Interior-Boundary Aware Graph Representation Learning for Spatially Resolved Transcriptomic

Spatially resolved transcriptomics (SRT) can measure gene expression while preserving spatial information, providing an important tool for studying complex tissue structures and biological processes. In recent years, graph-based methods have been widely used to capture spatial relationships between spots for spatial domain identification. However, existing methods typically adopt the same strategy to model interior and boundary regions, without explicitly considering the structural differences between them. This may make local node representations overly similar and increase the mixing of information from heterogeneous neighbors. As a result, the distinctions between spatial domains can be weakened. Intuitively, interior spots and boundary spots exhibit distinct patterns in local neighborhood features and global relation structures, motivating us to distinguish these two types of spots and model their representations with different strategies. Based on this observation, we propose Boundary-aware Multi-level Relational Learning (BMRL), which estimates boundary characteristics by combining local expression differences with global semantic structure. Building on the identified interior and boundary spots, BMRL applies different representation learning constraints to them and further incorporates structural information at the region and slice levels. This allows structural information at different levels to complement each other and improves the integration of multi-slice data. Extensive experimental results show that BMRL achieves promising performance on various downstream tasks in both single-slice and multi-slice settings.