Fig. 2
From: The compositional and functional imbalance of the gut microbiota in CKD linked to disease patterns

Gut microbiome in CKD patients is characterized by depressed alpha diversity and heterogeneity in microbial beta diversity. A We showed differences in diversity and richness of the gut microbial community between CKD patients and healthy subjects. Number of observed species, and distribution of microbial alpha diversity such as Chao1 index, Simpson, Shannon, Pielou and Goods coverage index comparing CKD cases and healthy controls are shown. P values were computed by Wilcoxon rank test. Box-and-whiskers plots represent the interquartile ranges (25th through 75th percentiles, boxes), medians (50th percentiles, bars within the boxes), and the 5th and 95th percentiles (whiskers below and above the boxes). B Rarefaction curve in each group by gradually enhancing the sequencing depth of random samples and calculating the number of OTUs. The observed OTUs that the sample contains in a given series of sequencing depths were shown. C PCoA distance matrix based on bray curtis distance illustrated the heterogeneity of CKD patients and healthy subjects in between-habitat diversity. Different colors in the scatter plots represent samples from different groups. The higher the similarity between samples, the closer they distribute in the plots. Values in brackets represent the amount of total variability explained by each principal coordinates. P values were from the Adonis to test the significance of difference between groups. Violin plot depicted the distribution of single coordinate axis in PCoA2, with the difference obtained by wilcoxon rank sum test. D The distribution of CKD subjects and the controls was plotted in a ternary diagram based on the relative abundance of the top3 most dominant phylum (Actinobacteria, Firmicutes, and Bacteroidetes). E All the individuals were assigned into discrete clusters as identified by K-means clustering of genus-level features. The percentage of control and CKD samples distributed in cluster1, cluster2 and cluster3. There were 7.4% controls in cluster1, 50% controls in cluster3; 22.3% CKD participants in cluster1, 35.1% CKD patients in cluster3. Relative abundances of the top genera in each cluster were demonstrated, with Prevotella prominent in cluster1, Bifidobacterium dominant in cluster2 and Faecalibacterium in cluster3. Boxes represent the interquartile ranges, lines inside the boxes denote medians, and circles are outliers. P values were from the Kruskal–Wallis rank sum test