edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets

Yunshun Chen ^1,2, Lizhong Chen ^1,3, Aaron T L Lun ⁴

⁰Bioinformatics Division, WEHI, Parkville, VIC 3052, Australia.

Nucleic Acids Research +

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January 23, 2025

View abstract on PubMed

Summary

edgeR version 4 enhances differential sequencing data analysis with improved infrastructure and new features for methylation, transcript expression, and pathway analysis. This R/Bioconductor package offers accurate statistical inference for RNA-seq and ChIP-seq data, especially with few replicates.

Area Of Science

Bioinformatics
Computational Biology
Statistical Genetics

Background

edgeR is a widely adopted R/Bioconductor package for differential analysis of sequencing count data.
It pioneered negative binomial modeling and generalized linear models for complex experimental designs in genomics.
Empirical Bayes moderation ensures reliable inference even with a small number of biological replicates.

Purpose Of The Study

To announce and detail the new features and improvements in edgeR version 4.
To review the statistical framework and computational implementation of edgeR.
To highlight advancements in differential methylation, transcript expression, and pathway analysis.

Main Methods

Utilizes negative binomial distribution and generalized linear models for read count data.
Implements empirical Bayes moderation for robust statistical inference.
Introduces infrastructure improvements including C-based model fitting and enhanced quasi-likelihood methods.

Main Results

edgeR version 4 incorporates support for fractional counts and improved accuracy for small counts.
New functionalities include differential methylation analysis, differential transcript and exon usage testing.
Enhanced capabilities for testing relative to a fold-change threshold and pathway analysis are now available.

Conclusions

edgeR version 4 provides significant advancements for differential analyses of various sequencing data types.
The updated package offers improved accuracy, expanded functionality, and enhanced computational efficiency.
It continues to be a leading tool for statistical analysis in genomics research.

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