I noticed that the results from using DESeq2 as a method to compare relative abundances in differential_abundance.py depend on the version of R and the version of DESeq2, especially with low sample size. Specifically, R version 3.1.2 and DESeq2 version lower than v.1.4 give much lower p values/more "significantly different" OTUs than R version 3.2.3 and DESeq2 v1.4.
So it might help to clarify which R and DESeq2 version to install because otherwise the results will be different depending on the version? Also, which versions are canon for QIIME developers?
Another thing that I think might help is that these discrepancies are really strong with low N per group, but at N>=20 per group they basically disappear, so maybe it would be helpful to include a minimal sample number for differential_abundance.py?