Empowering multifaceted analysis of spatial transcriptomics data with RGAST

Spatial transcriptomics (ST) enables mapping gene expression in native tissue context to resolve architecture and cellular interactions, but current analytical workflows rely on separate algorithms for distinct tasks. We present RGAST (Relational Graph Attention network for ST analysis), a framework that builds upon and extends our earlier HERGAST model (specifically designed for large-scale ST data analysis) for diverse downstream analysis. By introducing a relational graph attention auto-encoder, RGAST jointly models spatial proximity and gene expression similarity to capture both local and global structures in ST data. This design enables a wide range of downstream tasks within a single framework. Through comprehensive benchmarking, RGAST demonstrates superior performance in spatial domain identification across multiple platforms, improving adjusted rand index by ~10% compared to the second-best model in the dorsolateral prefrontal cortex dataset. RGAST accurately reconstructs known neuroglial interaction patterns in the mouse hypothalamus, including long-range signaling pathways that are often missed by distance-constrained methods. Moreover, RGAST also excels in boosting spatially variable gene identification accuracy, delivering more precise inference of developmental trajectories in the human cortex, and robust reconstruction of 3D tissue architectures from serial sections. Collectively, these results establish RGAST as a powerful tool for providing coherent solution to advance ST data analysis across multiple research scenarios.