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arXiv · 2609.33928

Preserving DEG Rankings for Gene Discovery in Histology-Based Spatial Gene Expression Prediction

Kaito Shiku·Kazuya Nishimura·Yasuhiro Kojima·Ryoma Bise
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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.

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Kaito Shiku, Kazuya Nishimura, Yasuhiro Kojima, Ryoma Bise. 2026-09-27. Preserving DEG Rankings for Gene Discovery in Histology-Based Spatial Gene Expression Prediction. https://arxiv.org/abs/2609.33928

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