Are there any ways to run repeated measures alpha/beta diversity statistical tests in QIIME2? I’m somewhat familiar with ways to run a few relevant ones in Python and R, but I’m helping a collaborator with no Python/R knowledge set up a QIIME2 pipeline (to be executed in Jupyter notebook) for a series of studies.
To make future troubleshooting easier, I’m trying to keep everything that could possibly be run as QIIME commands run as such, rather than having my collaborator transition to Python/R. If needed, I can include some Python code, but I’d rather not use R (to decrease the need for rpy2 or having them download and switch to R halfway through analyses - mostly trying to minimize any potential issues down the line).
Additionally, I have a few questions about the best ways to perform repeated measures tests for alpha and beta diversity.
Alpha group significance
I was hoping to use a Friedman test as a non-parametric alternative to a repeated measures ANOVA or a repeated measures ANOVA on ranks, but are there other ways (potentially in QIIME) to do so that I could be missing?
I was hoping to use a repeated measures correlation. Is this possible in QIIME2, maybe through the longitudinal plugin?
Beta group significance
Is calculating participant centroids and then performing PERMANOVA/Adonis the best way to do this? If so, what is a reliable way to calculate centroids? I know that you can get centroids from permdisper, but I’m trying to stay in Python if possible, and I haven’t seen any way to get centroids in skbio. Are the centroids that permdisper uses the geometric means of the PCoA coordinates? If so, I can do that, but is there any way to calculate centroids in QIIME?
This is the one I’m most unsure about. How should I approach a Mantel Test with non-independent samples?
From my understanding (please correct me if I’m wrong here!), the mantel test first calculates the correlation between the two distance matrices and then permutes the data and calculates the p value based on the portion of permutations with better correlation coefficients. Would there be any way to perform this with a repeated measures correlation, rather than Pearson/Spearman?
Thanks a lot!