SSMGCN: Multi-View Graph Clustering with Shared-Specific Information Modelling for Spatially Resolved Transcriptomics

The rapid development of Spatial Transcriptomics (ST) enables simultaneous acquisition of gene expression and spatial locations, offering new avenues to explore tissue organization. However, effectively integrating spatial and transcriptional information for spatial domain identification remains challenging in existing methods. Specifically, most existing methods construct graphs using a single spatial similarity metric, making results sensitive to metric choice. Even methods that build dual graphs from spatial and expression views often fail to disentangle shared and view specific information systematically, which limits their robustness in complex biological contexts. To this end, a novel Shared-Specific Multi-view Graph Convolutional Network (SSMGCN) is proposed. SSMGCN first constructs spatial and gene-expression graphs. Subsequently, to fully exploit the shared and specific information between the two types of graphs, a shared-specific information decoupling autoencoder framework is proposed based on the graph convolutional network. In this framework, we first employ a shared encoder and view specific encoders to capture common and unique knowledge across views. In the decoding phase, a zero-inflated negative binomial (ZINB) decoder is applied to the expression data to model zero inflation and over-dispersion, while two structure decoders are assigned to the spatial and expression graphs to simultaneously reconstruct their adjacency relationships in the latent space, thereby preserving local topological continuity and long-range functional connectivity. Finally, a Student's t-distribution-based clustering is adopted in the embedding space, which leverages soft assignments and KL divergence-based self-training to enhance intra-cluster compactness and inter-cluster separation, thereby yielding discriminative representations for spatial domain partition. Extensive experiments on diverse ST datasets demonstrate that SSMGCN consistently outperforms existing methods in spatial domain identification. Moreover, its unified embedding supports downstream tasks such as cell-type annotation, spatial localization, and functional analysis, providing a robust foundation for mechanistic exploration. The code and dataset of this study are available at https://github.com/ddddoreen/SSMGCN.