Online Program Home
My Program

Abstract Details

Activity Number: 615
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #321258 View Presentation
Title: Flexible Prior Specification Through Empirical Reparameterization in Hierarchical Models for RNA-Seq Experiments
Author(s): Andrew Lithio* and Dan Nettleton
Companies: Iowa State University and Iowa State University
Keywords: RNA-seq ; empirical-Bayes ; ShrinkBayes ; reparameterization

Naive approaches to prior specification for models of high-throughput RNA sequencing (RNA-seq) data often fail to adequately account for the strong correlations within each gene. As a computationally efficient alternative to allowing for an unstructured variance-covariance matrix for regression parameters, we use the spectral decomposition of the sample variance-covariance matrix of the maximum likelihood estimates to obtain an orthogonal reparameterization. This approach makes use of the high dimensionality of RNA-seq data to specify a nearly uncorrelated set of parameters, and allows for simplified prior specification in a Bayesian or empirical Bayesian approach. Using the R package ShrinkBayes, we demonstrate that, compared to a naive approach, the empirical reparameterization leads to more accurate estimation of hyperparameters and improved performance in tests for differential expression.

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

Back to the full JSM 2016 program

Copyright © American Statistical Association