Multivariate analysis of microarray data: differential expression and differential connection

Background: Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression. Results: We use sparse inverse covariance matrices and their associated graphical representation to capture the notion of gene networks. An important issue in using these models is the identification of the pattern of zeroes in the inverse covariance matrix. The limitations of existing methods for doing this are discussed and we provide a workable solution for determining the zero pattern. We then consider a method for estimating the parameters in the inverse covariance matrix which is suitable for very high dimensional matrices. We also show how to construct multivariate tests of hypotheses. These overall multivariate tests can be broken down into two components, the first one being si...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Harri T. Kiiveri
• Keywords: Artificial Intelligence, BMCBI 2011, Gene Network, Inverse Covariance, Microarray Data