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. 2020 Mar 10;8:32. doi: 10.1186/s40168-020-00802-3

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© The Author(s) 2020

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 — Comparative analysis of microbial gene functions and metabolic patterns between control and WFS. a PCoA based on the relative abundance of all Unigenes with Bray-Curtis distance and KEGG orthology groups. b OPLS-DA score plots based on the metabolic profiles in intestine samples from control group (n = 7) and WFS group (n = 6) in ES+ and ES− models. c Multiomics data integration for different categories. The relationship (edges) between OTUs (purple), Unigenes (olive) and metabolites (blue) between all samples is estimated by Spearman’s correlation analysis. And those with low correlated (|r| < 0.7) are not shown