Clustering of single-cell multi-omics data with a multimodal deep learning method
Open accessAbstract
Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. It provides a unique opportunity to jointly analyze multimodal data at the single-cell level for the identification of distinct cell types. A correct clustering result is essential for the downstream complex biological functional studies. However, combining different data sources for clustering analysis of single-cell multimodal data remains a statistical and computational challenge. Here, we develop a novel multimodal deep learning method, scMDC, for single-cell multi-omics data clustering analysis. scMDC is an end-to-end deep model that explicitly characterizes different data sources and jointly learns latent features of deep embedding for clustering analysis. Extensive simulation and real-data experiments reveal that scMDC outperforms existing single-cell single-modal and multimodal clustering methods on different single-cell multimodal datasets. The linear scalability of running time makes scMDC a promising method for analyzing large multimodal datasets.
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Lin et al. introduced the scMDC method, which utilized an end-to-end autoencoder model to learn joint embeddings of paired single-cell multi-omics datasets.
“For integrating multi-omics datasets from paired or unpaired single cells, Lin et al. introduced the scMDC method, which utilized an end-to-end autoencoder model to learn the joint embeddings of paired single-cell multi-omics datasets [119].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [119]
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An effective global structure-aware feature aggregation network for multi-modal medical clustering
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Attention-Fusion Model for Multi-omics (AMMO) Data Integration in Lung Adenocarcinoma
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scGEMOC, A Graph Embedded Contrastive Learning Single-cell Multiomics Clustering Model
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- Authors
- Lin, X., Tian, T., Wei, Z., & Hakonarson, H.
- Affiliations
- New Jersey Institute of Technology (US)
- Children's Hospital of Philadelphia (US)
- Corresponding author
- Wei, Zhi
- Venue
- Nature Communications
- Published
- 2022-12-13
- Volume
- 13
- Issue
- 1
- Pages
- 7705-7705
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC9748135
- OpenAlex
- W4311232381
- Cited by
- 135
- References
- 56
- Citations by year
- 2026: 45 · 2025: 43 · 2024: 39 · 2023: 7 · 2022: 1
- FWCI
- 7.35
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (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)
- Funders
- U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (UL1TR003017)
- 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 › Biophysics › Cell Image Analysis Techniques
- Keywords
- single-cell multi-omics, multimodal deep learning, cell type annotation, single-cell sequencing
- MeSH terms
- Deep Learning, Multiomics, Computer Simulation, Cluster Analysis, Single-Cell Analysis
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