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Activity Number: 372 - Statistical Methods for Microbiome Data Analysis
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #322063
Title: Subcommunity Learning and Dynamic Modeling for Microbiome Compositions Through the Logistic-Tree Normal Model
Author(s): Li Ma* and Patrick LeBlanc and Morris Greenberg and Zhuoqun Wang
Companies: Duke University and Duke University and University of Toronto and Duke University
Keywords: Compositional data; multivariate analysis; high-dimensional data; genomic sequencing
Abstract:

A key characteristic of microbiome compositional data is its large and complex cross-sample heterogeneity. Appropriately accounting for these “compositional variance components” is critical for a number of common inference tasks, including identifying latent structures, carrying out hypothesis testing on cross-group differences, and modeling dynamics, but is complicated by the key features of microbiome compositional data including high-dimensionality and compositionality. These characteristics incur the need for structural constraints on modeling taxa covariance while maintaining the analytical and computational tractability of the resulting model or method. To address this need, the logistic-tree normal model is introduced as a generative model for microbiome compositions that combines the key features of log-ratio based models and Dirichlet-tree models to allow both structural modeling on taxa covariance and scalable computation. In this talk, I demonstrate how one may utilize the logistic-tree normal model to improve statistical models for identifying subcommunity structures and for characterizing dynamics in microbiome compositions.


Authors who are presenting talks have a * after their name.

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