Gene association networks from microarray data using a regularized estimation of partial correlation based on PLS Regression

Arthur Tenenhaus^a, Vincent~Guillemot^a,b, Xavier Gidrol^a and Vincent Frouin^a

^a CEA, Laboratoire d'Exploration Fonctionnelle des Génomes, 2 rue Gaston Crémieux - 91000 Evry

^b Department of Signal and Electronic Systems, Supélec, Plateau de Moulon, 3 rue Joliot-Curie, Gif sur Yvette, 91192, France

Abstract

Reconstruction of gene-gene interactions from large scale data such as microarray is a first step toward better understanding the mechanisms at work in the cell. Two main issues have to be managed in such a context: (i) to choose which measures have to be used to distinguish between direct and indirect interactions from high dimensional microarray data and (ii) to construct networks with a low proportion of false positive edges. We present an efficient methodology for the reconstruction of gene interaction networks in a small sample size setting. The strength of independence of any two genes is measured, in such "high dimensional network", by a regularized estimation of the partial correlation based on the Partial Least Squares Regression. We finally emphasize specific properties of the proposed method.

To assess the sensitivity and specificity of the method, we carried out the reconstruction of networks from simulated data. We also tested PLS-based partial correlation network on static and dynamic real microarray data.

An R implementation of the proposed algorithm is available from http://biodev.extra.cea/fr/plspcnetwork

R code