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. 2024 Oct 26;24:436. doi: 10.1186/s12866-024-03590-0

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

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Fig. 6 — Network analysis shows clusters of microbial species and positive or negative correlations of their abundances. (A) Nodes (representing species) were colored according to the patient group in which species had the highest mean abundance. (B) Nodes were colored according to the detected communities using the Louvain algorithm. Node size is directly proportional to the centrality of the species within the overall network. Edge thickness is inversely proportional to the Pearson p-value (10% Benjamini–Hochberg two-stages FDR), and it is colored according to the positive (red) or negative (blue) Pearson’s coefficient. CKD: non-dialysis Chronic Kidney Disease, HD: Hemodialysis, Tx: Kidney transplant), HC: Healthy Controls, Comm: community.