Model-Based Clustering for RNA-Seq Data

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. Researchers at Iowa State University have developed clustering algorithms based on appropriate probability models for RNA-seq data. An Expectation-Maximization (EM) algorithm and another two stochastic versions of EM algorithms are described. In addition, a strategy for initialization based on likelihood is proposed to improve the clustering algorithms. Moreover, they 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.

AVAILABILITY: An R package, MBCluster.Seq, has been developed to implement our proposed algorithms. This R package provides fast computation and is publicly available at


Si Y, Liu P, Li P, Brutnell TP. (2013) Model-Based Clustering for RNA-Seq Data. Bioinformatics [Epub ahead of print]. [abstract]