Model-based clustering for RNA-seq data | Zendy

Yaqing Si | Zendy; Peng Liu | Zendy; Pinghua Li | Zendy; Thomas P. Brutnell | Zendy

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Model-based clustering for RNA-seq data

Author(s) -

Yaqing Si,

Peng Liu,

Pinghua Li,

Thomas P. Brutnell

Publication year - 2013

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btt632

Subject(s) - cluster analysis , computer science , data mining , initialization , hierarchical clustering , flexibility (engineering) , machine learning , mathematics , statistics , programming language

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.

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