JSM 2011 Online Program

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Abstract Details

Activity Number: 507
Type: Contributed
Date/Time: Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #302376
Title: Model-Based Clustering for RNA-Seq Data
Author(s): Yaqing Si*+ and Peng Liu
Companies: Iowa State University and Iowa State University
Address: Snedecor 2418, Ames, IA, 50010,
Keywords: Model-Based Clustering ; RNA-Seq Data ; Hierarchical Clustering ; Gene Expresion ; Annotation
Abstract:

Recently, ultra-high throughput sequencing technology has provided an unprecedented way to study gene expression. The resulting data, termed as RNA-Seq data, is ultra-high dimensional, discrete and highly skewed. Novel statistical methods are called for its analysis. Clustering is an important method to explore the gene expression patterns and has been widely applied to gene expression data generated from microarray technology. The same clustering methods used to analyze microarray data has been applied to transformed RNA-Seq data without evaluation of its performance. Here, we examine the performance of the clustering algorithms currently applied to RNA-Seq data and propose a model-based clustering algorithm for RNA-Seq data that is shown to be better in simulation study and real data analysis .


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