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. 2020 Sep 8;11:4469. doi: 10.1038/s41467-020-18169-2

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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. 3 — a Evaluation of prediction accuracy for 1A. Tumor purity, 1B. Number of subclones, 1C. Proportion of each subclone, 2A. Binary assignment of SNVs, 2B. Confusion matrix of co-occurrence of two SNVs in a subclone, 3A. Phylogeny tree, 3B. Tree relationship of individual SNVs. In the boxplots, black dots refer to each tumor sample, center lines refer to median performance, bounds of box refer to the first quartile and the third quartile of the data, and whiskers refer to min and max of the data. b Comparison of FastClone performance with other algorithms. Median scores of other algorithms are calculated from all entries of other teams on the leaderboard of each sub-challenge. Sub3A and Sub3B are not included in this comparison because there are no other entries on the leaderboard. c Example of inferred tumor heterogeneity versus ground truth. “Sub” refers to subclones and “Nor” refers to normal cell infusion. d The performance scores of FastClone and PyClone on predicting subclone numbers.