PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Classification of microarray data using gene networks
Franck Rapaport, Andrei Zinovyev, Marie Dutreix, Emmanuel Barillot and Jean-Philippe Vert
BMC Bioinformatics Volume 8, Number 35, 2007.

Abstract

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 classification algorithms of expression profiles, resulting in classifiers with biological relevance. We illustrate the method with the analysis of a set of expression profiles from irradiated and non-irradiated yeast strains. Conclusion Including a priori knowledge of a gene network for the analysis of gene expression data leads to good classification performance and improved interpretability of the results.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:3238
Deposited By:Jean-Philippe Vert
Deposited On:29 January 2008