This article is part of the supplement: Genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional Loci .

Proceedings

Mixture modeling of microarray gene expression data

Yang Yang¹ , Adam P Tashman¹ , Jung Yeon Lee¹ , Seungtai Yoon² , Wenyang Mao¹ , Kwangmi Ahn³ , Wonkuk Kim¹ , Nancy R Mendell¹ , Derek Gordon⁴ and Stephen J Finch¹

¹  Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11790, USA

²  Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA

³  Department of Health Evaluation Sciences, A210, Penn State College of Medicine, 600 Centerview Drive, Hershey, Pennsylvania 17033, USA

⁴  Department of Genetics, Rutgers University, 145 Bevier Road, Room 128, Piscataway, New Jersey 08854, USA

author email corresponding author email

BMC Proceedings 2007, 1(Suppl 1):S50

Published:	18 December 2007

Abstract

About 28% of genes appear to have an expression pattern that follows a mixture distribution. We use first- and second-order partial correlation coefficients to identify trios and quartets of non-sex-linked genes that are highly associated and that are also mixtures. We identified 18 trio and 35 quartet mixtures and evaluated their mixture distribution concordance. Concordance was defined as the proportion of observations that simultaneously fall in the component with the higher mean or simultaneously in the component with the lower mean based on their Bayesian posterior probabilities. These trios and quartets have a concordance rate greater than 80%. There are 33 genes involved in these trios and quartets. A factor analysis with varimax rotation identifies three gene groups based on their factor loadings. One group of 18 genes has a concordance rate of 56.7%, another group of 8 genes has a concordance rate of 60.8%, and a third group of 7 genes has a concordance rate of 69.6%. Each of these rates is highly significant, suggesting that there may be strong biological underpinnings for the mixture mechanisms of these genes. Bayesian factor screening confirms this hypothesis by identifying six single-nucleotide polymorphisms that are significantly associated with the expression phenotypes of the five most concordant genes in the first group.