Abstract Spatially resolved transcriptomics (SRT) measures transcriptomes of cells within intact biological tissues, providing unprecedented opportunities to investigate tissue micro-environments, where spatial domains are modeled as clusters of spatially neighboring cells. Current methods for the identification of spatial domain from SRT mainly rely on expression profiles and spatial coordinates of cells, which ignore intercellular interactions among them, resulting in high sensitivity and low accuracy. To bridge these gaps, we introduce a novel framework, called SiDMGF (Signal-based Domain identification with Multi-Graph Fusion), that integrates gene set-derived signaling and spatial graphs to jointly model biological context, spatial information, and gene expression of cell embedding, thereby dramatically improving accuracy and robustness of performance of algorithms for spatial domain identification. Experimental results demonstrate that SiDMGF consistently outperforms state-of-the-art methods across multiple benchmark datasets and achieves superior domain identification performance on diverse spatial sequence platforms. Furthermore, we demonstrate that the proposed SiDMGF can also be effectively applied to cancer-related tissue samples, accurately delineating micro-environment heterogeneity within tumor slice.
Ma et al. (Thu,) studied this question.