Distinguished Speaker Seminars MT16


Date:        Friday 16th September, 3.30 p.m.

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.


Week 1:    Friday 14th October, 2.30 p.m.

Speaker:  Professor Matthew Stephens, Department of Human Genetics, University of Chicago

Title:        "Come join the multiple testing party!"

Abstract: Multiple testing is often described as a "burden". My goal is to convince you that multiple testing is better viewed as an opportunity, and that instead of laboring under this burden you should be looking for ways to exploit this opportunity. I invite you to a multiple testing party.

Week 8:    Friday 2nd December, 3.30 p.m.

Speaker:  Professor Richard Durbin, Wellcome Trust Sanger Institute, Cambridge

Title:         Inferring population history from whole genome sequences

Abstract:  Genome sequences carry genetic information to make an organism, but they are also products of evolution and as such carry information about the genetic history of individuals and species.  In recent years analysis of genome sequence data has told us much about the origins of human populations across the world, their migrations and intermixing with other populations, including with archaic hominins such as Neanderthals and Denisovans.  However, we are still at the beginning of the process of interpreting genetic history from genome sequences. To extract this information requires use of statistical analysis methods that make use of efficient approximations to population genetic models.  I will discuss a series of methods to infer population history from whole genome sequence, with a particular emphasis on cases where there is gene flow or introgression between ancestral populations.  I will present a new method based on hidden Markov models to infer ancestral introgression from deeply diverged populations, illustrated with an application to recently obtained genome sequences of Papuans and aboriginal Australians (Malaspinas et al., 2016).


Week 10:  Thursday 15th December, 10.30 a.m.

Speaker:   Professor Eric Kolaczyk, Department of Mathematics and Statistics, Boston University

Title:          Network-based Statistical Models and Methods for Identification of Cellular Mechanisms of Action

Abstract:   Identifying biological mechanisms of action (e.g. genes, functional elements, or biological pathways) that control disease states, drug response, and altered cellular function is a multifaceted problem involving a dynamic system of biological variables that culminate in an altered cellular state. The challenge is in deciphering the factors that play key roles in determining the cell's fate. In this talk I will present an overview of various efforts by our group to develop statistical models and methods for identification of cellular mechanisms of action.  Common to all of our approaches is the use of certain perturbed Gaussian graphical models, which allows us to formulate the identification problem as a network-based statistical inverse problem.  Illustrations will be given in the context of yeast experiments and human cancer.