Identifying Spatial Domains From Spatial Multi-Omics Data With Graph Mutual Information and Deep Subspace Learning

Spatial omics technologies enable the measurement of multiple molecular characterizations from the same tissue section while preserving spatial information, providing unprecedented opportunities to elucidate the relationship between cellular localization and tissue function. Spatial domain identification, which segments intact tissues into functionally distinct regions, is a fundamental task in spatial omics analysis. However, existing approaches are often limited to single-omics data or neglect spatial context, facing substantial limitations when extended to spatial multi-omics data. In this paper, we propose SIMID (Spatial domain Identification via graph Mutual Information and Deep subspace learning), a framework that integrates heterogeneous molecular profiles with spatial information to identify spatial domains. Specifically, a graph mutual information encoder is employed to capture cellular spatial proximity and molecular profile similarity, generating omics-specific cell embeddings for each omics layer. The deep subspace learning is then employed to construct cell network for each omics layer, converting heterogeneous multi-omics data into a homogeneous cell multi-layer network. SIMID further employs the low-rank and discriminative constraints to decompose the cell multi-layer network into consistent and complementary structures, providing an effective strategy for domain identification from spatial multi-omics data. Experimental results on both simulated and real-world spatial multi-omics datasets demonstrate that SIMID consistently outperforms existing methods and precisely reveals spatial domains from spatial multi-omics data.