This event is organized by the Ontario Regional Centre of the Canadian Statistical Sciences Institute (CANSSI Ontario).
Annual Distinguished Lecture in Statistical Sciences with Professor Susan Holmes
Free Event | All Welcome
Date: May 22
Time: 3:30-4:30 p.m. ET
Format: Hybrid Event (In-person in Toronto / Online by Zoom)
Registration Required: https://canssiontario.utoronto.ca/event/2024-dlss-susan-holmes/
Location: 10th Floor, 700 University Avenue, Toronto, ON. Please arrive early, as seating is limited.
A reception follows the lecture on May 22.
For more information, email Esther Berzunza at esther.berzunza@utoronto.ca.
Talk Title (May 22): Hidden Variables: Using Statistics to Decode Heterogeneous Microbiome Data
Abstract: Most studies of clinical or environmental microbiota involve data that are heterogenous at multiple levels. Some of the studies involve response variables that we aim to predict and understand, preterm birth, growth rates in undernourished children, insulin levels in diabetes are some examples. Standard statistical methods usefully separate unknown parameters from the data themselves and provide insight into the optimality properties of some standard estimates. This clarification provides useful insight into uncertainty quantification and enables optimized downstream experimental design. Analogies with methods in textual analyses (Natural Language Processing) such as the use of latent variables methods provides useful interpretations as shown by Sankaran and Holmes, 2018. Testing in the context of combined heterogeneous longitudinal data in perturbation studies of the human microbiome can be even more challenging because there are often a small number of samples with strong dependencies as well as a large number of features from multiple domains. These provide interesting data science challenges where mathematical models of the underlying factors can be plagued with non-identifiability that can make effective uncertainty quantification difficult. We have shown that Bayesian and Bootstrap approaches can provide nonparametric answers to the statistical challenges and have supplemented these with effective visualization techniques distributed as R packages (phyloseq, agPCA, treelapse, bootLong, dada2). This presentation will include joint work with Kris Sankaran, Julia Fukuyama, Ben Callahan, Claire Donnat, Joey McMurdie, Pratheepa Jeganathan, Lan Huong Nguyen and David Relman's group at Stanford.
Speaker Profile: Susan Holmes has been working in non parametric multivariate statistics applied to Biology since 1985.
She started her research career in France at the INRAE institute in Montpellier. She has taught at MIT, Harvard and was an Associate Professor of Biometry at Cornell before moving to Stanford in 1998. She likes working on big messy data sets, mostly from the areas of Immunology, Cancer Biology and Microbial Ecology and her group developed the popular Bioconductor packages phyloseq and dada2 for microbiome data analyses.
Professor Holmes has co-authored an open access book with Wolfgang Huber (EMBL) published by Cambridge University Press on Modern Statistics for Modern Biology based on a popular course she teaches at Stanford. Her work is funded by the NIH and the Bill and Melinda Gates foundation. Her theoretical interests include applied probability, MCMC (Monte Carlo Markov chains), Graph Limit Theory, Differential Geometry and the topology of the space of Phylogenetic Trees.
Note: Event details can change. Please visit the unit’s website for the latest information about this event.