Bioinformatic Statistical Analysis

α-Diversity, β-diversity, and taxonomic differences of gut microbiota were analyzed as described previously.¹⁴ α-Diversity represents the richness (number of taxonomic groups or species, indicated by Chao, Ace, and operational taxonomic unit [OTU] indices) and evenness (distribution of abundances of the groups, indicated by Shannon and Simpson indices) of gut microbiota.²⁵ β-Diversity assesses the change in the diversity of species from one environment to another (structural differences).²⁶ We constructed the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology and KEGG pathway/module profiles²⁷ using PICRUSt2 (version 2.4.1; https://github.com/picrust/picrust2/wiki), which is software used to predict functional abundances on the basis of 16S rRNA marker gene sequences. A random forest 4.6-12 package was used to establish a disease diagnostic model on the basis of five-fold cross-validation. We calculated the probability of disease (POD) index to predict whether samples were from patients with IgAN or HCs, and the area under curve (AUC), on the basis of the ROC curve, was determined to evaluate the diagnostic efficiency of the established model (R version 3.4.1, pROC package).

We described quantitative data with normal distribution using the mean and SD, and presented data with non-normal distribution as median with 25th and 75th quartiles. We used a t test to compare the two groups of normally distributed quantitative data, and a nonparametric test (Mann–Whitney U test) for non-normally distributed quantitative data. Qualitative data were described using percentages and analysis using the chi-squared test. We adjusted multiple hypothesis tests using false discovery rate (FDR), and considered differences significant below a FDR threshold (P_fdr) of 0.05.