GLoDe: Global–Local Decomposition for Histology-based Spatial Gene Expression Prediction

The high cost of spatial transcriptomics motivates scalable prediction of spatial gene expression from readily available hematoxylin and eosin (H&E) images. However, existing methods typically predict absolute gene expression without explicitly disentangling slide-level molecular shifts from within-slide spatial variation, which may cause models to capture slide-specific biases rather than transferable spatial patterns, limiting their generalization across tissue sections. To address this limitation, we propose GLoDe, a global–local decomposition framework that decomposes gene expression into a slide-shared molecular baseline and a spot-specific residual, explicitly accounting for their distinct sources of variation. The global component captures tissue-level molecular characteristics shared across spots within each tissue section, while the local component employs a gene-conditioned residual decoder to integrate gene priors, local morphology, and spatial context for predicting gene-specific residual expression. Extensive experiments on HEST-1K and HER2ST demonstrate consistent performance gains, including a 16.9% relative improvement in average Pearson correlation under out-of-fold evaluation over the strongest evaluated baseline on HEST-1K.