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. 2023 Jun 16;13:9797. doi: 10.1038/s41598-023-36704-1

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© The Author(s) 2023

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Compositional analyses at the genus level of WBS and CTRL. (A) Partial least squares discriminant analysis (PLS-DA) plot; (B), plot of loading variables, filtered for loading coefficient > 0.1. The Root Mean Square Error (RMSE) = 0.24 indicates a good accuracy in classification’s prediction; (C), Principal component analysis (PCA) plot. More the loadings are distant from the origin, more they influence the model. The loadings separated by a small angle show a positive correlation; the loadings separated by a large angle have a negative correlation, and those with a right angle indicate no correlation. (D), Univariate ANCOM-BC plot.