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. 2021 Jan 6;22:6. doi: 10.1186/s12859-020-03921-8

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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. 4 — MiBiOmics and mixOmics comparison. a Venn diagram displaying common and distinct features extracted by DIABLO (mixOmics), multiple co-inertia and multi-omics WGCNA (MiBiOmics). Area under the curve (AUC) scores were computed to compare features selection and model performance of each method. b Selected features' weights comparison between multiple co-inertia and multi-WGCNA on their common subset of features. c Selected features' weights comparison between multiple co-inertia and DIABLO (mixOmics) on their common subset of features. d Selected features' weights comparison between DIABLO (mixOmics) and multi-WGCNA method on their common subset of features