Tool choice matters: Evaluating edgeR vs. DESeq2 for sensitivity, robustness, and cross-study performance

Area of Science:

• Transcriptomics
• Bioinformatics
• Computational Biology

Background:

• Differential gene expression (DGE) analysis is crucial for RNA-sequencing research.
• The selection of DGE tools can significantly alter study outcomes.
• edgeR and DESeq2 are widely adopted tools for DGE analysis.

Purpose of the Study:

• To compare the performance of edgeR and DESeq2 using diverse RNA-Seq datasets.
• To evaluate tool sensitivity to sample size, outlier robustness, and classification performance.
• To assess pathway-level concordance and cross-study generalizability of gene sets identified by each tool.

Main Methods:

• Analysis of real and semi-simulated bulk RNA-Seq data from human patients across various conditions.
• Repeated subsampling to assess sensitivity to sample size.
• Introduction of simulated outliers to test robustness.
• Classification modeling using tool-specific gene sets.
• Pathway enrichment analysis (Hallmark, KEGG).
• Cross-study validation using independent SARS-CoV-2 datasets.

Main Results:

• DESeq2 identified more differentially expressed genes (DEGs) than edgeR at smaller sample sizes; concordance increased with sample size.
• Both tools exhibited similar robustness to simulated outliers.
• edgeR achieved higher F1 scores and precision in classification tasks.
• Substantial pathway-level agreement was observed, with some tool-specific pathways identified.
• edgeR-specific gene sets demonstrated superior performance in cross-study validation (higher AUC, precision, recall).

Conclusions:

• DESeq2 may yield more DEGs under strict criteria, whereas edgeR produces more conservative, predictive, and generalizable gene sets.
• The choice of DGE tool should consider downstream reproducibility, predictive value, and biological interpretability beyond just DEG count.
• Findings highlight the importance of tool selection in transcriptomic research for reliable biological insights.