Classification of microarray data using gene networks

Background: Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks in order to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation. Results: We propose a method to integrate a priori the knowledge of a gene network in the analysis of gene expression data. The approach is based on the spectral decomposition of gene expression profiles with respect to the eigenfunctions of the graph, resulting in an attenuation of the high-frequency components of the expression profiles with respect to the topology of the graph. We show how to derive unsupervised and supervised classifica...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Franck Rapaport, Andrei Zinovyev, Marie Dutreix, Emmanuel Barillot, Jean-Philippe Vert
• Keywords: BMCBI 2007, Gene, Gene Expression Data, Gene Network