A Multivariate Poisson-Log Normal Mixture Model for Clustering Transcriptome Sequencing Data

TL;DR

This paper introduces a mixture model based on the multivariate Poisson-Log Normal distribution for clustering high-dimensional, discrete, and skewed transcriptome sequencing data, facilitating the discovery of gene co-expression groups.

Contribution

It proposes a novel MPLN mixture model tailored for RNA sequencing data, with a specific MCMC-EM algorithm for parameter estimation and model selection.

Findings

Effective modeling of correlation and overdispersion in count data

Successful identification of gene clusters in transcriptome data

Demonstrated advantages over traditional clustering methods

Abstract

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.