  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.
