Dual-graph attention autoencoder for spatial domain identification in ischemic stroke

Spatial transcriptomics enables molecular mapping of ischemic stroke tissue, but spatial domain identification is challenging when injury disrupts normal tissue geometry. Methods relying on a single spatial-proximity graph cannot connect physically distant spots that share damage-associated transcriptional programs.

We developed SpatialDomainAE, an unsupervised dual-graph attention autoencoder that constructs separate spatial-neighbor and expression-similarity graphs, processes each using graph attention, and combines their embeddings through learned per-spot fusion weights. The method was evaluated on a mouse middle cerebral artery occlusion 10× Visium dataset comprising control, 1-, 3-, and 7-day post-injury sections, totaling 10,173 spots and 22 annotated domains. All methods were evaluated over 10 random seeds using a shared Leiden-resolution protocol.

At 3 days post-injury, SpatialDomainAE achieved an ARI of 0.700 ± 0.025 and significantly exceeded all external baselines, including the dual-view Spatial-MGCN. Across all four samples, its performance was competitive rather than uniformly superior, and it was robust under a fixed clustering resolution. No comparable advantage was observed on an external human dorsolateral prefrontal cortex benchmark. Controlled experiments showed that the long-range transcriptomic content of the feature graph, rather than edge length alone, accounted for the improvement. Fusion weights separated lesion-associated domains at the region level, while differential expression and pathway enrichment recovered inflammatory, complement, gliosis, and proliferative programs.

SpatialDomainAE is particularly useful in disrupted pathological tissue. Its fusion weights should be interpreted as an exploratory region-level model diagnostic rather than a validated spot-level biomarker.