Differential abundance

add alternative differential abundance testing models to the standard ones employed in group_significance.py as recommended by McMurdie and Holmes, "Waste not want not why rarefying microbiome data is inadmissible."  We add the negative binomial GLM testing in DESeq2, and the zero-inflated Gaussian (ZIG) model of metagenomeSeq.  This requires rpy2 installation. NOTE - input an un-normalized, raw OTU table (not rarefied, nor otherwise normalized even by DESeq2 or metagenomeSeq) with uneven sample sequence sums.  The idea is that rarefying wastes data, and loses statistical power, by throwing away low depth samples, and by rarefying to a certain sum.  However, these models assume something about the underlying distribution.  Thanks.
say *palm and tongue body sites* instead of *L_palm* (as the latter isn't clear to someone who might not annotated their data using that specific term).