EPS quantifies the predictability of gene expression from histology images

Histology images and transcriptomic profiles capture complementary aspects of tissue biology: morphology and molecular state, respectively. Recent advances in multimodal learning have enabled models to predict the expression of thousands of genes directly from volumetric H&E images. However, the fundamental predictability of individual genes from histology—and hence the intrinsic limits of such models—remains poorly understood. Here, we introduce the Expression Predictability Score (EPS), a model-agnostic metric for quantifying the extent to which the expression of each gene can be inferred from another modality. Under a constrained Gaussian Markov random field model, we establish a theoretical connection between EPS and mutual information. In simulations where mutual information is analytically tractable, EPS closely tracks the true mutual information between modalities. Across diverse spatial omics datasets and tissue types, EPS consistently distinguishes highly predictable from poorly predictable genes, and its relationship with empirical prediction performance remains stable across substantially different predictive models. Genes with high and low EPS further exhibit distinct enrichment patterns across cellular components, revealing biological determinants of cross-modal predictability. Beyond histology-to-transcriptome inference, EPS shows consistent score–performance relationships when predicting RNA expression from surface proteins or chromatin accessibility. Together, these results establish EPS as a general framework for characterizing cross-modal information content, identifying predictable molecular features, and delineating the intrinsic scope of multimodal inference.