Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model
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Abstract
Single-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform the current practice in a downstream clustering assessment using ground truth datasets.
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Deviance identifies highly informative genes by fitting a gene-wise model that assumes constant expression across all cells and quantifying which genes violate this assumption.
“Deviance identifies highly informative genes by fitting a gene-wise model that assumes constant expression across all cells and quantifying which genes violate this assumption47.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [47]
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- Authors
- Townes, F. W., Hicks, S. C., Aryee, M. J., & Irizarry, R. A.
- Affiliations
- Harvard University (US)
- Johns Hopkins University (US)
- Venue
- Genome Biology
- Published
- 2019-12
- Volume
- 20
- Issue
- 1
- Pages
- 295-295
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC6927135
- OpenAlex
- W2998065416
- Cited by
- 471
- References
- 68
- Citations by year
- 2026: 49 · 2025: 56 · 2024: 78 · 2023: 76 · 2022: 70 · 2021: 80 · 2020: 48 · 2019: 14
- FWCI
- 17.71
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-07-22)
- Open access
- gold
- doi.org
- Licence
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (R00HG009007, R01HG005220, P41HG004059)
- National Cancer Institute (T32CA009337)
- National Institute of General Medical Sciences (R01GM083084)
- 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, feature selection, dimensionality reduction, multinomial model, unique molecular identifiers, highly variable genes, generalized principal component analysis, cell clustering
- MeSH terms
- Models, Statistical, Sequence Analysis, RNA, Single-Cell Analysis
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