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B0983
Title: Longitudinal microbiome data Authors:  Snehalata Huzurbazar - University of Wyoming (United States) [presenting]
Eugenie Jackson - West Virginia University (United States)
Abstract: As longitudinal microbiome studies become more common, it is important that we assess how to analyze such data. Methods for large, sparse multivariate count data collected over time are not common in traditional longitudinal data analysis. The first steps in most microbiome data analysis is often the use of ordination methods to explore the data and visually assess existence of patterns, especially of taxa composition with respect to covariate classes. We first consider options for such visualization for data collected over more than one time period on the same subjects. We then present a review of the literature for longitudinal inference for microbiome data, and consider other alternatives.