Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
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Abstract
Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.
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Papers evaluating the performance of single-cell-specific tools all compute the simulations as if cells were independent.
“Furthermore, papers evaluating the performance of single-cell-specific tools all compute the simulations as if cells were independent15–21.”
From A practical solution to pseudoreplication bias in single-cell studies (2021) · cites this paper as [19]
When bulk tools are adapted to model single-cell data via introducing gene weights into the tests, these tools have been suggested to outperform their single-cell counterparts.
“Furthermore, when bulk tools are adapted to model single‐cell data via introducing gene weights into the tests, these tools have been suggested to outperform their single‐cell counterparts (Van den Berge et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Van den Berge et al, 2018
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- Authors
- Van den Berge, K., Perraudeau, F., Soneson, C., Love, M. I., Risso, D., Vert, J.-P., Robinson, M. D., Dudoit, S., & Clement, L.
- Affiliations
- University of California System (US)
- SIB Swiss Institute of Bioinformatics (CH)
- University of North Carolina at Chapel Hill (US)
- Weill Cornell Medicine (US)
- Inserm (FR)
- Ghent University (BE)
- Corresponding author
- Dudoit, Sandrine
- Clement, Lieven
- ORCID
- Venue
- Genome Biology
- Published
- 2018-02-26
- Volume
- 19
- Issue
- 1
- Pages
- 24-24
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC6251479
- OpenAlex
- W2788908087
- Cited by
- 228
- References
- 70
- Citations by year
- 2026: 14 · 2025: 23 · 2024: 40 · 2023: 27 · 2022: 38 · 2021: 31 · 2020: 29 · 2019: 21 · 2018: 4
- FWCI
- 7.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Funders
- IAP StUDyS grant (P7/06)
- MRP N2N
- Fonds Wetenschappelijk Onderzoek (1S 418 16N)
- Forschungskredit (FK-16-107)
- National Institutes of Health (CA142538-08, U01 MH105979)
- Agence Nationale de la Recherche (ABS4NGS ANR-11-BINF-0001)
- European Research Council (ERC-SMAC-290032)
- Adolph C. and Mary Sprague Miller Institute for Basic Research in Science, University of California Berkeley
- Fulbright Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- 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
- Keywords
- zero-inflated data, dropout events, single-cell RNA sequencing, differential expression analysis, observation weighting, zero-inflated negative binomial regression
- MeSH terms
- Software, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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