Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR Article Swipe
Pedro L. Baldoni
,
Yunshun Chen
,
Soroor Hediyeh-zadeh
,
Yang Liao
,
Xueyi Dong
,
Matthew E. Ritchie
,
Wei Shi
,
Gordon K. Smyth
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.04.02.535231
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.04.02.535231
Differential expression analysis of RNA-seq is one of the most commonly performed bioinformatics analyses. Transcript-level quantifications are inherently more uncertain than gene-level read counts because of ambiguous assignment of sequence reads to transcripts. While sequence reads can usually be assigned unambiguously to a gene, reads are very often compatible with multiple transcripts for that gene, particularly for genes with many isoforms. Software tools designed for gene-level differential expression do not perform optimally on transcript counts because the read-to-transcript ambiguity (RTA) disrupts the mean-variance relationship normally observed for gene level RNA-seq data and interferes with the efficiency of the empirical Bayes dispersion estimation procedures. The pseudoaligners kallisto and Salmon provide bootstrap samples from which quantification uncertainty can be assessed. We show that the overdispersion arising from RTA can be elegantly estimated by fitting a quasi-Poisson model to the bootstrap counts for each transcript. The technical overdispersion arising from RTA can then be divided out of the transcript counts, leading to scaled counts that can be input for analysis by established gene-level software tools with full statistical efficiency. Comprehensive simulations and test data show that an edgeR analysis of the scaled counts is more powerful and efficient than previous differential transcript expression pipelines while providing correct control of the false discovery rate. Simulations explore a wide range of scenarios including the effects of paired vs single-end reads, different read lengths and different numbers of replicates.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.04.02.535231
- https://www.biorxiv.org/content/biorxiv/early/2023/10/15/2023.04.02.535231.full.pdf
- OA Status
- green
- Cited By
- 4
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4362595847
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4362595847Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.04.02.535231Digital Object Identifier
- Title
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Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeRWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-04Full publication date if available
- Authors
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Pedro L. Baldoni, Yunshun Chen, Soroor Hediyeh-zadeh, Yang Liao, Xueyi Dong, Matthew E. Ritchie, Wei Shi, Gordon K. SmythList of authors in order
- Landing page
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https://doi.org/10.1101/2023.04.02.535231Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2023/10/15/2023.04.02.535231.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2023/10/15/2023.04.02.535231.full.pdfDirect OA link when available
- Concepts
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False discovery rate, Count data, Computer science, Biology, Bioconductor, Overdispersion, Computational biology, Poisson distribution, Bayes' theorem, Gene, Bayesian probability, Statistics, Genetics, Mathematics, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2024: 1, 2023: 3Per-year citation counts (last 5 years)
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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