scDEBGCL: a deep embedding approach based on bipartite graph contrastive learning for single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) allows for the measurement of gene expression at the transcriptomic level with single-cell precision, thereby deepening our comprehension of cellular heterogeneity. However, the high dimensionality and sparsity of scRNA-seq data impede downstream analyses (such as cell clustering and trajectory inference), and learning effective embedded representations of the data has become a key aspect in scRNA-seq data analysis.

We present scDEBGCL, a novel deep embedding algorithm based on bipartite graph contrastive learning. scDEBGCL leverages Singular Value Decomposition (SVD) for bipartite graph enhancement and integrates graph contrastive learning, graph reconstruction, and data reconstruction to jointly learn low-dimensional embedded representations of cells, which are used for downstream tasks such as cell clustering, trajectory inference, and marker gene identification. Specifically, scDEBGCL first converts the gene expression matrix into a cell-gene bipartite graph and applies SVD to this bipartite graph for graph enhancement. This strategy effectively preserves the global cell-gene interactions and facilitate the learning of global synergistic signals within the data. To further capture the discriminative cellular representations, scDEBGCL performs contrastive learning between the enhanced graph and the original bipartite graph. Then, scDEBGCL integrates the contrastive learning loss, bipartite graph reconstruction loss, and ZINB distribution-based reconstruction loss to jointly optimize and learn the low-dimensional representations of cells for downstream analyses such as cell clustering, cell trajectory inference, and marker gene identification.

Experiments results demonstrate that scDEBGCL is a useful GCL framework for deep embedding in scRNA-seq data, providing a reliable foundation for various downstream analyses.