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

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. 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. See details in our paper: " scDEBGCL: a deep embedding approach based on bipartite graph contrastive learning for single-cell RNA-seq data".