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. 2021 Dec 31;7(1):132–144. doi: 10.1038/s41564-021-01023-6

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© The Author(s) 2022

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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Extended Data Fig. 7 — Gap statistic using the R function “clusGap” from the R package “cluster” version 2.1.2 was used to calculate a goodness of clustering measure, the “gap” statistic. The “k.max” parameter was set to 10, the bootstrap “B” parameter was set to 100, and the analysis was done with two different “FUNcluster” method including cluster:fanny and kmeans. The analysis was restricted to the top 50 TSS transformed taxa with a minimum abundance of 0.2%. a. The analysis using the “FUNcluster” method “kmeans”. b. the analysis using the “cluster::fanny”. The numbers of statistical identified clusters are characterized by a decrease in Gap value on the Y axis. Once there is not further decrease in the Gap value (line started to flattened out) on the y axis, indicates the optimal number of clusters. For this analysis all 1389 samples across the three sampling timepoints were combined. The error bars indicate one standard error.