2018/08/13 by Chetkar Jha, Jha, Chetkar
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Clustering Algorithms Research #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gene expression and cancer classification #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.1808.04045
openalex publication_date 2018/08/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The paper is motivated from clustering problem in high-throughput mixed datasets. Clustering of such datasets can provide much insight into biological associations. An open problem in this context is to simultaneously cluster high-dimensional mixed dataset. This paper fills that gap and proposes a nonparametric Bayesian method called Gen-VariScan for biclustering of high-dimensional mixed dataset. Gen-VariScan utilizes Generalized Linear Models (GLM), and latent variable approaches to integrate mixed dataset. We make use of Poisson Dirichlet Process (PDP) to identify a lower dimensional structure of mixed covariates. We show that covariate co-cluster detection is aposteriori consistent, as the number of subject and covariates grows. The advantage of Gen-VariScan is also demonstrated through numerical simulation and data analysis. As a byproduct, we derive a working value approach to perform beta regression. Supplementary materials for this article are available online.