scDMSC: Deep Multi-View Subspace Clustering for Single-Cell Multi-Omics Data.

Abstract

Single-cell multi-omics sequencing technology comprehensively considers various molecular features to reveal the complexity of cells information. The clustering analysis of multi-omics data provides new insight into cellular heterogeneity. However, multi-omics data are characterized by high dimensionality, sparsity, and heterogeneity. Here, we propose an unsupervised clustering algorithm based on deep multi-view subspace learning, called scDMSC. This approach coordinates the heterogeneity of omics data through weighted reconstruction and employs deep subspace learning to identify shared latent features, elucidating the correlations among the omics. Our algorithm was rigorously tested across multiple real and simulated datasets, outperforming existing single-cell multi-omics integration methods and standard single-cell transcriptomics clustering tools in terms of both precision and scalability. Furthermore, differential expression and modality interpretability analyses in downstream applications highlight the model's capacity in uncovering biological mechanisms.