Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks
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Abstract
The rapid development of spatial transcriptomics allows the measurement of RNA abundance at a high spatial resolution, making it possible to simultaneously profile gene expression, spatial locations of cells or spots, and the corresponding hematoxylin and eosin-stained histology images. It turns promising to predict gene expression from histology images that are relatively easy and cheap to obtain. For this purpose, several methods are devised, but they have not fully captured the internal relations of the 2D vision features or spatial dependency between spots. Here, we developed Hist2ST, a deep learning-based model to predict RNA-seq expression from histology images. Around each sequenced spot, the corresponding histology image is cropped into an image patch and fed into a convolutional module to extract 2D vision features. Meanwhile, the spatial relations with the whole image and neighbored patches are captured through Transformer and graph neural network modules, respectively. These learned features are then used to predict the gene expression by following the zero-inflated negative binomial distribution. To alleviate the impact by the small spatial transcriptomics data, a self-distillation mechanism is employed for efficient learning of the model. By comprehensive tests on cancer and normal datasets, Hist2ST was shown to outperform existing methods in terms of both gene expression prediction and spatial region identification. Further pathway analyses indicated that our model could reserve biological information. Thus, Hist2ST enables generating spatial transcriptomics data from histology images for elucidating molecular signatures of tissues.
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Several studies have explored predicting gene expression from H&E images using AI models.
“Several studies have explored predicting gene expression from H&E images using AI models59–62.”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [60]
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A visual–omics foundation model to bridge histopathology with spatial transcriptomics
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- Authors
- Zeng, Y., Wei, Z., Yu, W., Yin, R., Yuan, Y., Li, B., Tang, Z., Lu, Y., & Yang, Y.
- Affiliations
- Sun Yat-sen University (CN)
- Corresponding author
- Yang, Yuedong
- ORCID
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-07-17
- Volume
- 23
- Issue
- 5
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W4285803722
- Cited by
- 211
- References
- 79
- Citations by year
- 2026: 76 · 2025: 69 · 2024: 44 · 2023: 18 · 2022: 4
- FWCI
- 11.02
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Briefings in Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Griffith Research Online (submittedVersion, other-oa)
- Funders
- National Key Research and Development Program of China (2020YFB0204803)
- National Natural Science Foundation of China (12126610)
- Guangdong Key Field R&D Plan (2019B020228001, 2018B010109006)
- Introducing Innovative and Entrepreneurial Teams (2016ZT06D211)
- Guangzhou Science and Technology Research Plan (202007030010)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- spatial transcriptomics, histological images, transformer models, graph neural networks, spatial dependence, zero-inflated negative binomial regression, self-distillation, tumor tissue, pathway analysis
- MeSH terms
- Eosine Yellowish-(YS), Hematoxylin, Image Processing, Computer-Assisted, RNA, Neural Networks, Computer, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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