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Activity Number: 173 - Bayesian Methods Applied to Biometric Problems
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #304361
Title: High-Dimensional Association Detection in Large Scale Genomic Data
Author(s): Hillary Koch* and Qunhua Li
Companies: Pennsylvania State University and Penn State University
Keywords: composite likelihood; latent variable models; classification

Genomic studies frequently seek to uncover relationships between many subjects of interest (e.g., genes), and probe how these relationships change across multiple conditions (e.g., in different tissues or cell types). To elucidate shared and differential patterns among subjects across many conditions, it is desirable to analyze all conditions jointly, as condition-by-condition analyses are severely underpowered to uncover shared or differential effects. Several joint analyses have been developed, yet each still suffers from some critical drawbacks both in model flexibility and computational intractability for even a modest number of conditions due to a combinatorial explosion in the number of latent classes included in the model. Using a novel limited information mixture model, we sidestep this computational intractability by paring down the collection of candidate latent classes. We then feed this output into an empirical Bayesian framework which simultaneously performs classification and parameter estimation, without making the restrictive assumptions made by competing methods.

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

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