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Abstract:
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Culture independent techniques, such as shotgun metagenomics and 16S rRNA amplicon sequencing have dramatically changed the way we can examine microbial communities. However, statistical methods are limited in identifying and comparing bacteria driving changes in their ecosystem. This is partly due to the inherent properties of microbiome data, including sparse counts, the compositional nature of data and the fact that microbial communities modulate and influence biological systems as a whole. We have developed mixMC, a multivariate data analysis framework to identify specific associations between Microbial Communities and their type of habitat. mixMC accounts for the compositional nature of 16S data and enables detection of subtle differences when high inter-subject variability is present due to microbial sampling performed repeatedly on the same subjects but in multiple habitats. Through data dimension reduction the multivariate methods provide insightful graphical visualizations to characterize each type of environment in a detailed manner. We will illustrate the added value of using multivariate methodologies to fully characterize and compare microbial communities.
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