# STRATA

Volumetric spatial transcriptomics is expensive, and fixed-grid sampling measures redundant spots while missing informative regions in 3D tissue. STRATA co-optimizes sampling and prediction under a budget: on a 3D graph combining H&E morphology with single-cell priors, a reinforcement-learning agent picks morphologically sparse, transcriptomically informative spots, and a predictor fuses retrieval, regression and cross-layer imputation with per-spot weights. Across HER2, Breast, cSCC and a private Kidney cohort, STRATA has the highest PCC and lowest MSE; on HER2 its PCC is 0.361 and MSE 0.356, while the strongest-baseline figures stated in the text disagree with the table's baseline values, so that comparison is not quoted here.

Volumetric spatial transcriptomics is expensive, and fixed-grid sampling measures redundant spots while missing informative regions in 3D tissue. STRATA co-optimizes sampling and prediction under a budget: on a 3D graph combining H&E morphology with single-cell priors, a reinforcement-learning agent picks morphologically sparse, transcriptomically informative spots, and a predictor fuses retrieval, regression and cross-layer imputation with per-spot weights. Across HER2, Breast, cSCC and a private Kidney cohort, STRATA has the highest PCC and lowest MSE; on HER2 its PCC is 0.361 and MSE 0.356, while the strongest-baseline figures stated in the text disagree with the table's baseline values, so that comparison is not quoted here.