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Activity Number: 85 - SPEED: An Ensemble of Advances in Genomics and Genetics
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 5:05 PM to 5:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #332974
Title: Empirical Bayes Analysis of Overdispersed High-Dimensional Protein Interaction Data
Author(s): Anna Reisetter*
Keywords: Bayesian; Empirical bayes; High dimensional

The yeast two-hybrid assay is a powerful tool for identifying protein-protein interactions, however, only a limited number of proteins can be assayed at a time. Researchers at the University of Iowa have adapted this method to screen and identify multiple protein interactions simultaneously, using a single replicate per gene and next generation sequencing. Using this method, yeast colony counts were obtained for non-binding control vectors and potentially binding proteins, at baseline and again once they had been subjected to an environment of selection pressure for a particular gene. Often in such count data, overdispersion is observed. A Bayesian hierarchical model termed DEEPN was developed to identify true protein-protein interactions. DEEPN accounts for the variability of the count data via estimation of separate overdispersion parameters for the baseline and selected counts. The R package, edgeR, estimates a common overdispersion parameter and allows for a less computationally intensive and time-consuming empirical Bayesian analysis. We compare DEEPN, edgeR, and negative binomial regression for analysis of the DEEPN data.

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

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