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The paper introduces HEXST, a novel Transformer architecture tailored for predicting spatial gene expression from histology images, addressing limitations of existing methods that ignore the hexagonal sampling patterns of spatial transcriptomics data. HEXST incorporates a hexagonal shifted-window attention mechanism and hexagonal rotary positional encoding to efficiently model local-to-global context. By combining point-wise regression with a contrast-sensitive differential objective and transcriptomic priors, HEXST achieves state-of-the-art performance across seven datasets, preserving gene-wise contrast and spatial heterogeneity.
Spatial transcriptomics predictions get a boost from HEXST, a Transformer that respects the hexagonal geometry of spot arrays and recovers gene-specific spatial heterogeneity.
Spatial transcriptomics offers spatially resolved gene expression profiling within tissue sections, but its cost and limited throughput hinder large-scale deployment. To extend this capability to routine practice, recent computational methods aim to infer spatial gene expression directly from ubiquitous hematoxylin and eosin-stained histology slides. However, most existing models assume Cartesian or geometry-agnostic locality, despite the hexagonal sampling of widely used spot-array platforms, and point-wise regression objectives often yield over-smoothed gene expression profiles, obscuring gene-specific spatial heterogeneity. To address these, we propose HEXST, a geometry-aligned Transformer for spatial gene expression prediction from histology. HEXST operates directly on hexagonal spot coordinates to enable efficient local-to-global contextual modeling via tailored shifted-window attention mechanism and hexagonal rotary positional encoding. To enhance gene-wise spatial contrast, HEXST complements point-wise regression with a contrast-sensitive differential objective and transcriptomic priors from a pretrained single-cell foundation model during training. Across seven spatial transcriptomics datasets, HEXST consistently outperforms state-of-the-art models, providing accurate and robust spatial gene expression predictions while preserving gene-wise contrast and spatial heterogeneity.
