High-throughput sequencing has advanced our understanding of the role that bacteria and archaea play in marine, terrestrial and host-associated health. Microbial community ecology differs in many ways from macroecology, and therefore new statistical methods are required to analyze microbiome data. In this talk I will present two new statistical methods for the analysis of microbiome data. The first, DivNet, estimates the diversity of microbial communities, and the second, corncob, estimates the relative abundance of microbial strains, metabolites, or genes. I argue that the models underpinning these methods better reflect microbial dynamics (such as microbe-microbe interactions) than existing methods, resulting in improved error rates for hypothesis testing. The methods will be illustrated with an analysis of microbial communities living on seafloor rocks on the Dorado Outcrop, an area of exposed basalt on the East Pacific Rise. This talk is aimed at non-statisticians; a conceptual understanding of the methods will be emphasized over mathematical detail.
This is joint work with Bryan Martin and Daniela Witten at the University of Washington.