Comparison of single and module-based methods for modeling gene regulatory networks.

Abstract

Gene regulatory networks describe the regulatory relationships among genes, and developing methods for reverse engineering these networks is an ongoing challenge in computational biology. The majority of the initially proposed methods for gene regulatory network discovery create a network of genes and then mine it in order to uncover previously unknown regulatory processes. More recent approaches have focused on inferring modules of co-regulated genes, linking these modules with regulatory genes and then mining them to discover new molecular biology. In this work we analyze module-based network approaches to build gene regulatory networks, and compare their performance to single gene network approaches. In the process, we propose a novel approach to estimate gene regulatory networks drawing from the module-based methods. We show that generating modules of co-expressed genes which are predicted by a sparse set of regulators using a variational Bayes method, and then building a bipartite graph on the generated modules using sparse regression, yields more informative networks than previous single and module-based network approaches as measured by: (i) the rate of enriched gene sets, (ii) a network topology assessment, (iii) ChIP-Seq evidence and (iv) the KnowEnG Knowledge Network collection of previously characterized gene-gene interactions. The code is written in R and can be downloaded from https://github.com/mikelhernaez/linker. Supplementary data are available at Bioinformatics online.