDRs, sometimes exceeding 20% against a target of 5%.
Most tested methods, except the Wilcoxon rank-sum test, failed to adequately control FDR.
The Wilcoxon rank-sum test showed robust FDR control.

Conclusions:

Standard bioinformatics tools like DESeq2 and edgeR may not be reliable for FDR control in large population RNA-seq studies.
The Wilcoxon rank-sum test is recommended for its superior FDR control in such analyses.
Researchers should carefully select methods to ensure the validity of differential gene expression findings.