TY - JOUR
T1 - Clustering of single-cell multi-omics data with a multimodal deep learning method
AU - Lin, Xiang
AU - Tian, Tian
AU - Wei, Zhi
AU - Hakonarson, Hakon
N1 - Funding Information:
This work was supported by grant R15HG012087 (Z.W.) from the National Institutes of Health (NIH), and partially supported by the National Center for Advancing Translational Sciences (NCATS), a component of NIH under award number UL1TR003017 (Z.W.). The computing resource was partially provided by Extreme Science and Engineering Discovery Environment (XSEDE) through allocation CIE160021 and CIE170034 (supported by National Science Foundation Grant No. ACI-1548562).
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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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U2 - 10.1038/s41467-022-35031-9
DO - 10.1038/s41467-022-35031-9
M3 - Article
C2 - 36513636
AN - SCOPUS:85144118816
SN - 2041-1723
VL - 13
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 7705
ER -