I have the following experimental design and I´m a bit lost:
I have subgingival samples collected from 30 patients. Two samples were taken from each patient on a first visit (V1):
- A sample from a tooth that has not received any treatment (V1-C)
- A sample from the treated tooth (V1-T)
(In each patient sample C and T were taken at the same time)
Sampling was repeated for all patients two months after first visit (V2).
In other words, I have 120 samples: 30 V1-C, 30 V1-T, 30 V2-C and 30 V2-T and I am interested in knowing if there are differences between treatment and control in both alpha and beta diversity.
I´m quite unsure what´s the proper statistical analysis/methods for this study because it involves either paired and longitudinal samples. I have read the q2-longitudinal tutorial but it is not clear to me. What´s the best way to analyse these samples?
Welcome to the forum!
My best recommendation is to contact a statistician and see if you can collaborate on this project. I think the level of support you need verges into authorship territory, especially since you may have a doubly nested design.
However, there are somethings you might wish to consider in your conversations:
- Is an individual's oral microbiome more similar overall, and so do you need to account for that in your model? (I would guess yes, but I mostly work in the gut)
- What are the kind of things you want to compare? The time point? The tooth? Figuring that out will help you model better.
- Do you have a hypothesis around what you expect to change? Abundance? Richness? Both? Do you have the correct metrics to test that?
Thanks for your answer. I just want to compare the tooth: treatment (T) vs control (C). I had thought to use
qiime longitudinal pairwise-differences (for alpha diversity metrics) and
qiime longitudinal pairwise-distances (for beta diversity metrics). First, I would perform the T vs C comparison for visit 1 (V1) and then for visit 2 (V2). I don't know if this approach is correct...
Thank you in advance.
I think there are two questions, one that can be easily answered here and one that's a little harder.
So, are q2-longitudinal
pairwise-distances good ways to get differences between timepoints or along a gradient? Yes. You just have to make sure that your data is coded in a way that the model will accept the gradient. (You may need to code C=0 and T=1 in a numeric column, for example).
I don't think I'm in a position to judge how correct your biological question is, and whether the model you're working with is the best way to address that question. I would recommend working with a biostatistican to make sure your modeling is correct, and inviting them to be a co-author on your publication. This is past the point of support I can provide here.