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. 2024 Jun 21;21(9):1658–1667. doi: 10.1038/s41592-024-02316-4

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 1 — (a) Comparison of clustering results with different numbers of neighbors k to illustrate SpatialGlue’s sensitivity to parameters. (b) Supervised metrics on the clustering results. (c) Comparison of clustering results with different number of PCs to illustrate SpatialGlue’s sensitivity to input dimensionality. (d) Supervised metrics on the clustering results. (e) Comparison of clustering results with different numbers of GNN layers to illustrate SpatialGlue’s sensitivity to input dimensionality. (f) Supervised metrics on the clustering results. (g) Time complexity of SpatialGlue and competing methods (Seurat, totalVI, MultiVI, MOFA+, MEFISTO, scMM, StabMap). In the experiment, we used murine spleen dataset with 4,697 spots for RNA & Protein data, and mouse brain RNA ATAC P22 with 9,215 cells for RNA & ATAC data. (h) Time complexity of SpatialGlue with various principal components as inputs on mouse brain RNA ATAC P22.