spGCLF: a versatile deep graph contrastive learning framework for spatial transcriptomics analysis

The rapid development of spatial transcriptomics (ST) has revolutionized the study of spatial heterogeneity and increased the demand for comprehensive methods to effectively characterize spatial domains. As a prerequisite for ST data analysis, spatial domain characterization is a critical step for downstream analysis and biological interpretation. Here, we propose a deep graph contrastive learning framework (spG-CLF) for spatial transcriptomics, which leverages contrastive learning through graph convolutional networks to effectively learn features among spots, addressing the issue of poor cross-platform generalizability. By alternately performing attribute and topological denoising, our method specifically reduces noise in ST data. We demonstrate that s p GCLF is effective for spatial domain identification, trajectory inference, multi-slice integration, and type annotation.