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. 2020 Mar 11;11:1310. doi: 10.1038/s41467-020-15155-6

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© The Author(s) 2020

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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Fig. 5 — a t-SNE representation of unsupervised graph-based clustering of paediatric kidney cancer organoids and tissues gene expression profiles, demonstrating a disease-based separation for the three main tumour types (RCC, MRTK and Wilms tumour) and a composition-based separation for the most prevalent one, Wilms tumour. b t-SNE maps, as in a, showing the colour-coded logged expression levels of several markers used in the clinic or separating the different populations. c, d Depicted are fusion transcripts detected in tRCC-derived organoids 107T (c) and 71T (d) with their chromosomal location and exon structure and a schematic representation of the fusion breakpoint. Coverage track of the fusion genes is included at the bottom, indicating RNA expression levels. The number above the red arc represents the sequencing reads that support the fusion event. e t-SNE analysis was performed using the top 2000 most variably methylated CpG sites in paediatric kidney cancer organoids and tissues, and revealed that organoids cluster with the tumour entity they were derived from.