Gene network reconstruction using global-local shrinkage priors

TL;DR

This paper introduces a Bayesian approach with global-local shrinkage priors for gene network reconstruction, improving stability and reproducibility over traditional sparse methods in high-dimensional molecular data analysis.

Contribution

It combines local regularization with global shrinkage in a Bayesian framework, using variational approximations and a novel posterior thresholding method.

Findings

Outperforms popular sparse methods in simulations

Yields more stable network edges

Enhances reproducibility of inferred networks

Abstract

Reconstructing a gene network from high-throughput molecular data is often a challenging task, as the number of parameters to estimate easily is much larger than the sample size. A conventional remedy is to regularize or penalize the model likelihood. In network models, this is often done locally in the neighbourhood of each node or gene. However, estimation of the many regularization parameters is often difficult and can result in large statistical uncertainties. In this paper we propose to combine local regularization with global shrinkage of the regularization parameters to borrow strength between genes and improve inference. We employ a simple Bayesian model with non-sparse, conjugate priors to facilitate the use of fast variational approximations to posteriors. We discuss empirical Bayes estimation of hyper-parameters of the priors, and propose a novel approach to rank-based posterior thresholding. Using extensive model- and data-based simulations, we demonstrate that the proposed inference strategy outperforms popular (sparse) methods, yields more stable edges, and is more reproducible.