How do you find differences when your metagenome sample size is small? I received a matrix of genomics features (KEGG annotated metagenomes) abundance from 6 samples belonging to 2 groups. My collaborator is interested in finding differences between the two groups.
The matrix, after scaling and removing zero-variance, is roughly 4000 features. Via shapiro test I already know some 30% of the features are not normally distributed across the samples, so I assume PCA is out of option and any test with the normal distribution assumption.
I already made some preliminary analysis using NMDS to cluster my samples and was thinking of trying also t-SNE and recursive feature elimination. However I am not sure how to find &quot;significant&quot; differences given the small sample size. Any suggestion? Would something like LEfSe work in this instance?
I think there are several ways to figure this out. I wouldn't worry too much about normality as most of the methods are either nonparametric or pretty robust to violations.

Since you are using KEGG, I would suggest working at the pathway or module level of annotation (if your features are e.g. KEGG Orthology groups). In principle, you can then combine information across features as well as across samples. Simplest I can think of, without even looking at the literature, would be to use permutations (shuffle row and column labels) to look for modules different in abundance between groups. (Many existing methods are doing some more sophisticated version of this.)
I think that LEfSe in theory should work quite well here, it's been a while since I thought about it but I believe this is the problem that it is designed to solve, assuming that I understand your question.
There are a lot of methods that are previously described for doing this kind of work. I would suggest reading some reviews of methods, for example the section &quot;Multivariate differential abundance testing&quot; in this article, or here, looking for some inspiration. To some extent it depends on which questions you are interested in answering and what exactly your features represent.