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. 2021 Mar 17;18:8. doi: 10.1186/s12977-021-00552-6

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© The Author(s) 2021

Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — Partial least square (PLS) and Random Forest Analysis supported initial analyses. a Partial Least Square (PLS) Analysis for combined Zambian and Rwandan cohorts. Analysis was done with NIPALS Fit with 1 Factor. VIP (Variable Importance Plot) Threshold was set at 0.8. ITAC, GMCSF, Fractalkine, IFNg, IL-10, IL-12, IL-17a, IL-1b, IL-2, IL-21, IL-23, IL-5, IL-7, IL-8, MIP-1b, and TNFa were the biomarkers that caused preinfection profile in combined cohorts. b Random Forest Analysis used to determine which cytokines and chemokines contributed to the pattern of predisposition observed. In this analysis, the color of the square indicates the importance of the cytokine or chemokine to the profile of predisposition (light yellow is less importance, dark red is high importance)