mlsanityData to 14 Oct 2026

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

arXiv 2609.33928 ↗First posted 27 Sep 2026cs.CVcited by 0

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Abstract (arXiv)

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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References · 16

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2024HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysiscited by 107
  3. 2023Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Predictioncited by 92
  4. 2023Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learningcited by 79
  5. 2024Accurate Spatial Gene Expression Prediction by integrating Multi-resolution featurescited by 52
  6. 2022Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Predictioncited by 39
  7. 2025Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Imagescited by 37
  8. 2025Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matchingcited by 19
  9. 2024MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Imagescited by 17
  10. 2024M2ORT: Many-To-One Regression Transformer for Spatial Transcriptomics Prediction from Histopathology Imagescited by 8
  11. 2026HistoPrism: Unlocking Functional Pathway Analysis from Pan-Cancer Histology via Gene Expression Predictioncited by 5
  12. 2024RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistencycited by 4
4 more
  1. 2025Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomicscited by 3
  2. 2025PEaRL: Pathway-Enhanced Representation Learning for Gene and Pathway Expression Prediction from Histologycited by 2
  3. 2026Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Imagescited by 2
  4. 2025Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selectioncited by 1

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