ENGGT: An Edge-Node Guided Graph Transformer for Spatial Domain Parsing with Accurate Boundaries
Spatial transcriptomics (ST) enables the simultaneous measurement of high-throughput gene expression and spatial structural information, offering a powerful means to decipher tissue heterogeneity. However, current spatial domain identification methods struggle to accurately distinguish continuous biological boundaries, such as smooth tissue transitions or invasive tumor margins. To overcome this challenge, we propose ENGGT, an edge-node guided graph transformer framework that identifies spatial domains with accurate biological boundaries by modeling interactions between node and edge representations. ENGGT employs a dual-branch architecture to capture spatial patterns. Specifically, an edge transformer branch encodes edge features to characterize the spatial and expression relationships of spots pairs within a local tissue microenvironment. It incorporates a topology-aware edge masking strategy to prune unreliable connections and enhance boundary sensitivity. A node transformer branch then integrates the learned edge representations as local attention biases into a global self-attention module, promoting intra-region consistency while limiting error propagation across biological boundaries. Evaluated on ST datasets from multiple measurement platforms, ENGGT consistently outperforms seven state-of-the-art spatial domain identification methods across all evaluation metrics. Moreover, the learned edge-bias matrix offers traceable biological interpretability and enables biological boundary localization. ENGGT provides a robust and interpretable tool for spatial transcriptomics analysis by identifying spatially coherent tissue structures and delineating precise boundaries.