title: An empirical Bayes approach to network recovery using external knowledge.
abstract: Reconstruction of a high-dimensional network may benefit substantially from the inclusion of prior knowledge on the network topology. In the case of gene interaction networks such knowledge may come for instance from pathway repositories like KEGG, or be inferred from data of a pilot study. The Bayesian framework provides a natural means of including such prior knowledge. Based on a Bayesian Simultaneous Equation Model, we develop an appealing Empirical Bayes (EB) procedure that automatically assesses the agreement of the used prior knowledge with the data at hand. We use variational Bayes method for posterior densities approximation and compare its accuracy with that of Gibbs sampling strategy. Our method is computationally fast, and can outperform known competitors. In a simulation study, we show that accurate prior data can greatly improve the reconstruction of the network, but need not harm the reconstruction if wrong. We demonstrate the benefits of the method in an analysis of gene expression data from GEO. In particular, the edges of the recovered network have superior reproducibility (compared to that of competitors) over resampled versions of the data.
authors.affiliation: Department of Mathematics, University of Leiden, Niels Bohrweg 1, 2333, CA Leiden, The Netherlands.
authors.forename: Gino B
authors.initials: GB
authors.lastname: Kpogbezan
authors.affiliation: Department of Mathematics, University of Leiden, Niels Bohrweg 1, 2333, CA Leiden, The Netherlands.
authors.forename: Aad W
authors.initials: AW
authors.lastname: van der Vaart
authors.affiliation: Department of Mathematics, Vrije Universiteit Amsterdam, De Boelelaan 1081, 1081, HV Amsterdam, The Netherlands.|Department of Epidemiology and Biostatistics, VU University Medical Center, 1007, MB, Amsterdam, The Netherlands.
authors.forename: Wessel N
authors.initials: WN
authors.lastname: van Wieringen
authors.affiliation: MRC Biostatistics Unit, Cambridge Institute of Public Health, Forvie Site, Cambridge, CB2 0SR, United Kingdom.
authors.forename: Gwenaël G R
authors.initials: GGR
authors.lastname: Leday
authors.affiliation: Department of Mathematics, Vrije Universiteit Amsterdam, De Boelelaan 1081, 1081, HV Amsterdam, The Netherlands.|Department of Epidemiology and Biostatistics, VU University Medical Center, 1007, MB, Amsterdam, The Netherlands.
authors.forename: Mark A
authors.initials: MA
authors.lastname: van de Wiel
mesh_terms: D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D053263:Gene Regulatory Networks; D015233:Models, Statistical; D010865:Pilot Projects; D015203:Reproducibility of Results
keywords: Empirical Bayes; High-dimensional Bayesian inference; Prior information; Undirected network; Variational approximation