Abstract Number:
2818
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Aubrey Odom (1), Evan Johnson (2)
Institutions:
(1) Boston University, N/A, (2) Rutgers University, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
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.
Keywords:
generalized estimating equations|linear mixed models|longitudinal data analysis|metagenomics|microbiome|compositional data
Sponsors:
Section on Statistics in Genomics and Genetics
Tracks:
Miscellaneous
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