Abstract
Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC) algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper, we present an extension of the BHC algorithm. Our Gaussian BHC (GBHC) algorithm represents data as a mixture of Gaussian distributions. It uses normal-gamma distribution as a conjugate prior on the mean and precision of each of the Gaussian components. We tested GBHC over 11 cancer and 3 synthetic datasets. The results on cancer datasets show that in sample clustering, GBHC on average produces a clustering partition that is more concordant with the ground truth than those obtained from other commonly used algorithms. Furthermore, GBHC frequently infers the number of clusters that is often close to the ground truth. In gene clustering, GBHC also produces a clustering partition that is more biologically plausible than several other state-of-the-art methods. This suggests GBHC as an alternative tool for studying gene expression data.The implementation of GBHC is available at https://sites.google.com/site/gaussianbhc/
Highlights
Clustering analysis is an important tool in studying genomic data such as gene expression profiles and can be used to infer biological function and regulation of genes
We presented a model-based clustering algorithm which employs a Gaussian mixture model to model the gene expression profiles in a Bayesian framework
We proposed two variations of the Gaussian BHC (GBHC) algorithm: GBHCTREE and GBHC-NODE, according to two different hyperpara
Summary
Clustering analysis is an important tool in studying genomic data such as gene expression profiles and can be used to infer biological function and regulation of genes. In modern medical research, clustering analysis has been used to identify disease subtypes based on genetic variation [5,6], and to identify a gene expression signature that can be used as a prognostic marker for known disease subtypes [7,8,9]. This aids stratification of patients for personalized medicine. Two common choices of metrics in gene clustering analysis literature are Euclidean distance and Pearson correlation coefficient [15]. The problems of how to identify the number of clusters and the distance metric can be cast as a model selection problem - how to choose a statistical model that best describes the data
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