Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST

Yahui Long, Kok Siong Ang, Mengwei Li, Kian Long Kelvin Chong, Raman Sethi, Chengwei Zhong, Hang Xu, Zhiwei Ong, Karishma Sachaphibulkij, Ao Chen, Li Zeng, Huazhu Fu, Min Wu, Lina Hsiu Kim Lim, Longqi Liu and Jinmiao Chen ()
Additional contact information
Yahui Long: Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove
Kok Siong Ang: Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove
Mengwei Li: Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove
Kian Long Kelvin Chong: Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove
Raman Sethi: Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove
Chengwei Zhong: Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove
Hang Xu: Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove
Zhiwei Ong: National Neuroscience Institute
Karishma Sachaphibulkij: National University of Singapore (NUS)
Ao Chen: BGI
Li Zeng: National Neuroscience Institute
Huazhu Fu: Agency for Science, Technology and Research (A*STAR)
Min Wu: Agency for Science, Technology and Research (A*STAR)
Lina Hsiu Kim Lim: National University of Singapore (NUS)
Longqi Liu: BGI
Jinmiao Chen: Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove

Nature Communications, 2023, vol. 14, issue 1, 1-19

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-36796-3

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