Wednesday, Aug 7: 9:55 AM - 10:00 AM
2818
Contributed Speed
Oregon Convention Center
Microbial time-series data poses unique challenges, including intricate covariate dependencies and diverse longitudinal study designs. Existing methods for profiling, modeling, and visualizing microbiomics data often fall short in addressing these challenges due to their lack of versatility, data type specificity, or failure to account for the compositional nature of the data. In response, we introduce LegATo, an open-source suite comprising modeling, visualization, and statistical software tools tailored for the analysis of microbiome dynamics. LegATo offers a user-friendly interface, making it accessible for researchers dealing with various study structures. Particularly well-suited for longitudinal microbiomics and transcriptomics data, our package incorporates joint Generalized Estimating Equation (GEE) models specifically crafted to accommodate compositional data. This toolkit will allow researchers to determine which microbial taxa are affected over time by perturbations such as the onset of disease or lifestyle choices, and to predict the effects of these perturbations over time, including changes in composition or stability of commensal bacteria.
generalized estimating equations
linear mixed models
longitudinal data analysis
metagenomics
microbiome
compositional data
Main Sponsor
Section on Statistics in Genomics and Genetics