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Activity Number: 322
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #320891 View Presentation
Title: Analysis of RNA-Seq Data Using a Family of Negative Binomial Models
Author(s): Lili Zhao* and Weisheng Wu and Dai Feng and Hui Jiang and XuanLong Nguyen
Companies: University of Michigan and University of Michigan and Merck and University of Michigan and University of Michigan
Keywords: RNA-Seq ; Negative binomial ; Exon ; Transcript ; Isoform ; Differenital Analysis

The analysis of RNA-seq data has been focused on three main categories, including gene expression, (relative) exon usage and transcript expression. Many approaches have been proposed independently for each category using a negative binomial model. We proposed a family of negative binomial models (FNB), which integrates the gene, exon and transcript analysis under one unified negative binomial model. The beauty of the model is that it easily incorporates the uncertainty of assigning reads to transcripts and greatly simplifies the estimation for relative usage. We use fully tractable closed-forms ("conjugacy") for the posterior inference. Our results showed that the FNB model provides competitive alternatives to existing tools in the RNA-seq analysis.

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

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