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Activity Number: 469
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #305351
Title: Sparse Logistic Normal Regression for Modeling Over-Dispersed Count Data with an Application to Microbiome Data Analysis
Author(s): FAN XIA*+ and Hongzhe Li
Companies: The University of Hong Kong and University of Pennsylvania
Address: 4247 Locust St, Philadelphia, PA, 19104, United States
Keywords: Compositional data ; Logistic normal regression ; Group variable selection ; MCEM ; Human gut microbiome

The human body contains trillions of microbial cells which have an influence on human health and predisposition to complex diseases. One important problem of microbiome data analysis is to identify the environmental covariates associated with different taxa. However, the taxa count data is often over-dispersed and therefore we propose to use a hierarchical statistical model that combines the Aitchison's logistic normal (LN) distribution for the latent compositions and conditional multinomial distribution for the observed counts. To deal with the high dimensionality of the covariates, we apply a group l1 penalized LN regression for the selection of relevant covariates. This model can naturally accout for sampling variabilities and zero observations. We present a Monte Carlo expectation-maximization (MCEM) algorithm to estimate the model parameters. Our simulation results show that the pro

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