Differential analysis of RNA-seq incorporating quantification uncertainty
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Abstract
We describe sleuth (http://pachterlab.github.io/sleuth), a method for the differential analysis of gene expression data that utilizes bootstrapping in conjunction with response error linear modeling to decouple biological variance from inferential variance. sleuth is implemented in an interactive shiny app that utilizes kallisto quantifications and bootstraps for fast and accurate analysis of data from RNA-seq experiments.
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Regularization by combining information across similar genes is commonly applied in learning error models for bulk RNA-seq in differential expression analysis, but has not been previously applied in this context for single-cell normalization.
“This approach is commonly applied in learning error models for bulk RNA-seq in the context of differential expression analysis [22–25], but to our knowledge has not been previously applied in this context for single-cell normalization.”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [24]
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- Authors
- Pimentel, H., Bray, N. L., Puente, S., Melsted, P., & Pachter, L.
- Affiliations
- University of California, Berkeley (US)
- Innovative Genomics Institute (US)
- University of Iceland (IS)
- California Institute of Technology (US)
- Corresponding author
- Pachter, Lior
- Venue
- Nature Methods
- Published
- 2017-06-05
- Volume
- 14
- Issue
- 7
- Pages
- 687-690
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2620721366
- Cited by
- 1,534
- References
- 8
- Citations by year
- 2026: 81 · 2025: 120 · 2024: 164 · 2023: 166 · 2022: 222 · 2021: 249 · 2020: 189 · 2019: 185 · 2018: 111 · 2017: 37 · 2016: 7 · 2012: 1
- FWCI
- 59.00
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (NIH-R01)
- National Human Genome Research Institute (R01HG006129)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK094699)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- differential gene expression, RNA-seq, Sleuth, bootstrapping, Shiny app, uncertainty quantification
- MeSH terms
- Base Sequence, Computer Simulation, Models, Biological, RNA, Software, Gene Expression
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