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. 2020 Feb 5;11:715. doi: 10.1038/s41467-020-14605-5

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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. 2 — a Transcriptional adaptation to CNA (TACNA) profiling. For each dataset, weights of genes mapping to a contiguous genomic region in CNA-CESs marked by the detection algorithm were retained in the CES matrix. Weights of genes that mapped outside marked genomic regions were instead set to zero. Next, TACNA profiles were calculated as the product between this transformed CES matrix and the consensus mixing matrix. b Examples of TACNA profiles illustrating that the resulting patterns clearly matched those in their paired, independently generated CNA profiles. c Left panel: distribution of Pearson correlations between TACNA profiles and paired CNA profiles for the TCGA, CCLE, and GDSC datasets. Right panel: variance observed in CNA profiles versus the Pearson correlation between CNA profiles and paired TACNA profiles for the TCGA, CCLE, and GDSC datasets. d Quartiles distribution plot of Pearson correlations between TACNA profiles and paired CNA profiles, per tumor type in the TCGA dataset. ER: estrogen receptor; HER2: human epidermal growth factor receptor 2; PR: progesterone receptor.