Bayesian approach to single-cell differential expression analysis
Open accessAbstract
Single-cell data provide a means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression-magnitude distortions typical of single-cell RNA-sequencing measurements, which enables detection of differential expression signatures and identification of subpopulations of cells in a way that is more tolerant of noise.
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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 [17]
The Online Methods sections on similarity measures and differential-expression performance describe evaluations of single-cell tools, including random resampling of cells and benchmarking against measured expression data. They do not establish that this paper computed simulations as if cells were independent, or support the claim that all such papers did so. No page numbers are available in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThe sparsity and heterogeneity of single-cell data initially encouraged the development of specialized statistical methods to identify differentially expressed mRNAs.
“The sparsity and heterogeneity of single-cell data initially encouraged the development of specialized statistical methods to identify differentially expressed mRNAs7,8.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [7]
Sparsity and heterogeneity are two major challenges in analyzing single-cell data and usually cause false discoveries of differentially expressed genes.
“Sparsity and heterogeneity are two major challenges in analyzing single-cell data, and usually cause false discoveries of differentially expressed genes65.”
From ASGARD is a single-cell guided pipeline to aid repurposing of drugs (2023) · cites this paper as [65]
The cell cycle often has an enormous impact on cellular gene expression, and this impact can extend to other processes and be affected by them.
“The cell cycle often has an enormous impact on cellular gene expression146,149,150 (although some87 have challenged this view), but this impact can extend to other processes, and in turn be affected by them.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [87]
Measurements of single-cell transcriptomes contain various sources of noise.
“Measurements of single‐cell transcriptomes contain various sources of noise (Grün et al, 2014; Kharchenko et al, 2014; Hicks et al, 2017).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Kharchenko et al, 2014
These artefacts are taken into account in methods designed specifically for single-cell data.
“These artefacts are taken into account in methods designed specifically for single‐cell data (Kharchenko et al, 2014; Finak et al, 2015).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Kharchenko et al, 2014
A mixture model including a dropout probability and a negative binomial distribution represents scRNA-seq data better than the negative binomial alone.
“Indeed, a mixture model including a dropout probability and a negative binomial distribution, modeling the overdispersion in the count data, have been shown to represent scRNA-seq data better than the negative binomial alone (Finak et al., 2015; Kharchenko et al., 2014).”
From Comparative Analysis of Single-Cell RNA Sequencing Methods (2017) · cites this paper as Kharchenko et al., 2014
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- Authors
- Kharchenko, P. V., Silberstein, L., & Scadden, D. T.
- Affiliations
- Boston Children's Hospital (US)
- Harvard University (US)
- Corresponding author
- Kharchenko, Peter V
- Venue
- Nature Methods
- Published
- 2014-05-18
- Volume
- 11
- Issue
- 7
- Pages
- 740-742
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC4112276
- OpenAlex
- W1967327758
- Cited by
- 1,348
- References
- 23
- Citations by year
- 2026: 55 · 2025: 98 · 2024: 114 · 2023: 122 · 2022: 136 · 2021: 166 · 2020: 160 · 2019: 150 · 2018: 134 · 2017: 105 · 2016: 75 · 2015: 25 · 2014: 7
- FWCI
- 28.95
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Institute on Aging (K25AG037596)
- National Heart, Lung, and Blood Institute (U01HL100402, R01HL097794)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK050234)
- 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
- single-cell RNA sequencing, differential gene expression, Bayesian model, expression variability, technical noise, biological variation, probabilistic models, cell subpopulations, noise immunity
- MeSH terms
- Algorithms, Bayes Theorem, Sequence Analysis, RNA, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Single-Cell Analysis
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