Multivariate hierarchical Bayesian model for differential gene expression analysis in microarray experiments

Background: Identification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increasingly popular to solve this type of problems. These models show good performance in accommodating noise, variability and low replication of microarray data. However, the correlation between different fluorescent signals measured from a gene spot is ignored, which can diversely affect the data analysis step. In fact, the intensities of the two signals are significantly correlated across samples. The larger the log-transformed intensities are, the smaller the correlation is. Results: Motivated by the complicated error relations in microarray data, we propose a multivariate hierarchical Bayesian framework for data analysis in the replicated microarray experiments. Gene expression data are modelled by a multivariate normal distribution, parameterized by the corresponding mean vectors and covariance matrixes with ...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Hongya Zhao, Kwok-Leung Chan, Lee-Ming Cheng, Hong Yan
• Keywords: Bayesian Model, BMCBI 2008, Gene Expression Data, Microarray Data