scDGCL: A Dual-Level and Graph-Constrained Contrastive Learning Method for Single-Cell RNA Sequencing Data Clustering

Single-cell RNA sequencing (scRNA-seq) has provided unprecedented insights for life science research. In scRNA-seq data analysis, clustering is a crucial step that lays the foundation for downstream tasks. However, the high dimensionality and sparsity of scRNA-seq data lead to suboptimal representations learned by existing methods, thereby limiting clustering performance. To address these issues, we propose scDGCL, a novel dual-level and graph-constrained contrastive learning method for scRNA-seq data clustering. Specifically, we first design Dual-level Contrastive Learning (DCL), which optimizes cell representations by simultaneously considering similarities and disparities at both cell and cluster levels. Then, we design Graph-constrained Contrastive Learning (GCL), which aligns the DCL-derived representations with the graph's relational priors, further enhancing cell representations. To systematically evaluate scDGCL, we perform experiments on 12 real datasets and 8 simulated datasets. Comparing scDGCL with 17 representative clustering methods, the results demonstrate it's superiority in scRNA-seq data clustering. Ablation experiments and hyperparameter experiments are performed to verify the effectiveness of each component and the overall robustness of our method. Marker gene expression and cell trajectory inference analysis verify the biological plausibility of our method from a biological perspective.