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 "significant" differences given the small sample size. Any suggestion? Would something like LEfSe work in this instance?