I have a set of microbiome data from treated and control groups at different time points after a microbiome transplant treatment. My hypothesis is that the microbiome of the treated groups is more similar to the transplanted microbiome than the control groups.
I'm using QIIME2 to analyse my data. I have produced distance matrices and used PERMANOVA and ANOSIM to look for significant effects of variables on beta diversity. But this doesn't resolve my hypothesis that treated groups are more similar to the transplant.
I can see from the PCoA plots that treated samples are closer to the transplant, but I can't find a way to show that these samples are "significantly" closer to the transplant than their control counterparts.
What are some possible solutions to test my hypothesis?
Hello Peter
that's a very good question, but you are starting from a good place in the sense that you already have performed PCoA. The test you are looking for is constrained analysis of principle coordinates (CAP) devised by marti anderson and described here
https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/0012-9658%282003%29084%5B0511%3ACAOPCA%5D2.0.CO%3B2
I hope that helps
Andy
I suggest you to use Adonis multivariate analysis. There is a simple and useful example on Vegan R Package.
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