Interpretable dimensionality reduction of single cell transcriptome data with deep generative models
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Abstract
Single-cell RNA-sequencing has great potential to discover cell types, identify cell states, trace development lineages, and reconstruct the spatial organization of cells. However, dimension reduction to interpret structure in single-cell sequencing data remains a challenge. Existing algorithms are either not able to uncover the clustering structures in the data or lose global information such as groups of clusters that are close to each other. We present a robust statistical model, scvis, to capture and visualize the low-dimensional structures in single-cell gene expression data. Simulation results demonstrate that low-dimensional representations learned by scvis preserve both the local and global neighbor structures in the data. In addition, scvis is robust to the number of data points and learns a probabilistic parametric mapping function to add new data points to an existing embedding. We then use scvis to analyze four single-cell RNA-sequencing datasets, exemplifying interpretable two-dimensional representations of the high-dimensional single-cell RNA-sequencing data.
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First applications of deep learning to scRNA-seq are starting to emerge from dimensionality reduction to denoising.
“First applications to scRNA‐seq are starting to emerge from dimensionality reduction to denoising (e.g. scVis: Ding et al, 2018; scGen: preprint: Lotfollahi et al, 2018; DCA: Eraslan et al, 2019).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Ding et al, 2018
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“One concern in applying methods based on neural networks (Ding et al , 2018; Wang & Gu, 2018; Amodio et al , 2019; Eraslan et al , 2019; Grønbech et al , 2020) in single-cell genomics and other domains is the robustness to hyperparameters choices (Hu & Greene, 2019).”
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- Authors
- Ding, J., Condon, A., & Shah, S. P.
- Affiliations
- Broad Institute (US)
- University of British Columbia (CA)
- BC Cancer Agency (CA)
- Corresponding author
- Ding, Jiarui
- Shah, Sohrab P.
- Venue
- Nature Communications
- Published
- 2018-05-21
- Volume
- 9
- Issue
- 1
- Pages
- 2002-2002
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC5962608
- OpenAlex
- W2949272108
- Cited by
- 337
- References
- 62
- Citations by year
- 2026: 12 · 2025: 32 · 2024: 38 · 2023: 50 · 2022: 48 · 2021: 52 · 2020: 49 · 2019: 47 · 2018: 8 · 2017: 1
- FWCI
- 13.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Genome Canada
- Michael Smith Health Research BC
- Canada Research Chairs
- Terry Fox Research Institute
- BC Cancer Foundation
- Canadian Institutes of Health Research (143246_1, 115170_1)
- Canadian Cancer Society Research Institute (701584)
- Natural Sciences and Engineering Research Council of Canada
- 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 RNA sequencing, dimensionality reduction, scVI, deep generative models, interpretable embeddings, cell clustering, probabilistic embedding, data augmentation, cell type discovery, cell state identification
- MeSH terms
- Algorithms, Animals, Cells, Humans, RNA, Software, Models, Statistical, Sequence Analysis, RNA, Databases, Nucleic Acid, Mice, Single-Cell Analysis, Transcriptome
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