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

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

Nature Communications

10.1038/s41467-023-36796-3

https://doi.org/10.1038/s41467-023-36796-3

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, Jinmiao Chen

01 March 2023

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. (2023). Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nature Communications, 14(1). https://doi.org/10.1038/s41467-023-36796-3

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.

Attribution 4.0 International (CC BY 4.0)

The work was supported by AI, Analytics and Informatics (AI3) Horizontal Technology Programme Office (HTPO) seed grant (Spatial transcriptomics ST in conjunction with graph neural networks for cell–cell interaction #C211118015) from A * STAR, Singapore; Open Fund Individual Research Grant by National Medical Research Council (Mapping hematopoietic lineages of healthy and high-risk acute myeloid leukemia patients with FLT3-ITD mutations using single-cell omics #OFIRG18nov-0103) from Ministry of Health, Singapore; Singapore National Research Foundation grant #NRF-CRP19-2017-04; Industry Alignment Fund (Pre-Positioning) grant (SinGapore ImmuNogrAm for ImmunoOncoLogy #IAF-PP H19/01/a0/024) from A*STAR and the National Research Foundation, Singapore; the National Research Foundation, Singapore, and Singapore Ministry of Health’s National Medical Research Council under its Open Fund-Large Collaborative Grant (“OF-LCG”) (#MOH-OFLCG18May-0003); Singapore National Medical Research Council (#NMRC/OFLCG/003/2018); Singapore A*STAR Central Research Fund.

https://oar.a-star.edu.sg/communities-collections/articles/23363

2041-1723

Institute of High Performance Computing 

https://doi.org/10.1038/s41467-023-36796-3