Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data

Authors:
Yi He;Wei Pan;Jizhen Lin
Affiliations:
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA;Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA;Department of Otolaryngology, School of Medicine, University of Minnesota, Minneapolis, MN, USA
Venue:
Computational Statistics & Data Analysis
Year:
2006

Citing 5
Cited 8

A mixture model approach for the analysis of microarray gene expression data

Computational Statistics & Data Analysis
Model-based methods for identifying periodically expressed genes based on time course microarray gene expression data

Bioinformatics
Variance-stabilizing transformations for two-color microarrays

Bioinformatics
A mixture model-based strategy for selecting sets of genes in multiclass response microarray experiments

Bioinformatics
Incorporating gene functions as priors in model-based clustering of microarray gene expression data

Bioinformatics

Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm

Computational Statistics & Data Analysis
Adaptive clustering for time series: Application for identifying cell cycle expressed genes

Computational Statistics & Data Analysis
An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization

Expert Systems with Applications: An International Journal
Which Distance for the Identification and the Differentiation of Cell-Cycle Expressed Genes?

IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
A random-periods model for the comparison of a metrics efficiency to classify cell-cycle expressed genes

Pattern Recognition Letters
A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering

Expert Systems with Applications: An International Journal
A review on particle swarm optimization algorithms and their applications to data clustering

Artificial Intelligence Review
Parametric Estimation of the Local False Discovery Rate for Identifying Genetic Associations

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

Quantified Score

Hi-index	0.03

Visualization

Abstract

DNA microarrays make it possible to study simultaneously the expression of thousands of genes in a biological sample. Univariate clustering techniques have been used to discover target genes with differential expression between two experimental conditions. Because of possible loss of information due to use of univariate summary statistics, it may be more effective to use multivariate statistics. We present multivariate normal mixture model based clustering analyses to detect differential gene expression between two conditions. Deviating from the general mixture model and model-based clustering, we propose mixture models with specific mean and covariance structures that account for special features of two-condition microarray experiments. Explicit updating formulas in the EM algorithm for three such models are derived. The methods are applied to a real dataset to compare the expression levels of 1176 genes of rats with and without pneumococcal middle-ear infection to illustrate the performance and usefulness of this approach. About 10 genes and 20 genes are found to be differentially expressed in a six-dimensional modeling and a bivariate modeling, respectively. Two simulation studies are conducted to compare the performance of univariate and multivariate methods. Depending on data, neither method can always dominate the other. The results suggest that multivariate normal mixture models can be useful alternatives to univariate methods to detect differential gene expression in exploratory data analysis.