Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST
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Abstract
Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits spatial transcriptomics data to outperform existing methods. It combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. We demonstrated GraphST on multiple tissue types and technology platforms. GraphST achieved 10% higher clustering accuracy and better delineated fine-grained tissue structures in brain and embryo tissues. GraphST is also the only method that can jointly analyze multiple tissue slices in vertical or horizontal integration while correcting batch effects. Lastly, GraphST demonstrated superior cell-type deconvolution to capture spatial niches like lymph node germinal centers and exhausted tumor infiltrating T cells in breast tumor tissue.
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Long et al. introduced the GraphST framework, which combined GNN and contrastive learning to integrate information within spatial transcriptomic datasets, across tissue sections and scRNA-seq datasets.
“Long et al. introduced the GraphST framework, which combined GNN and contrastive learning to integrate information within spatial transcriptomic datasets, across tissue sections and scRNA-seq datasets [127].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [127]
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- Authors
- Long, Y., Ang, K. S., Li, M., Chong, K. L. K., Sethi, R., Zhong, C., Xu, H., Ong, Z., Sachaphibulkij, K., Chen, A., Zeng, L., Fu, H., Wu, M., Lim, L. H. K., Liu, L., & Chen, J.
- Affiliations
- Agency for Science, Technology and Research (SG)
- National Neuroscience Institute (SG)
- National University of Singapore (SG)
- BGI Group (China) (CN)
- Duke-NUS Medical School (SG)
- Corresponding author
- Chen, Ao
- Chen, Jinmiao
- ORCID
- Venue
- Nature Communications
- Published
- 2023-03-01
- Volume
- 14
- Issue
- 1
- Pages
- 1155-1155
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC9977836
- OpenAlex
- W4322738920
- Cited by
- 700
- References
- 55
- Citations by year
- 2026: 284 · 2025: 233 · 2024: 156 · 2023: 27
- FWCI
- 62.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation
- National Research Foundation Singapore (2017-04, NRF-CRP19-2017-04, MOH-OFLCG18May-0003)
- Medical Research Council
- National Medical Research Council (MOH-OFLCG18May-0003, NMRC /OFLCG/003/2018, NMRC-OFIRG18nov-2013)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- spatial transcriptomics, graph neural networks, self-supervised contrastive learning, spatial clustering, cell type deconvolution, batch effect correction, tumor-infiltrating T cells, breast tumors, brain tissue
- MeSH terms
- Brain, Cluster Analysis, Germinal Center, Gene Expression Profiling, Transcriptome
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