ArticleNAR genomics and bioinformatics2024
Faster and more accurate assessment of differential transcript expression with Gibbs sampling and edgeR v4.
Article in NAR genomics and bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Review
- Suppression rather than activation of the integrated stress response (GCN2-ATF4) pathway extends lifespan in the fly.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Genome-wide identification and expression analysis of HMAs gene family in tobacco under abiotic stress.Scientific reports · 2026Article
- Revisiting differential expression analysis: An updated six-dimensional comparative study.PloS one · 2026Article
- Isoformic: a workflow for transcript-level RNA-seq interpretation.NAR genomics and bioinformatics · 2025Article
- Dividing out quantification uncertainty enables assessment of differential transcript usage with limma and edgeR.Nucleic acids research · 2025Article
- Synchronous bacterial barrier and exudate absorption: A novel dual-function dressing strategy for pin-site infection prevention.Materials today. Bio · 2025Article
- edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets.Nucleic acids research · 2025Article
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Authors and funding
3 authors.
Funding
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Abstract
This article further develops edgeR's divided-count approach for differential transcript expression (DTE) analysis of RNA-seq data to produce a faster and more accurate pipeline. The divided-count approach models the precision of transcript quantifications from the kallisto and Salmon software tools and divides the estimated overdispersions out of the transcript read counts, after which the divided-counts can be analysed by statistical tools developed for gene-level counts. This article adds three new refinements to the pipeline that dramatically decrease the computational overhead and storage requirements so that DTE analysis of very large datasets becomes practical. The new pipeline replaces bootstrap with Gibbs resampling and replaces edgeR v3 with v4. Both of these changes improve statistical power and accuracy and provide better resolution for low-count transcripts. The accuracy of overdispersion estimation is shown to depend on the total number of resamples across the whole dataset rather than on individual samples, dramatically reducing the recommended number of technical samples for large datasets. Test data and extensive simulations data show that the new pipeline is more powerful and efficient than previous DTE pipelines while providing correct control of the false discovery rate for any sample size.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.