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edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets
Open accessPublished version of a preprint
Abstract
edgeR is an R/Bioconductor software package for differential analyses of sequencing data in the form of read counts for genes or genomic features. Over the past 15 years, edgeR has been a popular choice for statistical analysis of data from sequencing technologies such as RNA-seq or ChIP-seq. edgeR pioneered the use of the negative binomial distribution to model read count data with replicates and the use of generalized linear models to analyze complex experimental designs. edgeR implements empirical Bayes moderation methods to allow reliable inference when the number of replicates is small. This article announces edgeR version 4, which includes new developments across a range of application areas. Infrastructure improvements include support for fractional counts, implementation of model fitting in C and a new statistical treatment of the quasi-likelihood pipeline that improves accuracy for small counts. The revised package has new functionality for differential methylation analysis, differential transcript expression, differential transcript and exon usage, testing relative to a fold-change threshold and pathway analysis. This article reviews the statistical framework and computational implementation of edgeR, briefly summarizing all the existing features and functionalities but with special attention to new features and those that have not been described previously.
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
Self-citationlimma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
Self-citationKEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F.-A., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E.-M., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,845
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
Self-citationRSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Research works printed but not linked: 3
- Lin, 2022, Benchmarking differential expression, imputation and quantification methods for proteomics data, Brief Bioinform, 23, bbac138, 10.1093/bib/bbac138 · doi:10.1093/bib/bbac138
- Baldoni, 2024, Faster and more accurate assessment of differential transcript expression with Gibbs sampling and edgeR v4, NAR Genom Bioinform, 6, lqae151, 10.1093/nargab/lqae151 · doi:10.1093/nargab/lqae151
- Chen, 2013, Differential expression analysis of complex RNA-seq experiments
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Lu, J. Y., Tu, W. B., Li, R., Weng, M., Sanketi, B. D., Yuan, B., Reddy, P., Rodriguez Esteban, C., & Izpisua Belmonte, J. C. (2025) · Cell · doi:10.1016/j.cell.2025.07.031 · Cited by 68
(2025) · doi:10.1016/j.molcel.2025.04.012 · Cited by 21
Lysosomes cell autonomously regulate myeloid cell states and immune responses
Tejwani, L., Balak, C., Skuja, L. L., Tatarakis, D., Rana, A., Fitzgerald, G. A., Ha, C., Lunkes de Melo, G., Sun, E. W., Heffner, C. M., et al. (2024) · doi:10.1101/2024.11.11.623074 · Cited by 4
Selleri, E., Longhi, G., Tarracchini, C., Carolis, E. D., Petraro, S., Mancabelli, L., Milani, C., Turroni, F., Ventura, M., & Lugli, G. A. (2026) · Microbiome Research Reports · doi:10.20517/mrr.2026.19
Metadata
- Authors
- Chen, Y., Chen, L., Lun, A. T. L., Baldoni, P. L., & Smyth, G. K.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2025-01-11
- Volume
- 53
- Issue
- 2
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W4406778273
- Cited by
- 816
- References
- 80
- FWCI
- 199.89
- Type
- journal article
- Language
- en
- Open access
- gold
- Funders
- Chan Zuckerberg Initiative (2019-207283, 2021-237445)
- National Health and Medical Research Council (1058892, 2025645, 1176199)
- Department of Health, State Government of Victoria
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- Chen, Y., Chen, L., Lun, A. T. L., Baldoni, P. L., & Smyth, G. K. (2025). edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets. Nucleic Acids Research. https://doi.org/10.1093/nar/gkaf018
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