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. 2022 Aug 10;608(7922):360–367. doi: 10.1038/s41586-022-05023-2

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© The Author(s) 2022

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 3 — a, UMAP summary of GEFs from 1k spatial transcriptomics experiments of prostate samples from patient 1. b, UMAP summary of GEFs from high resolution Visium experiments of prostate samples from patient 1. Top marker genes for each GEF are available in Supplementary Table 3, 4. c, Benign GEFs from b (high resolution) were used as a reference set for analysis of d, Tumour GEFS from b (high resolution). e, Snapshot of inferCNV profiles for chr 7 and 8 from GEF10. GEF inferCNV heterogeneity is highlighted by 3 subclones: the first harbouring no changes to chr 7 and 8, the second having a deletion and amplification in chr 8, and the last having alterations in both chr 7 and chr 8. While further subclustering of GEF10 spots using gene expression factors improved GEF to clone concordance, GEF to clone heterogeneity remained. f, Tumor GEFs distribution by siCNV clones (Fig. 2). GEF = Gene Expression Factor, chr = Chromosome, siCNV = spatial inferCNV.