Clustering Single-Cell Multi-Omics Data with Graph Contrastive Learning

Single-cell multi-omics sequencing technology has made significant advances, which provides rich information for cell type identification. As a label-free data, clustering methods have been employed on multi-omics data to realize cell type identification. Although effectively integrating data from different omics facilitates the clustering tasks, most of existing methods designed for specific types of single-cell multi-omics data lack generalization capabilities. Therefore, this paper proposes a Graph Contrastive Learning framework for clustering single-cell multi-omics data (scGCL), which can be applied to various multi-omics datasets. Specifically, Our scGCL includes two modules, i.e., the Graph Generation (GG) module and the Graph Contrastive (GC) module. The GG module leverages k-nearest neighbors and random walk to build graphs based on omics data, capturing the global information and interactions among cell samples. The GC Module utilizes contrastive learning to extract discriminative feature-level and cluster-level representations. Moreover, considering the heterophily within single-cell multi-omics data, the backbone of GC module has been adjusted to enhance the representation learning. Experimental results on three single-cell multi-omics datasets demonstrate the superiority of our proposed scGCL.