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Cell clustering for spatial transcriptomics data with graph neural networks

Abstract

Spatial transcriptomics data can provide high-throughput gene expression profiling and the spatial structure of tissues simultaneously. Most studies have relied on only the gene expression information but cannot utilize the spatial information efficiently. Taking advantage of spatial transcriptomics and graph neural networks, we introduce cell clustering for spatial transcriptomics data with graph neural networks, an unsupervised cell clustering method based on graph convolutional networks to improve ab initio cell clustering and discovery of cell subtypes based on curated cell category annotation. On the basis of its application to five in vitro and in vivo spatial datasets, we show that cell clustering for spatial transcriptomics outperforms other spatial clustering approaches on spatial transcriptomics datasets and can clearly identify all four cell cycle phases from multiplexed error-robust fluorescence in situ hybridization data of cultured cells. From enhanced sequential fluorescence in situ hybridization data of brain, cell clustering for spatial transcriptomics finds functional cell subtypes with different micro-environments, which are all validated experimentally, inspiring biological hypotheses about the underlying interactions among the cell state, cell type and micro-environment.

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Fig. 1: CCST workflow for cell subpopulation discovery.
Fig. 2: Spatial distribution of cells in different clustered groups in MERFISH dataset.
Fig. 3: Cell cycle phase identification.
Fig. 4: Performance of CCST on two annotated datasets.
Fig. 5: Identifying cell subgroups in interneuron cells of the seqFISH+ mouse OB dataset.
Fig. 6: Neighbour enrichment ratios and GO term analysis for each cell subtype of astrocytes, endothelial cells, and neural stem cells of the seqFISH+ mouse OB dataset.

Data availability

Source data for Figs. 26 are available with this manuscript. The datasets utilized in this study can be downloaded from: (1) MERFISH dataset5: https://www.pnas.org/doi/10.1073/pnas.1912459116#supplementary-materials or our Github link: https://github.com/xiaoyeye/CCST/tree/main/dataset; (2) SeqFISH+ dataset35: https://github.com/CaiGroup/seqFISH-PLUS; (3) DLPFC dataset37: http://research.libd.org/globus/jhpce_HumanPilot10x/index.html; (4) 10× Visium spatial transcriptomics data of human breast cancer: https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Breast_Cancer_Block_A_Section_1. The annotation file can be found on the SEDR32 website: https://github.com/JinmiaoChenLab/SEDR_analyses/tree/master/data/BRCA1.

Code availability

CCST is implemented in Python. The source code and the utilized MERFISH dataset can be downloaded from the supporting website: https://github.com/xiaoyeye/CCST. https://doi.org/10.5281/zenodo.6560643 (ref. 50).

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Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (no. 61725302 to H.S.), 62073219 (to H.S.), 62103262 (to Y.Y.) and 61903248 (to X.P.) and the Shanghai Pujiang Programme (no. 21PJ1407700 to Y.Y.).

Author information

Authors and Affiliations

Authors

Contributions

H.S. and Y.Y. conceived and supervised the study. Y.Y. designed experiments. J.L. developed the computational model and conducted data analysis. Y.Y., H.S. and X.P. provided advice on data analysis. Y.Y. and S.C. proposed the proper computational model. J.L. drafted the manuscript. Y.Y. and H.S. revised the manuscript.

Corresponding authors

Correspondence to Ye Yuan or Hong-Bin Shen.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Computational Science thanks Xin Zhou and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Handling editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Comparison on sample 151676 of DLPFC.

Annotation and cluster labels obtained by CCST and prior methods on sample 151676 of DLPFC. Metrics including ARI, FMI and NMI are annotated on the bottom of each figure. Numbers in the legend refer to cluster labels.

Source data

Extended Data Fig. 2 Comparison on 10x Visium spatial transcriptomics data of human breast cancer.

Annotation and cluster labels obtained by CCST and prior methods on 10x Visium spatial transcriptomics data of human breast cancer. Metrics including ARI, FMI and NMI are annotated on the bottom of each figure. Numbers in the legend refer to cluster labels.

Source data

Supplementary information

Supplementary information Comparison on 10x Visium spatial transcriptomics data of human breast cancer.

Supplementary Figs. 1–30, Sections 1–15 and Tables 1–4.

Reporting summary

Supplementary Data 1. Comparison on 10x Visium spatial transcriptomics data of human breast cancer.

The top 200 significantly DE genes of each cell group obtained by CCST on the MERFISH dataset.

Source data

Source Data Fig. 2.

Statistical source data of each subfigure is listed in each sheet.

Source Data Fig. 3.

Statistical source data of each subfigure is listed in each sheet.

Source Data Fig. 4.

Raw data of boxplots is listed in each sheet.

Source Data Fig. 5.

Statistical source data of each subfigure is listed in each sheet.

Source Data Fig. 6.

Statistical source data of each subfigure is listed in each sheet.

Source Data Extended Data Fig. 1.

Cell cluster labels.

Source Data Extended Data Fig. 2.

Cell cluster labels.

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Li, J., Chen, S., Pan, X. et al. Cell clustering for spatial transcriptomics data with graph neural networks. Nat Comput Sci 2, 399–408 (2022). https://doi.org/10.1038/s43588-022-00266-5

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