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. 2020 Apr 8;10:6072. doi: 10.1038/s41598-020-62997-7

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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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Classification of T cells for various pairs of classes relative to control. (a) LN vs SMG. (b) LN vs Skin. (c) SMG vs Skin. (d) Surfaces and SPHARM reconstructions of representative cells from the three analyzed classes. Significantly higher than control: **p < 0.01, ***p < 0.001; significantly higher than static features: ^$$$p < 0.001; significantly higher than dynamic time features: ^###p < 0.001; one-sided Mann-Whitney test, n = 150 per group. Numbers below the box plots indicate the number (ratio) of cells in each class for the corresponding class pair. For ratios between 0.4 and 0.6, the random classifier was used as control. In all other cases, the majority classifier was used, whose accuracy is defined by the ratio of cells. In the dynamic classifiers, fewer cells were analyzed because not all cell tracks were sufficiently long (20 time points) to be included. This resulted in different ratios between cell numbers and hence different control accuracies for the static and dynamic classifiers.