2.4 Statistical analysis
Demographic data including age and sex were summarized using descriptive methods.
Microbiome data were analyzed using QIIME2 microbiome bioinformatics platform (31).
Sampling depth of the samples was evaluated to determine if the within-sample diversity was fully captured: saturation of the alpha refraction plots was inspected. Within-sample diversity was measured by Shannon’s alpha diversity index. Influence of demographic characteristics (age and sex) and clinical variables (phenotype of VKC, positive skin prick test total and/or serum specific IgE, topical therapy, type of delivery and feeding, type of diet, contact with pets, allergy family history, living place, history of atopic dermatitis during the first year of life, history gastrointestinal disorders) were ascertained by Kruskal-Wallis statistical test. Pairwise comparisons were made when more than two modalities were present, and Benjamini-Hochberg adjustment procedure for type I error inflation due to multiple testing was used. Between-sample diversity was measured using Bray-Curtis index. Potential sample clusters were highlighted by 3D plots obtained from Principal Coordinates Analysis (PCoA) based on unweighted UniFrac as a distance metric.
The analysis of composition of microbiomes (ANCOM) (32) was used to identify differential abundant bacterial and fungal taxa between VKC and HC at various levels of hierarchy including phylum, family, genus, species, and OTUs. Volcano plot of the effect size difference vs. ANCOM test statistic W was used to visualize the result of differential abundance testing.
Results