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Accordingly, to compare distributions between groups, we used T-test, non-parametric Kruskal-Wallis test or chi-square test.
In those cases where we had significant differences in variance between groups, we used student's t-test with unequal variance.
For quantitative comparisons between groups, we used either Mann-Whitney (between two groups) or Kruskal-Wallis tests (>2 groups).
To compare overall similarity in microbiota composition between groups, we used mothur software to perform a weighted principal component analysis (PCA) [20].
To evaluate the SAR and to compare between groups we used exact binomial 95% confidence intervals, and χ2 tests and multivariable logistic regression models adjusting for potential within-household correlation [22], [23], with a 5% type I error rate.
To evaluate statistical differences between groups, we used analysis of variance (ANOVA) with post-hoc Scheffe's tests or with Bonferroni-corrected t-test, performed using StatView SAS Inst.., Cary, NC).
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For comparison of data between multiple groups we used Kruskal-Wallis test and for comparison between two groups we used Mann-Whitney U test.
For correlation between two variables, Spearman's rank correlation was used and for differences between two groups we used Mann-Whitney's U-test.
To compare variables between the groups we used a Chi2 test.
In order to perform functional annotation of the differentially expressed genes between different groups, we used Ingenuity Pathway Analysis (IPA) software.
To compare percentage between two groups we used χ2 test.
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between arms we used
between TFs we used
between groups we employed
between groups we asked
between groups we determined
between groups we measured
between cycles we used
between arrays we used
between groups we treated
between sites we used
between visits we used
between groups we hypothesized
between groups we applied
between groups we removed
between cities we used
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