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Reference: Wang L and Wang X (2013) Hierarchical Dirichlet process model for gene expression clustering. EURASIP J Bioinform Syst Biol 2013(1):5

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Abstract

Clustering is an important data processing tool for interpreting microarray data and genomic networkinference. In this article, we propose a clustering algorithm based on the hierarchical Dirichlet processes(HDP). The HDP clustering introduces a hierarchical structure in the statistical model whichcaptures the hierarchical features prevalent in biological data such as the gene express data. We developa Gibbs sampling algorithm based on the Chinese restaurant metaphor for the HDP clustering.We apply the proposed HDP algorithm to both regulatory network segmentation and gene expressionclustering. The HDP algorithm is shown to outperform several popular clustering algorithms by revealingthe underlying hierarchical structure of the data. For the yeast cell cycle data, we compare theHDP result to the standard result and show that the HDP algorithm provides more information andreduces the unnecessary clustering fragments.

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Journal Article
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Wang L, Wang X
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