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Activity Number: 527
Type: Topic Contributed
Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #309522
Title: Poisson Graphical Models for Inferring Networks from Next-Generation Sequencing Data
Author(s): Genevera Allen*+ and Zhandong Liu and Euhno Yang and Pradeep Ravikumar
Companies: Rice University and Baylor College of Medicine and The University of Texas at Austin and The University of Texas at Austin
Keywords: Markov Networks ; undirected graphical models ; Poisson models ; RNA-sequencing ; gene networks
Abstract:

Gaussian graphical models are often used to infer gene networks based on microarray expression data. Many scientists, however, have begun using next generation sequencing technologies to measure gene expression. As the resulting data consists of counts of sequencing reads for each gene, Gaussian graphical models are not optimal for this discrete data. We propose novel models and methods for Poisson Graphical Models that can be used to infer gene networks from RNA-sequencing data. Existing Poisson Markov Random Fields (MRF) permit only negative associations between variables. By restricting the domain of the variables in the joint density, we introduce a Winsorized Poisson MRF which permits a rich dependence structure. We develop neighborhood selection algorithms to estimate network structure from high-dimensional count data by fitting graphical models based on the Poisson MRF, our Winsorized Poisson MRF, and a local approximation to the Winsorized Poisson MRF. We demonstrate the advantages of our approach over existing methods for graphical models through simulations and an application to breast cancer microRNAs measured by next generation sequencing.


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