Clustering of single-cell multi-omics data with a multimodal deep learning method

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 resul...

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Bibliographic Details
Published in:Nature communications 2022-12, Vol.13 (1), p.7705-7705, Article 7705
Main Authors: Lin, Xiang, Tian, Tian, Wei, Zhi, Hakonarson, Hakon
Format: Article
Language:English
Subjects:
49/111
49/39
49/91
631/114/1305
631/114/2397
Biological analysis
Cluster Analysis
Clustering
Computer applications
Computer Simulation
Data analysis
Data sources
Datasets
Deep Learning
Embedding
Humanities and Social Sciences
multidisciplinary
Multiomics
Science
Science (multidisciplinary)
Single-Cell Analysis
Teaching methods
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Summary: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. Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. Here the authors develops a multimodal deep clustering method for the analysis of single-cell multi-omics data that supports clustering different types of multi-omics data and multi-batch data, as well as downstream differential expression analysis.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-022-35031-9