A statistical approach for identifying differential distributions in single-cell RNA-seq experiments
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Abstract
Abstract The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. Although understanding such heterogeneity is of primary interest in a number of studies, for convenience, statistical methods often treat cellular heterogeneity as a nuisance factor. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. Using simulated and case study data, we demonstrate that the modeling framework is able to detect differential expression patterns of interest under a wide range of settings. Compared to existing approaches, scDD has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and is able to characterize those differences. The freely available R package scDD implements the approach.
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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 [18]
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“scDD [39] employs a Bayesian modeling framework to identify genes with differential distributions and to classify them into four situations: where f ( Y g | M DD ) is the predictive distribution of the observed expression value from gene g under a given hypothesis, M DD denotes the differential…”
BackgroundMethodTargeted Perturb-seq enables genome-scale genetic screens in single cells
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- Authors
- Korthauer, K. D., Chu, L.-F., Newton, M. A., Li, Y., Thomson, J., Stewart, R., & Kendziorski, C.
- Affiliations
- Harvard University (US)
- University of Wisconsin–Madison (US)
- University of Wisconsin System (US)
- Venue
- Genome Biology
- Published
- 2016-10-25
- Volume
- 17
- Issue
- 1
- Pages
- 222-222
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC5080738
- OpenAlex
- W2950541340
- Cited by
- 273
- References
- 73
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 26 · 2023: 31 · 2022: 36 · 2021: 56 · 2020: 29 · 2019: 21 · 2018: 24 · 2017: 18 · 2016: 5 · 2013: 1
- FWCI
- 11.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Institute of General Medical Sciences (GM102756)
- National Institute of Allergy and Infectious Diseases (U54AI117924)
- National Institutes of Health (4UH3TR000506-03)
- National Heart, Lung, and Blood Institute (5U01HL099773-06)
- 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
- differential gene expression, single-cell RNA sequencing, gene expression distribution, cellular heterogeneity
- MeSH terms
- Algorithms, Humans, RNA, Software, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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