Nov 6, 2013

Model-based clustering for RNA-seq data

Bioinformatics
Yaqing SiThomas P Brutnell

Abstract

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. 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 p...Continue Reading

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References

Mentioned in this Paper

In Silico
Zea mays
Gene Expression
High-Throughput Nucleotide Sequencing
Two-Parameter Models
Zea luxurians
Silo (Dataset)
Plant Leaves
MRNA Differential Display
Disease Clustering

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