Frank Dondelinger - A Bayesian Multi-Task Approach for Detecting Global Microbiome Associations
|Dates:||13 November 2019|
|Times:||14:00 - 14:00|
|What is it:||Seminar|
|Organiser:||Department of Mathematics|
|Who is it for:||University staff, External researchers, Current University students|
Frank Dondelinger, lecturer in biostatistics at the Centre for Health Informatics, Computation and Statistics at Lancaster University, joins us for the Statistics seminar series.
The human gut microbiome has been shown to be associated with a variety of human diseases, including cancer, metabolic conditions and inflammatory bowel disease. Current statistical techniques for microbiome association studies often rely on a measure of ecological distance, or on detecting associations with individual bacterial species. In this work, we develop a novel, Bayesian multi-task approach for detecting global microbiome associations. Our method is not dependent on a choice of distance measure, and is able to incorporate phylogenetic information about microbial species. We apply our method to simulated data, and show that it allows for consistent estimation of global microbiome effects. Additionally, we apply the method to a real-world microbiome study in inflammatory bowel disease (Beamish, 2017) and show that we can use it to detect microbiome-metabolome associations.
Organisation: Lancaster University
Travel and Contact Information
Frank Adams 2
Alan Turing Building