scGDCC: Graph-based Dual Contrastive Calibration for Single Cell MultiOmics Clustering

Cell clustering is vital for studying cellular heterogeneity and understanding biological mechanisms. With the advancement of sequencing technologies, it is now possible to obtain multiomics data from single cells, such as ATAC-seq and RNA-seq. Compared to single-omics data, multiomics data offer a more comprehensive view of the cellular landscape. Although several single-cell multiomics clustering methods have been developed, the sparsity and complexity of multiomics data make clustering a challenging computational task. This paper proposes a single cell multiomics clustering method called scGDCC, which is based on graph neural networks and dual contrastive calibration. scGDCC utilizes graph neural networks to capture the neighborhood information of cells and employs dual contrastive calibration to achieve more consistent joint representations of cells. Experiments on five dual-omics datasets (ATAC-seq and RNA-seq) and two triple-omics datasets (ATAC-seq, RNA-seq, and protein) demonstrate the superiority of this clustering method. Additionally, visualization experiments further validate the effectiveness of the joint cellular representations.