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Unless otherwise noted, all statistical analyses were performed using python scripts implemented in QIIME v. 1.8.0, and all python scripts referenced here are QIIME scripts. The IPython notebook file used for all QIIME analyses is available at http://nbviewer.ipython.org/gist/jennomics/c6fe5e113525c6aa8add. To explore the differences in overall microbial community composition across the 15 meals, both the phylogenetic weighted UniFrac distances \cite{20827291} and the taxonomic Bray-Curtis dissimilarities \cite{Bray_1957} were calculated using the beta\_diversity\_through\_plots.py script. This script also produced a principal coordinates analysis (PCoA) plot in which the Bray-Curtis dissimilarities between samples were used to visualize differences among groups of samples (see Figure 1 for this type of visualization for the three Diet Types.) To test for the significance of dietary pattern on the overall microbial community composition, we used a permutational multivariate ANOVA as implemented in the compare\_categories.py script. To test for significant differences in taxonomic richness across dietary patterns, we used the non-parametric Kruskal-Wallis test \cite{Kruskal_1952} with the FDR (false discovery rate) correction as implemented in compare\_alpha\_diversity.py. To test for the significant variation in frequency of individual OTUs across dietary patterns, we used the Kruskal-Wallis test with the FDR correction as implemented in the group\_significance.py script. To test for significant correlation between the relative abundance of a single taxonomic group and meal metadata categories (i.e. nutrient composition, whether a meal contains fermented foods, etc) at 5 taxonomic levels (phylum-genus) Pearson correlation coefficients \cite{Pearson_1895} were calculated and tested for statistical significance using Stata (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP.)  ##Metagenome Prediction with PICRUSt  A synthetic metagenome was generated based on the observed 16S rDNA sequences for each meal. To do this, the 16S rDNA sequences were clustered into a collection of OTUs sharing 99% sequence identity, using the pick\_closed\_reference\_otus.py. pick\_closed\_reference\_otus.py script.  The resultant OTU table was normalized with respect to inferred 16S rRNA gene copy numbers using the normalize\_by\_copy\_number.py script distributed with PICRUSt v. 1.0.0 \cite{23975157}. The normalized OTU table was used to predict meal microbial metagenomes with PICRUSt's predict\_metagenomes.py script. The final predicted metagenome is output as a .biom table, which is suitable for analysis with a tool such as STAMP \cite{25061070}. We used STAMP to test for and visualize significant (predicted) functional differences in microbial communities between the three dietary patterns.