158 Statistical analysis Demographic and clinical characteristics of the study population were presented as means ± standard deviations (SD), medians with interquartile ranges (IQR) or proportions n with corresponding percentages (%). Differences between groups were analyzed using MannWhitney U-tests, while paired differences were determined usedWilcoxon signed-rank tests while adjusting for multiple comparisons under a false discovery rate (FDR) of 5%. Bacterial genera having a mean relative abundance above 0.25 were considered eligible for analysis. Microbial richness and diversity of samples was estimated with the Shannon diversity index as a measure of microbial alpha-diversity using QIIME. The microbial dissimilarities matrix (Bray-Curtis distances) was obtained using vegdist from the vegan R package and used as a measure of beta-diversity.24 Principal coordinates were constructed using the cmdscale function. Permutational multivariate analysis of variance (PERMANOVA) using distance matrices (ADONIS) was performed to analyze the variance in the Bray-Curtis dissimilarity matrix that could be explained by sample origin (IBD vs HC) or sample treatment (MACS-sorted samples vs. unsorted samples). Statistical analyses were performed using R (v.3.5.2) and the Python programming language (v.3.8.5, Python Software Foundation, https://www.python.org), using the pandas (v.1.2.3), numpy (v.1.20.0) and scikit-bio (v.0.2.3) modules. Data visualization was performed using the seaborn (v.0.11.1) and matplotlib (v.3.4.1) packages in Python. Two-tailed P-values ≤ 0.05 were considered as statistically significant. Chapter 5
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