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. 2021 Feb 9;40(10):1792–1805. doi: 10.1038/s41388-021-01665-0

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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. 4 — a The tree trained on the 469 TCGA samples. Classification of a new sample into one of the four subtypes is done by traversing the tree from its root to one of its leaves (representing an assignment to a subtype). Three biomarkers (shown in black) are used to determine the route along the tree: overexpression of KLK8 distinguishes the “Keratin” subtype, overexpression of TIGIT distinguishes the “Immune” subtype, and finally, overexpression of TRIM63 distinguishes the “Melanogenesis-high” from the “Melanogenesis-low” subtype. b Threshold values and surrogate genes for the three decision tree predictors as identified by the algorithm. Threshold values are used to distinguish between high and low values (based on normalized expression values), and surrogates can be used as alternatives for the respective predictor gene.