“Microbiome Data Analysis in Longitudinal Study”
Hosted by the Division of Biostatistics
Abstract: Recent research demonstrates that changes in the microbiome can have considerable health implications such as malnutrition, asthma, obesity, diabetes, and other conditions. This has further promoted substantial interest in the microbiome from both
basic and clinical perspectives, and prospective longitudinal studies have been conducted to probe the mechanisms on how the microbiome affects health and disease. However, the special structure and characteristics of high-dimensional compositional microbiome data complicate effective analysis of microbiome data. In today’s seminar, I will introduce two recent works in my group. One is a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. Our method can help scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, by identifying the specific microbial agents and quantifying causal microbiome effects. The other one is a novel joint modeling framework, which is designed to handle the zero-inflated and highly skewed longitudinal microbial proportion data and examine whether the temporal pattern of microbial presence and/or the non-zero microbial proportions are associated with differences in the time to an event.
Everyone must register for each individual seminar. Each presentation has a unique registration link found on the Biostatistics seminars webpage.
For inquiries contact Emily Gremminger.