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

Document Type

Article

Publication Date

12-1-2022

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.

Identifier

85144118816 (Scopus)

Publication Title

Nature Communications

External Full Text Location

https://doi.org/10.1038/s41467-022-35031-9

e-ISSN

20411723

PubMed ID

36513636

Issue

1

Volume

13

Grant

CIE160021

Fund Ref

National Science Foundation

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