I’ve used 16s to study the diversity and composition of the microbiome of dozens of mammals.
I want to find differentialy abundant ASVs between groups of hosts based on host categorical traits (such as social structure) while taking into account the phylogenetic confounding factors.
I’ve found [phylogenetic ANOVA] but ANOVA assumes normal distribution which is not the case in abundance or relative abundance of bacteria.
Do you have any suggestion of non-parametric test that accounts for the host phylogeny?
I’m hoping to do some differential abundance analyses myself, and have been reading some papers. This review paper suggests a couple of methods for compositional data that I plan to work with. Hope it helps a little.
I haven’t worked much in comparative phylogeny, but there was a recent paper that addressed it across a wide variety of species. Maybe that methods section could help you?
One thought (off the top of my head, feel free to disregard) is to look at a relationship between your host phylogenetic distance and your community disimilarity using somehting like a procrustes analysis. For this, I can imagine turning your host tree into a distance matrix (I think you can do this in scikit-bio and then import into QIIME 2 using the artifact API) and then looking at the relationship between that and your community measured with your favorite metric(s).
I think this might actually be a really interesting place to apply some of the tree based methods. I know gneiss is being slowly decomissioned and ILR interepretation is a headache and a half, but you might be able to do some kind of clustering between the organism and the phylogenetic distance?
Thanks for referring me to compositional methods, however my issue is with treating phylogenetic confoundments