Clinical Data Analysis
Continuous variables were reported as means and standard deviations (SD)
or medians and interquartile ranges (IQR); categorical variables are
reported as frequencies and percentages. Differences in baseline
characteristics between cohorts were evaluated using t-tests for
continuous variables, Pearson chi-square and Fisher’s exact tests were
performed for categorical variables.
Bivariate
correlation analyses were conducted
to
detect the direction and strength of
relations
between score of each items in OABSS
(e.g.,
Daytime frequency,
Nighttime
frequency, Urgency, Urgency incontinence) and bacterial abundance and
the relations between clinical data and indices of bacterial alpha
diversity using Spearman correlation. Statistical analysis was performed
using the Statistical Package for Social Science (SPSS, version 21,
USA).
For
differentially abundant taxa between
cohorts,
Wilcoxon rank sum test was applied, and
Benjamini-Hochberg
false discovery rate correction was performed in R (version 3.4.1, stats
package). Statistical tests were based on two-tailed probability and the
results were considered significant when the P value was less
than 0.05.
The wrapper package Quantitative Insights Into Microbial Ecology (QIIME)
was applied to process the raw reads to create an operational taxonomic
units (OTUS) table. Using an open reference selection strategy with
Uclust, the sequences were clustered into individual OTU at the default
similarity level of 97%, and then chimera detection was performed using
the the program UCHIME. Using Ribosomal Database Project Classifier to
align a single representative sequence from each clustered OTU to the
Greengenes database.
Alpha diversity, including the
Observed
species, Chao1, Shannon, Simpson and Abundance-Based Coverage Estimator
(ACE) and Pielou’s index, was evaluated using QIIME. The Chao1, ACE and
the Observed species were used to calculate richness, samples with
larger values are richer. Evenness was calculated with Pielou’s Index,
which ranks samples from 0 to 1, with 1 being completely even, while a
smaller index score indicates that certain species are more abundant
than others. Shannon
and
Simpson index combines interactions between richness and evenness,
Larger Shannon diversity values indicate more diverse communities with
greater richness and/or evenness, and Simpson diversity is the opposite.
The difference of alpha diversity was evaluated by Wilcoxon rank sum
test. Beta-diversity, measured by calculating the Bray Curtis, weighted
UniFrac and unweighted UniFrac distances, was used to compare microbial
composition between samples. Taxa summaries were reformatted and
inputted into Linear discriminant analysis effect size (LEfSe) via the
Huttenhower Lab Galaxy Server to identify significantly different
bacteria as biomarkers between groups at genus level. In the settings of
LEfSe,11 the significantly specific bacteria were
identified using the Mann-Whitney U test, and their effect size were
estimated via linear discriminant analysis
(LDA).
The threshold on the logarithmic LDA score for discriminative features
was 2.0.