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. 2021 Aug 27;12:5172. doi: 10.1038/s41467-021-25469-8

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© The Author(s) 2021

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. 1 — a Input genome-wide copy number gain (red) and loss (blue) data from 752 newly diagnosed multiple myeloma whole genomes. b Measure copy number as classified by 6 key features. c Define the optimum number of categories for each copy number feature by a mixed-effects model (mclust). d Tally the number of CN variation for each of 28 CN categories to produce a matrix of key CN features. This comprises the input matrix for the hierarchical Dirichlet process (hdp) for de novo extraction of CN signatures.