Spatial Gene Expression Prediction

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The field predicts gene activity at tissue spots or individual cells from H&E images, with the goal of extending costly spatial assays to more samples. Approaches combine pathology foundation-model features with local and global context from transformers or graphs, and add cell-type, gene-program, or ontology priors; some also use retrieval or generative models and sparse measured expression. Methods are commonly evaluated on HEST-1k and spatial platforms such as Visium and Xenium, using expression correlations and measures of spatial-pattern recovery, including held-out-slide or held-out-organ tests. A key challenge is generalizing across patients, tissues, and platforms while preserving spatial heterogeneity and biologically meaningful gene relationships, rather than optimizing pointwise accuracy alone.

Latest papers

39 papers6 papers in the last four weeks, up 20% on the four weeks before. 0.0% of all new papers.

CHRep: Cross-modal Histology Representation and Post-hoc Calibration for Spatial Gene Expression Prediction
Apr 23, 2026Changfan Wang, Xinran Wang, Donghai Liu +4Spatial Transcriptomics Post-Hoc Calibration
Beijing University of Posts and Telecommunications, Beijing 100876, China, and also with the Beijing Key Laboratory of Network System and Network Culture, Beijing 100876, China · Peking University Third Hospital, Beijing 100191, China
A Multimodal Foundation Model of Spatial Transcriptomics and Histology for Biological Discovery and Clinical Prediction
Apr 4, 2026Jinxi Xiang, Siyu Hou, Yuchen Li +17Spatial Transcriptomics Gene Expression Prediction from Histopathology Images
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA · Department of Statistics and Data Science, Yale University, New Haven, CT, USA · Department of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA, USA +4
Central-to-Local Adaptive Generative Diffusion Framework for Improving Gene Expression Prediction in Data-Limited Spatial Transcriptomics
Mar 27, 2026Yaoyu Fang, Jiahe Qian, Xinkun Wang +2Spatial Transcriptomics Synthetic Data Augmentation
Department of Radiology, Northwestern University. · Department of Cell and Developmental Biology, Northwestern University. · Department of Pathology, Northwestern University.
FEAST: Fully Connected Expressive Attention for Spatial Transcriptomics
Mar 26, 2026Taejin Jeong, Joohyeok Kim, Jinyeong Kim +2Spatial Gene Expression Prediction Gene Expression Prediction from Histopathology Images
Yonsei University · Emory University
Cross-Slice Knowledge Transfer via Masked Multi-Modal Heterogeneous Graph Contrastive Learning for Spatial Gene Expression Inference
Mar 24, 2026Zhiceng Shi, Changmiao Wang, Jun Wan +1Spatial Gene Expression Prediction Gene Expression Prediction from Histopathology Images
Yunnan University, Kunming 650500, China · Shenzhen Research Institute of Big Data, Shenzhen 518172, China · Zhongnan University of Economics and Law, Wuhan 430073, China
Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Images
Mar 19, 2026Kazuya Nishimura, Ryoma Bise, Shinnosuke Matsuo +2Gene Expression Prediction from Histopathology Images Spatial Gene Expression Prediction
The University of Osaka, Japan · National Cancer Center Japan · Kyushu University
BiTro: Bidirectional Transfer Learning Enhances Bulk and Spatial Transcriptomics Prediction in Cancer Pathological Images
Mar 16, 2026Jingkun Yu, Guangkai Shang, Changtao Li +4Gene Expression Prediction from Histopathology Images Spatial Gene Expression Prediction
SWJTU-Leeds Joint School, Southwest Jiaotong University, Chengdu, China · Yu-Yue Pathology Scientific Research Center, Jinfeng Laboratory, Chongqing, China · Department of Epidemiology and Medical Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China +2
MAD: Microenvironment-Aware Distillation -- A Pretraining Strategy for Virtual Spatial Omics from Microscopy
Mar 11, 2026Jiashu Han, Kunzan Liu, Yeojin Kim +2Spatial Gene Expression Prediction Self-Supervised Knowledge Distillation
Research Laboratory of Electronics, Massachusetts Institute of Technology · Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology · The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology +1
MINT: Molecularly Informed Training with Spatial Transcriptomics Supervision for Pathology Foundation Models
Mar 9, 2026Minsoo Lee, Jonghyun Kim, Juseung Yun +2Pathology Foundation Models Gene Expression Prediction from Histopathology Images
LG AI Research

Apr 23, 2026J/K move · Enter open · S save

CHRep: Cross-modal Histology Representation and Post-hoc Calibration for Spatial Gene Expression Prediction

Changfan Wang, Xinran Wang, Donghai Liu, Fei Su+3

Beijing University of Posts and Telecommunications, Beijing 100876, China, and also with the Beijing Key Laboratory of Network System and Network Culture, Beijing 100876, China · Peking University Third Hospital, Beijing 100191, China

Spatial Transcriptomics Post-Hoc Calibration Cross-Modal Representation Learning Cross-Modal Alignment Spatial Gene Expression Prediction Gene Expression Prediction from Histopathology Images

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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.