🤖 AI Summary
This study addresses the misalignment between reconstruction objectives and the identification of differentially expressed genes (DEGs) when predicting spatial gene expression from histology images. To this end, we propose the IDER framework, which introduces a novel differentiable IDER objective function. Without requiring predefined biological labels, this approach directly optimizes gene ranking consistency through morphological proxy contrast, effectively integrating deep learning with differentiable statistical alignment techniques. Experimental evaluations on public datasets demonstrate that the proposed framework significantly improves DEG ranking consistency and pathway enrichment overlap compared to conventional reconstruction methods. By achieving superior performance, IDER enables efficient and scalable translation from histology images to spatial transcriptomics, bridging the gap between image-based prediction and downstream biological discovery.
📝 Abstract
Predicting spatial gene expression from histology images could scale spatial transcriptomics (ST) to image-only cohorts, but conventional histology-based ST prediction is trained and evaluated mainly by per-gene spatial-profile reconstruction. This objective is misaligned with a key downstream use of ST: differentially expressed gene (DEG) discovery, where genes are ranked for a biological or morphology-defined contrast by evidence of between-group expression differences. We formulate image-based differential expression ranking (IDER), which asks whether predicted expression profiles preserve the contrast-specific ranked gene list obtained from measured profiles. IDER compares gene rankings induced by differential-expression statistics, rather than raw expression magnitudes or per-gene spatial correlations. We further introduce a differentiable IDER objective that aligns these statistics across genes and can be trained with morphology-derived proxy contrasts without predefined biological group labels. Experiments on public ST datasets show improved DEG-ranking agreement and pathway-enrichment overlap over conventional reconstruction objectives, including morphology-derived and pathologist-annotated tissue-region evaluations.