Guillaume ran us through the MG-RAST interface
Question that came up again (to be asked at the QIIME workshop) – what is the difference between PCA and PCoA
Canonical Correlation Analysis – a way to put vectors on your PCA to explain patterns in terms of metadata.
I suggested that we start putting up little tutorials about how to do different
Next week we will talk about annotation databases – please skim through some papers (and share them!) about the differences between COGS, KEGGS, etc.
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Jonathan Eisen's Lab 
I have used the vegan package in R (Oksanen et al. 2013) for CCA to investigate the effects of environmental variables on community composition. Here are some useful references:
Dixon, P. 2003. VEGAN, a package of R functions for community ecology. Journal of Vegetation Science 14:927–930.
Jari Oksanen, F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre, Peter R. Minchin, R. B. O’Hara, Gavin L. Simpson, Peter Solymos, M. Henry H. Stevens and Helene Wagner 2013. vegan: Community Ecology Package. R package version 2.0-7. http://CRAN.R-project.org/package=vegan
ter Braak, C. J. E. 1987. The analysis of vegetation environment-relationships by canonical correspondence analysis. Vegetatio 69:69–77.
ter Braak, C. J. E. 1988. Partial canonical correspondence analysis. in H. H. Bock, editor. Classification and related methods of data analysis. Elsevier, Amsterdam, Netherlands.
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My understanding is dissimilarity (e.g. distance matrices) are the measures used for Principle Coordinates Analysis (PCoA), whereas Principle Components Analysis (PCA) uses measures of similarity.
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