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. 2020 Jul 23;10:12347. doi: 10.1038/s41598-020-69271-w

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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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(A) The analysis workflow for filtering significant candidate proteins. (B) The PLS-DA plot shows a clear grouping of patients with FM and controls by using the 22 candidate proteins. The PLS-DA transformation preserves as much covariance as possible between the 22 candidate proteins and sample labels in the first component, which is the most relevant for distinguishing sample labels. The first two components are retained to distinguish the control and FM samples. (C) The decision tree analysis of candidate proteins suggests a panel of four proteins for accurate identification of patients with FM.