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Abstract Details

Activity Number: 604
Type: Topic Contributed
Date/Time: Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #306086
Title: Using Finite Poisson Mixture Models for RNA-Seq Data Analysis and Transcript Expression Level Quantification
Author(s): Yu Zhu*+ and Han Wu and Zhaohui Qin
Companies: Purdue University and Purdue University and Emory University
Address: , , ,
Keywords: RNA-Seq ; gene transcript expressioin ; Bayesian Information Criterion ; Poisson mixture models

RNA-Seq has emerged as a powerful technique for transcriptome study. In this talk, we propose to use finite Poisson mixture models to characterize base pair-level RNA-Seq data and further quantify transcript/gene expression levels. Finite Poisson mixture models combine the strength of fully parametric models with the flexibility of fully nonparametric models, and are extremely suitable for modeling heterogeneous count data such as what we observe from RNA-Seq experiments. In particular, we consider three types of Poisson mixture models and propose to use a BIC-based model selection procedure to adapt the models to individual transcripts. A unified quantification method based on the Poisson mixture models is developed to measure transcript/gene expression level The Poisson mixture models and the proposed quantification method were applied to analyze two RNA-Seq data sets and demonstrated excellent performances in comparison with existing methods. Our approach resulted in better characterization of the data and more accurate measurements of transcript expression levels.

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