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

Xiang Lin, Tian Tian, Zhi Wei, Hakon Hakonarson

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

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.

Original languageEnglish (US)
Article number7705
JournalNature communications
Volume13
Issue number1
DOIs
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy

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