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Activity Number: 40 - Statistical Methods for Microbiome and Tumor Data
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #306894 Presentation
Title: Discriminative Factor Model for Microbiome Analysis
Author(s): Yiwen Liu and Peter Merrill* and Noelle Younge and C. Michael Cotten and Ricardo Henao
Companies: Duke University and Duke Clinical Research Institute and Duke University School of Medicine and Duke University School of Medicine and Duke University
Keywords: microbiome; non-negative matrix factorization; factor model; sparsity; low-resolution data

Analysis of sequencing-enabled microbiome data poses many challenges that result from the combination of small sample sizes, low resolution, sparsity and high-dimensional measurements. Further, resolution and sparseness make it difficult for the data to be analyzed under the assumption of log-normality that is commonly made when analyzing other sequencing-based omics data. Fortunately, measurements of microbial communities exhibit rich correlation structure that can be leveraged within a dimensionality reduction paradigm to uncover associations with biological processes related to health and disease. To this end, we present a discriminative factor model based on non-negative matrix factorization that addresses the challenges of microbiome data, as well as the need for identifying microbial communities associated to phenotypes of interest. Experiments on artificial and real-world microbiome datasets illustrate the capabilities of the proposed approach in relation to existing methods.

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

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