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. 2018 Oct 8;8:14924. doi: 10.1038/s41598-018-32682-x

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© The Author(s) 2018

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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Network-based subtype stratification using PRECISE. (a) Proportions of tumor types represented in each cluster. (b) Heatmap depicting combined activated and suppressed PRECISE scores after unsupervised hierarchical clustering of the score matrix consisting of 6,844 patients across all cancer lineages and 12 proteomic signaling pathways. The scores are indicated on low-to-high scale (yellow-to-red). 23 clusters are defined. Annotation bars include tumor types. (c) Heatmap depicting enrichment probabilities (EP) of mutations for most frequently mutated 20 genes (across all clusters). The probabilities are indicated on low-to-high scale (yellow-to-blue).