  High-dimensional data of discrete and skewed nature is commonly encountered
in high-throughput sequencing studies. Analyzing the network itself or the
interplay between genes in this type of data continues to present many
challenges. As data visualization techniques become cumbersome for higher
dimensions and unconvincing when there is no clear separation between
homogeneous subgroups within the data, cluster analysis provides an intuitive
alternative. The aim of applying mixture model-based clustering in this context
is to discover groups of co-expressed genes, which can shed light on biological
functions and pathways of gene products. A mixture of multivariate Poisson-Log
Normal (MPLN) model is proposed for clustering of high-throughput transcriptome
sequencing data. The MPLN model is able to fit a wide range of correlation and
overdispersion situations, and is ideal for modeling multivariate count data
from RNA sequencing studies. Parameter estimation is carried out via a Markov
chain Monte Carlo expectation-maximization algorithm (MCMC-EM), and information
criteria are used for model selection.
