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. 2020 Jun 8;10:9154. doi: 10.1038/s41598-020-65689-4

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© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 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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Principal component analysis with kernel density estimates reveals distinct clusters of different skin conditions separated mostly along PC1. (a) Low values of PC1 correspond to healthy skin in the control group. Increasingly higher values of PC1 indicate the spectrum of skin conditions transitioning from healthy to pseudo-control to psoriasis. (b) Eczema patients form a cluster at the interface between pseudo-control and psoriasis clusters. Outliers can be explained by errors induced by severe artifacts caused by subject movements during image acquisition. Shaded areas show estimated kernel density map for the corresponding groups. (c–j) Box plots, summarizing the extracted features used for principal component analysis.