Computational approach to modeling microbiome landscapes associated with chronic human disease progression
Fig 2
Microbial community dynamic analysis performed on a human gut microbiome dataset (n = 275) obtained from a Crohn’s disease study.
(a) The number of clusters was estimated to be five by gap statistic. (b) Resampling-based consensus clustering analysis identified five robust and stable clusters. (c) Silhouette width analysis further confirmed the robustness of clustering assignment. A total of 255 of 275 (93%) samples had a positive silhouette width, and the average was equal to 0.1. (d) By combining the principal-tree and clustering results, a microbial progression model of Crohn’s disease was constructed, and four progression paths were identified. Each node represents an identified cluster, and the pie chart in each node depicts the percentage of the samples in the node having one of the CD behaviors (left panel) or belonging to one of the CD subtypes (right panel). (e-g) Visualization analysis provided a general view of sample distribution supported by the selected microorganisms. Each point represents a sample, which was projected onto a three-dimensional space by using the DDRTree method. Each sample was color-coded by its cluster index (e), CD behavior (f), and CD subtype (g), respectively. The solid line represents the constructed principal tree. HC: healthy control, cCD: colonic Crohn’s disease, iCD: ileal Crohn’s disease, icCD: ilealcolonic Crohn’s disease, r/nr: with/without ileocaecal resection.