Model-Based Clustering for RNA-Seq Data

• Activity Number: 623
• Type: Contributed
• Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Yaqing Si*+ and Peng Liu
• Companies: Iowa State University and Iowa State University
• Keywords: Model-Based Clustering ; RNA-Seq Data ; Hierarchical Clustering ; Gene-Expression

Abstract

Next-generation sequencing technology has been exploited to measure the gene expression (mRNA) level and the resulting RNA-seq data set is an ultra-high-dimensional discrete data set. Clustering methods have been helpful in interpreting high-dimensional gene expression data resulting from microarray experiments. Here we evaluate clustering methods for RNA-seq data and also propose a model-based clustering algorithm. Real data analysis shows that the proposed algorithm has more agreement with gene annotations than other commonly used methods. We also show how to build a hierarchical tree for the clusters.