A cell clustering model for spatial transcripts that uses cell embedding obtained by graph neural networks can be applied to datasets from multiple platforms for cell type or subpopulation identification and further analysis of the spatial microenvironment.
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References
Xiaowei, A. C. Method of the Year 2020: spatially resolved transcriptomics. Nat. Methods 18, 1 (2021). A Review article that reports spatial transcriptomics as the Method of the Year 2020.
Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022). A Review article that presents the history and recent progress in spatial transcriptomics.
Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl Acad. Sci. USA 116, 19490–19499 (2019). This paper reports the MERFISH pipeline.
Hu, J. et al. SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods 18, 1342–1351 (2021). This paper reports SpaGCN, a graph convolutional network approach to identify spatial domains by coherent expression and histology information.
Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. 39, 1375–1384 (2021). This paper reports BayesSpace, a Bayesian statistical method for resolution enhancement and clustering on spatial transcriptomics.
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This is a summary of: Li, J. et al. Cell clustering for spatial transcriptomics data with graph neural networks. Nat. Comput. Sci. https://doi.org/10.1038/s43588-022-00266-5 (2022).
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Graph embedding enables cell identification from spatial transcripts. Nat Comput Sci 2, 422–423 (2022). https://doi.org/10.1038/s43588-022-00272-7
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DOI: https://doi.org/10.1038/s43588-022-00272-7
