Identification of Shared Components and Sparse Networks in Gene Expression Time-Course Data

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. In this article, we develop multivariate techniques for visualizing gene regulatory networks using independent components analysis (ICA) techniques. A desirable feature of the ICA method is that it approximates a biological model for the gene expression. The methods are outlined and illustrated with application to yeast gene expression data.