SpaMGAN: Multi-view graph augmentation network for spatial domain identification in spatial transcriptomics

Recent advancements in spatial transcriptomics have made it possible to profile gene expression while maintaining the spatial organization of cells, opening new avenues for exploring tissue microenvironments. However, integrating spatial and gene expression data to accurately identify spatial domains remains challenging. In this study, we present SpaMGAN as a multi-view graph augmentation network for spatial domain identification in spatial transcriptomics. The model constructs a spatial neighborhood graph by combining spot spatial proximity with cosine-weighted gene expression similarity. A pre-clustering pruning strategy generates a cell-type-aware K-nearest neighbor graph to better capture spatial similarity at domain boundaries. These graphs are merged into a weighted adjacency matrix. To enhance robustness and generalization, SpaMGAN incorporates adjacency matrix weighting, node shuffling, and feature masking. Using a consistency-based contrastive strategy, multiple augmented graph views are processed through graph convolution layers, and feature representations are fused via an attention mechanism. Evaluated on four datasets from three spatial transcriptomics platforms, SpaMGAN outperforms eight advanced methods. Specifically, the algorithm achieved the highest adjusted rand index scores of 0.594 and 0.585 on the datasets of the human dorsolateral prefrontal cortex and mouse visual cortex, respectively. In breast cancer tissue, SpaMGAN effectively reveals spatial heterogeneity, offering insights into the tumor microenvironment. On large-scale datasets such as mouse embryos, it identifies major anatomical regions and uncovers biologically meaningful domains enriched in developmental processes. Overall, SpaMGAN demonstrates strong scalability and biological interpretability, making it a powerful tool for analyzing tissue structure and disease mechanisms in spatial transcriptomics.