A Nonparametric Bayesian Method for Clustering of High-Dimensional Mixed Dataset
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Motivation: Advances in next-generation sequencing (NGS) methods have enabled researchers and agencies to collect a wide variety of sequencing data across multiple platforms. The motivation behind such an exercise is to analyze these datasets jointly, in order to gain insights into disease prognosis, treatment, and cure. Clustering of such datasets, can provide much needed insight into biological associations. However, the differing scale, and the heterogeneity of the mixed dataset is hurdle for such analyses. Results: The paper proposes a nonparameteric Bayesian approach called Gen-VariScan for biclustering of high-dimensional mixed data. Generalized Linear Models (GLM), and latent variable approaches are utilized to integrate mixed dataset. Sparsity inducing property of Poisson Dirichlet Process (PDP) is used to identify a lower dimensional structure of mixed covariates. We apply our method to Glioblastoma Multiforme (GBM) cancer dataset. We show that cluster detection is aposteriori consistent, as number of covariates and subject grows. As a byproduct, we derive a working value approach to perform beta regression.
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