MoSCHG: Multi-omics Single-cell Classification based on Heterogeneous Graphs and Supervised Contrastive Learning.

Single-cell classification based on single-omics data is often constrained by the one-sidedness of the data. With the advancement of single-cell sequencing technology, it has become possible to classify single cells using multi-omics data. However, integration and classification of multi-omics data are still challenging. In this study, we propose a model named MoSCHG. In this model, we first construct a heterogeneous bipartite graph based on the data of each omics, where the two types of nodes represent cells and their features (e.g., genes, chromatin) respectively, and the edge weights represent the relationship between cells and features; then GCN is applied with a residual mechanism to learn node embeddings in each bipartite graph and cell embeddings from different graphs are aligned based on supervised contrastive learning; finally, the aligned multi-omics cell embeddings are concatenated and the classification task is completed. Experimental results on three real datasets show that the proposed MoSCHG model outperforms the current state-of-the-art algorithms in classification performance, and through ablation studies, we validate the effectiveness of each module in MoSCHG.