A bi-Poisson model for clustering gene expression profiles by RNA-seq

Abstract

With the availability of gene expression data by RNA-seq, powerful statistical approaches for grouping similar gene expression profiles across different environments have become increasingly important. We describe and assess a computational model for clustering genes into distinct groups based on the pattern of gene expression in response to changing environment. The model capitalizes on the Poisson distribution to capture the count property of RNA-seq data. A two-stage hierarchical expectation–maximization (EM) algorithm is implemented to estimate an optimal number of groups and mean expression amounts of each group across two environments. A procedure is formulated to test whether and how a given group shows a plastic response to environmental changes. The impact of gene–environment interactions on the phenotypic plasticity of the organism can also be visualized and characterized. The model was used to analyse an RNA-seq dataset measured from two cell lines of breast cancer that respond differently to an anti-cancer drug, from which genes associated with the resistance and sensitivity of the cell lines are identified. We performed simulation studies to validate the statistical behaviour of the model. The model provides a useful tool for clustering gene expression data by RNA-seq, facilitating our understanding of gene functions and networks.

A block mixture model to map eQTLs for gene clustering and networking

“…This new model displays a tremendous methodological breakthrough embodied in the two following aspects: First, existing gene clustering approaches classify tens of thousands of genes recorded on a single biological entity, such as a cell type, an organ, a treatment, or an individual. Although considerable efforts have been made to cluster genes simultaneously on several entities 24 25 26 , no studies thus far have been able to tackle gene clusters on a high-dimensional set of entities which contain unknown latent components. The new model capitalizes on the advantage of a block mixture model for the simultaneous identification of latent gene clusters or latent genotypes from high-dimensional genes × high-dimensional genotype expression data.…”

“…Functional clustering, aimed to classify gene expression profiles based on their dynamic changes using parametric or nonparametric approaches 27 28 29 , can be integrated with the block mixture model, which allows dynamic eQTLs for gene clustering to be characterized. Second, to study how the organism responds to changing environment, gene expression experiments frequently include multiple environments or multiple tissues 24 25 26 . The implementation of the block mixture model with multiple environments enables us to understand the impact of genotype-environment interactions on regulatory machineries.…”

DGEclust: differential expression analysis of clustered count data

“…For example, both the k -means and the self-organising map algorithms require the number of clusters as input. Similarly, methods that model the data as a finite mixture of Poisson or negative binomial distributions [ 61 - 63 ] require prior knowledge of the number of mixture components. Estimating the number of clusters usually makes use of an optimality criterion, such as the Bayesian information criterion or the Akaike information criterion, which requires repeated application of the algorithm on the same dataset with different initial choices of the number of clusters.…”

Modeling Expression Plasticity of Genes that Differentiate Drug-sensitive from Drug-resistant Cells to Chemotherapeutic Treatment

“…The expression reads of genes in each treatment are thought to obey a Poisson distribution [19], thus the distribution of the read differences between the two cell types is modeled by the Skellam function [22, 23], expressed as …”

“…More recently, Wang et al [ 19 ] developed a bi-variate Poisson model to cluster genes expressed in two different environments. Jiang et al [ 20 ] derived an algorithmic model for clustering genes based on their environment-induced differentiation patterns.…”