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The Specious Art of Single-Cell Genomics
Open accessAbstract
Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with reduction to two or three dimensions to produce ‘all-in-one’ visuals of the data that are amenable to the human eye, and these are subsequently used for qualitative and quantitative exploratory analysis. However, there is little theoretical support for this practice, and we show that extreme dimension reduction, from hundreds or thousands of dimensions to two, inevitably induces significant distortion of high-dimensional datasets. We therefore examine the practical implications of low-dimensional embedding of single-cell data, and find that extensive distortions and inconsistent practices make such embeddings counter-productive for exploratory, biological analyses. In lieu of this, we discuss alternative approaches for conducting targeted embedding and feature exploration, to enable hypothesis-driven biological discovery.
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“a recent study showed that relying only on 2D embeddings can lead to misinterpretation of the relationships between cells”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [49]
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Metadata
- Authors
- Chari, T., & Pachter, L.
- Affiliations
- California Institute of Technology (US)
- Corresponding author
- Pachter, Lior
- Venue
- PLoS Computational Biology
- Publisher
- openRxiv
- Published
- 2021-08-26
- Volume
- 19
- Issue
- 8
- Pages
- e1011288-e1011288
- ISSN
- 1553-734X (ISSN-L)
- PMC
- PMC10434946
- OpenAlex
- W4385945347
- Cited by
- 379
- References
- 93
- Citations by year
- 2026: 73 · 2025: 97 · 2024: 102 · 2023: 63 · 2022: 40 · 2021: 3
- FWCI
- 29.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by-nc)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- California Institute of Technology
- Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology
- National Institutes of Health
- National Institute of Mental Health (U19MH114830)
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Keywords
- single-cell genomics, dimensionality reduction, embedding distortion, high-dimensional data, exploratory data analysis
- MeSH terms
- Data Analysis, Humans, Genomics
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