Search papers, labs, and topics across Lattice.
CHRep is introduced to predict spatial gene expression from H&E slides, addressing limitations of existing models under leave-one-slide-out evaluation due to slide-level appearance shifts and over-smoothing. The method employs a two-phase framework: a training phase with structure-aware representation learning via correlation-aware regression, image-expression alignment, and spatial topology regularization, followed by an inference phase with a lightweight calibration module for cross-slide robustness. Results show consistent improvement in gene-wise correlation across three cohorts, particularly on Alex+10x, demonstrating superior performance compared to HAGE and mclSTExp.
H&E slides can now predict spatial gene expression with significantly improved accuracy and robustness, even when faced with unseen slide variations, thanks to a novel post-hoc calibration technique.
Spatial transcriptomics (ST) enables spatially resolved gene profiling but remains expensive and low-throughput, limiting large-cohort studies and routine clinical use. Predicting spatial gene expression from routine hematoxylin and eosin (H&E) slides is a promising alternative, yet under realistic leave-one-slide-out evaluation, existing models often suffer from slide-level appearance shifts and regression-driven over-smoothing that suppress biologically meaningful variation. CHRep is a two-phase framework for robust histology-to-expression prediction. In the training phase, CHRep learns a structure-aware representation by jointly optimizing correlation-aware regression, symmetric image-expression alignment, and coordinate-induced spatial topology regularization. In the inference phase, cross-slide robustness is improved without backbone fine-tuning through a lightweight calibration module trained on the training slides, which combines a non-parametric estimate from a training gallery with a magnitude-regularized correction module. Unlike prior embedding-alignment or retrieval-based transfer methods that rely on a single prediction route, CHRep couples topology-preserving representation learning with post-hoc calibration, enabling stable neighborhood retrieval and controlled bias correction under slide-level shifts. Across the three cohorts, CHRep consistently improves gene-wise correlation under leave-one-slide-out evaluation, with the largest gains observed on Alex+10x. Relative to HAGE, the Pearson correlation coefficient on all considered genes [PCC(ACG)] increases by 4.0% on cSCC and 9.8% on HER2+. Relative to mclSTExp, PCC(ACG) further improves by 39.5% on Alex+10x, together with 9.7% and 9.0% reductions in mean squared error (MSE) and mean absolute error (MAE), respectively.
