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