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. 2019 Oct 30;9:15635. doi: 10.1038/s41598-019-52059-y

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© The Author(s) 2019

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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Renal cell carcinoma subtype classification based on untargeted metabolomics data. (a) OPLS-DA model of RCC subtype classification (N = 37). The model is composed of 2 predictive components and 1 orthogonal component and presents an R²X_(cum) of 44.2%, a goodness-of-fit R² of 75.0%, a goodness-of-prediction Q² of 59.7% and a CV-ANOVA p-value of 7.516 × 10⁻⁸. (b) Validation plot obtained from 100 permutation tests. (c) ROC (Receiver Operating Characteristic) curves obtained from an independent cohort (N = 28) showing the ability of OPLS-DA model to predict RCC subtypes. (d) Loading plot showing the most discriminative metabolites. The metabolites with VIP (Variable Importance for the Projection) >3 are highlighted with red circles; with VIP >2 are highlighted with orange circles. Variables with VIP >3 are used for further pathway analysis.