We have a bunch of 16S metagenomics samples for case and control patients. Unfortunately, based on 16S microbial abundance counts, there are no significant differences between the case and control patients. Before we go ahead and scrap the experiment, my supervisor suggested looking into metadata of the study (smokers/non-smokers, BMI, age, sex, etc). He is hoping that we might be able to identify confounders that way; maybe excluding the smokers from the analysis will give us stronger signals, etc.
I am new to this kind of analysis and would appreciate suggestions as to what kind of tools I could look at (ideally with beginner's tutorials :-P)
Thanks in advance!