GatorTrio: Topology-Refined Tri-View Graph Learning for Spatial Domain Identification in Spatial Transcriptomics

Abstract

Spatial domain identification in spatial transcriptomics (ST) partitions a tissue section into spatially coherent regions with distinct transcriptional programs. Recent graph-based approaches construct a spatial neighborhood graph and/or an expression-similarity graph and perform unsupervised domain identification with GNN encoders or graph-regularized embeddings. However, dropout and boundary mixing make neighborhood reliability highly non-uniform across space, so fixed graphs can propagate signals across true boundaries and blur domains, motivating unit-wise multi-view fusion and topology refinement. We propose GatorTrio, a topology-refined tri-view graph learning framework that adaptively fuses views per unit via an interaction-aware sparse Mixture-of-Experts (MoE) router. GatorTrio constructs three complementary graphs: (i) an attention-induced expression-affinity graph, (ii) a dropout-aware expression-similarity graph, and (iii) a spatial kNN neighborhood graph. A parameter-sharing GNN encoder encodes each graph, and the router fuses the resulting view-specific embeddings; view dropout and learnable missing-view tokens improve robustness to noisy and missing views. We refine topology through self-training by pruning pseudo-label–inconsistent inter-cluster edges and re-encoding on the refined graphs. Training combines bidirectional topology-informed contrastive learning between expression views with spatially anchored prototype alignment that matches expression-view centroids to perturbed spatial prototypes under a curriculum warm-up. Experiments on diverse ST benchmarks show consistent improvements over ten representative baselines and support downstream biological analyses.