title: Model-based clustering for RNA-seq data.
abstract: BACKGROUND
  RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important technique for RNA-seq data analysis.
  
  
  RESULTS
  In this manuscript, we derive clustering algorithms based on appropriate probability models for RNA-seq data. An expectation-maximization algorithm and another two stochastic versions of expectation-maximization algorithms are described. In addition, a strategy for initialization based on likelihood is proposed to improve the clustering algorithms. Moreover, we present a model-based hybrid-hierarchical clustering method to generate a tree structure that allows visualization of relationships among clusters as well as flexibility of choosing the number of clusters. Results from both simulation studies and analysis of a maize RNA-seq dataset show that our proposed methods provide better clustering results than alternative methods such as the K-means algorithm and hierarchical clustering methods that are not based on probability models.
  
  
  METHODS
  An R package, MBCluster.Seq, has been developed to implement our proposed algorithms. This R package provides fast computation and is publicly available at http://www.r-project.org
authors.affiliation: School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China, Department of Statistics, Iowa State University, Ames, IA 50011, USA, Institute of Tropical Biosciences and Biotechnology (ITBB), Chinese Academy of Tropical Agriculture Sciences (CATAS), Haikou, Hainan 571101, China and Enterprise Institute for Renewable Fuels, Donald Danforth Plant Science Center, St. Louis, MO 63132, USA.
authors.forename: Yaqing
authors.initials: Y
authors.lastname: Si
authors.forename: Peng
authors.initials: P
authors.lastname: Liu
authors.forename: Pinghua
authors.initials: P
authors.lastname: Li
authors.forename: Thomas P
authors.initials: TP
authors.lastname: Brutnell
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D015233:Models, Statistical; D018515:Plant Leaves / Q000378:metabolism; D003313:Zea mays / Q000235:genetics*