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. 2019 Nov 14;9:16801. doi: 10.1038/s41598-019-53041-4

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© The Author(s) 2019

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Both origin and disease effects are evident in an integrative analysis of two cohorts. Data from a Chinese cohort¹⁰ was integrated with our own to form a merged dataset for analysis. (a) PCoA of Jaccard distance matrix. (b) Boxplots of key phyla across cohort and type. (c) LEfSe analysis identifies disease discriminating taxa in the integrated dataset. (d) ROC of a random forest model built on the features identified in (c). In the boxplots, lines indicate median relative abundances, boxes denote 3^rd and 1^st quantile, whiskers denote ± 1.5*IQR, and outliers are shown as dots.