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

Xiang Lin, Tian Tian, Zhi Wei () and Hakon Hakonarson
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Xiang Lin: New Jersey Institute of Technology
Tian Tian: Children’s Hospital of Philadelphia
Zhi Wei: New Jersey Institute of Technology
Hakon Hakonarson: Children’s Hospital of Philadelphia

Nature Communications, 2022, vol. 13, issue 1, 1-18

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-022-35031-9

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