Self-Supervised Graph-Based Refinement of Spatial Transcriptomics at Subspot Resolution

Spatial transcriptomics enables the study of gene expression within tissue context, but current sequencing-based technologies operate at a spatial resolution where each measurement aggregates signals from multiple cells. While deconvolution methods aim to estimate each spot's cell-type composition, they do not provide fine-grained spatial structure. We propose \methodname, a self-supervised framework based on graph attention networks to infer plausible subspot-level gene expression patterns from spatial transcriptomics data. The method leverages spatial neighborhood structure through message passing and introduces virtual nodes that represent latent subspot locations, enabling the redistribution of observed transcriptomic signals into finer spatial units. Importantly, our approach operates without requiring external references such as single-cell RNA-seq data or histological images. Rather than recovering true cellular-resolution measurements, Square aims to infer spatially consistent fine-grained structure that is compatible with the observed data and underlying spatial dependencies. Across synthetic, ST, Visium and Visium HD samples, we show that the inferred subspots exhibit improved spatial coherence, reduced mixing of cell-type signals, and enhanced alignment with known biological organization, while mitigating sparsity. These results suggest that graph-based modeling of spatial context can effectively refine coarse spatial transcriptomics data, providing a principled approach to studying fine-scale tissue organization when direct cellular-resolution measurements are unavailable.