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. 2020 Jun 5;5:26. doi: 10.1038/s41525-020-0133-4

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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. 1 — a MutSpot analysis workflow. b, c For each analysis, MutSpot outputs three types of descriptive figures: a Manhattan plot, a feature importance plot of features evaluated by the background mutation model, and lollipop plots of the top hotspots. Figures produced by MutSpot from b a genome-wide analysis and c a CBS-specific analysis of 168 gastric cancer whole genomes. Hotspots with FDR <0.05 are labeled in magenta. d, e Comparison of the number of hotspots detected using MutSpot with the number of hotspots detected using other statistical approaches in d the genome-wide and e CBS-specific analyses.