Handling paired before/after microbiome data

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Hi everyone,

I am working on analyzing the microbiome of a specific human body site to compare patients before and after a clinical intervention. Because the data is collected from the exact same patients at two different time points, the samples are inherently correlated (a strictly paired/repeated-measures design).

For alpha diversity, my current plan is to evaluate normality and proceed with either a parametric approach (Paired t-test) or a non-parametric approach (Wilcoxon signed-rank test).

However, I could use some guidance on the subsequent steps of the analysis:

  1. Beta Diversity (PERMANOVA): Does anyone have advice on how to properly implement this before/after design in a PERMANOVA to evaluate shifts in beta diversity?
  2. Differential Abundance (DA) Analysis: Which differential abundance tools do you highly recommend for this type of paired design?

Any insights, R package recommendations, or links to similar analytical workflows would be greatly appreciated.

Thank you in advance!

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Hi @kbenmd,
I think QIIME 2 might have what you want.

For beta diversity:

Have you checked out q2-longitudinal? longitudinal - Microbiome marker gene analysis with QIIME 2. I think thepairwise-distances might be what you are looking for but there is a ton of cool longitudinal analyses that might be useful!

For differential abundance:

ANCOM-BC2 is able to account for repeated measure so that would be my recommendation for differential abundance. ANCOM-BC2 is also available in QIIME 2. composition - Microbiome marker gene analysis with QIIME 2

Here are the docs I like to reference when using ANCOMBC2 for repeated measures: ANCOM-BC2 Tutorial

I hope all this helps

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Hi @kbenmd and @cherman2,

Hopefully its okay that I mention it. I've used gemelli CTF a few times for both beta diverisity and sort of differential abundance. I like it because it does the deltas for me, and becuase I can look at orientation more easily. (Is there directionality in PCA space). Since CTF/PCA are grounded in features, I can translate that direction to changing taxa.

Best,
Justine

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