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. 2020 May 19;10:157. doi: 10.1038/s41398-020-0831-9

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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. 2 — Boxplots show the predictive power in the fivefold cross-validations of each prediction model utilizing binary CES-D traits as response variables and metabolites and other covariates as predictive variables. Abbreviations: CES-D Center for Epidemiologic Studies-Depression Scale, HSIC Hilbert–Schmidt independence criterion, Lasso least absolute shrinkage and selection operator, SVM support vector machine, SPLS sparse partial least squares, MLR P < 0.05 multiple logistic regression with P < 0.05 variables, SVM P < 0.05 support vector machine with P < 0.05 variables, Lasso + SVM support vector machine with variables selected by Lasso, PLS partial least squares, MLR all multiple linear regression with all variables, SVM covariates support vector machine with only covariates, MLR covariates multiple logistic regression with only covariates, AUC area under the curve.