Speaker: Professor Susan Holmes, Stanford University
Title: Statistical Challenges posed by the Human Microbiome
Abstract: We propose a new statistical workflow for the analyses of bacterial strains in longitudinal data analyses of data from the Human Microbiome. This includes using hierarchical mixtures for abundance modeling, hierarchical testing strategies and the propagation of uncertainty through the analyses to the ordination plots. We use a combination of normalization and Bayesian methods that incorporate estimates of uncertainty due to sample library size differences and differences in precision for different types of samples. We will show applications to the study of the vaginal microbiome and prediction of preterm birth.
This contains joint work with Ben Callahan, Kris Sankaran, Lan Nguyen, Julia Fukuyama, Sergio Bacallado, Stefano Favaro, Lorenzo Trippa and Boyu Ren and the Relman Lab at Stanford.