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. 2021 Jan 11;11:322. doi: 10.1038/s41598-020-79121-4

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© The Author(s) 2021

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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Patient Similarity Network (PSN) analysis of CLL patients based on the CLL cell bulk and activation of immune cells. (A) PSN shows the distribution of CLL patients into four clusters, according to percentage and CLL cell counts and the expression of HLA-DR on CLL cells and subsets of monocytes (classical, intermediate, non-classical MON). Individual clusters are coloured. Each node corresponds to one patient. Lines connect patients with the highest similarity of expression/cell count profiles. (B) The silhouette of clustered patients in the acquired PSN shows high positive values, indicating that the patients were unambiguously assigned to a cluster far from the boundary between two adjacent clusters; the presence of negative values indicates patients who may also be included in another cluster. (C) Characteristics of obtained clusters revealed by PSN. The y-axis in the graph shows the average values of the used markers/cell counts normalised to the maximum value in the data set. The whiskers represent confidence intervals.