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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
Published on: September 5, 2025
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FmH2ST: foundation model-based spatial transcriptomics generation from histological images
Yuequn Wang1,2, Jun Wang2, Yanyu Xu2
1School of Software, Shandong University, Jinan 250101, Shandong, China.
Nucleic Acids Research
|September 9, 2025
Summary
We developed FmH2ST, a foundation model for spatial gene expression prediction. This method improves accuracy by integrating prior knowledge and fine-grained details, outperforming existing approaches.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Spatial transcriptomics (ST) provides tissue-level gene expression data.
- Predicting spatial gene expression from histology faces challenges like limited data and capturing complex tissue heterogeneity.
Purpose of the Study:
- To introduce FmH2ST, a novel foundation model-based method for enhanced spatial gene expression prediction.
- To address limitations in current ST data and improve the accuracy of gene expression mapping.
Main Methods:
- FmH2ST utilizes a dual-branch framework integrating foundation models and spot-specific learning.
- It employs multilevel feature extraction, dual-graph strategies, multiscale convolutions, Transformers, and graph neural networks.
- Adaptive fusion of features from both branches enhances prediction accuracy.
Main Results:
- FmH2ST significantly outperforms state-of-the-art methods on benchmark datasets.
- The model effectively denoises gene expression data and reveals spatial heterogeneity in cancer.
- It identifies gene co-expression patterns and supports gene regulatory network inference.
Conclusions:
- FmH2ST is a highly effective method for spatial gene expression prediction.
- The approach has potential applications in clinical diagnostics and personalized medicine through improved ST analysis.
