HyBoxST: Coupling Hyperbolic Spatial Representations with Molecular Program Boxes for Transcriptomics Prediction from Histology

Predicting spatial gene expression from histology provides a scalable way to profile tissue molecular states. Although existing methods increasingly capture tissue morphology and spatial context, they rarely model the pathway structure among predicted genes explicitly. This leaves known relationships among functionally related genes underused when learning their expression from tissue morphology. This makes it difficult for these models to accurately recover coordinated expression changes among genes. Here we propose HyBoxST, which incorporates gene-to-pathway memberships into expression prediction through a pathway-guided structured decoder. HyBoxST represents pathways as learnable boxes and genes as points in a shared molecular latent space, so that pathway membership can be expressed through containment and shared membership through overlap between pathway boxes. For each spot, features from the spot image and its surrounding niche generate a query box. Its overlap with pathway boxes is used to adjust directly predicted pathway expression scores according to the local tissue context. For each gene, the decoder aggregates the corrected scores from pathways to which the gene belongs, while a residual branch captures expression variation beyond the selected pathways. Across four tissue datasets, HyBoxST consistently improves gene expression prediction. Additional controls with randomized gene-pathway memberships and spatial analyses of pathway signals show that the improvement relies on the biological pathway structure rather than the mask structure alone.