A non-parametric Bayesian clustering for gene expression data
Liming Wang, Xiaodong Wang · 2012
Clustering is an important data processing tool for interpreting microarray data and genomic network inference. In this paper, we propose a non-parametric Bayesian clustering algorithm based on the hierarchical Dirichlet processes (HDP). The proposed clustering algorithm captures the hierarchical features prevalent in biological data such as the gene express data by introducing a hierarchical structure in the model. We develop a Gibbs sampling algorithm based on the Chinese restaurant metaphor. We conduct experiments on the yeast galactose datasets and yeast cell cycle datasets by comparing our clustering results to the standard results. The proposed clustering algorithm is shown to outperform several popular clustering algorithms by revealing the underlying hierarchical structure of the data. The experiments also show that the proposed clustering algorithm provides more information and reduces the unnecessary clustering fragments than the clustering algorithm based on Dirichlet mixture model.