Octave: Scale-Resolved Evaluation of Spatial Gene Expression Prediction from Histology

Spatial gene expression prediction from histology is typically evaluated using the mean per-gene Pearson correlation (PCC). However, PCC does not distinguish spatial scales: high performance may reflect coarse tissue organisation rather than fine-scale expression patterns. Indeed, on a widely used benchmark with approximately 100-µm sampling, a simple predictor that captures image-derived tissue regions but no within-region spatial variation achieves 82% of the PCC of a trained model. This raises a fundamental question: at what spatial scales does a model actually predict gene expression accurately? Herein, we introduce OCTAVE, a scale-resolved evaluation framework that separates predicted and measured expression into spatial scales and evaluates their agreement at each scale. On higher-resolution Xenium data, differences that appear modest under PCC become substantially larger at finer scales: at approximately 20 µm, the domain oracle’s shortfall relative to a trained model is 2.4 times its shortfall under PCC, consistently across all 13 specimens. Across 57 image encoders, OCTAVE further reveals greater performance differences and changes the ordering of 261 of 1596 encoder pairs. The benchmark’s own finest band is 50 µm wide, coarser than the scale at which these differences appear, so no score on that pitch can directly validate finer structure. These results show that PCC alone can obscure the spatial structure underlying predictive performance. OCTAVE complements existing evaluation by revealing not only how well a model predicts spatial gene expression, but also at what spatial scales it succeeds.