An empirical Bayes approach to network recovery using external knowledge

TL;DR

This paper introduces an empirical Bayes method for network reconstruction that leverages external prior knowledge, demonstrating improved accuracy and reproducibility in gene network inference.

Contribution

It develops a novel empirical Bayes approach within a Bayesian framework that automatically evaluates the relevance of prior knowledge for network recovery.

Findings

Accurate prior knowledge significantly improves network reconstruction.

The proposed method outperforms existing competitors in accuracy and speed.

Reconstructed gene networks show higher reproducibility across resampled data.

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 procedure which automatically assesses the relevance of the used prior knowledge. 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.