Hi (probably) @cmartino or other appropriate person,
I know at one point you mentioned that it's gradient-aware but not longitudinal. In the examples from both the original paper, a study of the built enviroment of SARS-CoV2 by Rob Knight's group and an infant transplantation study, it looks like Gemelli was applied to look at dyanmics after the intervention/grouping. (i.e. comparing comparing vaginal/cesarean born-infants; IBD patients vs healthy controls).
Is this the recommend use case, rather than say, working with something where there's a baseline sample followed by an intervention?
Good question, thanks for asking! I think the best way to describe gemelli / CTF is that allows for context-dependent or repeated-measure data dimensionality reduction. It is not restricted to longitudinal repeated-measure data because the order does not matter but that also means it is not explicitly modeling time. In that way, it is truly an unsupervised dimensionality reduction (like PCoA on beta-diversity distances). I will note that it is not unbreakable, so filter or structure the data accordingly. For example making sure the data is well balanced, remove a time-point where only one sample was collected, or remove a subject with only one time-point.