Reverse Engineering Gene Networks: A Comparative Study at Genome-scale

Abstract

Motivation: Reverse engineering gene networks from expression data is a widelymstudied problem, for which numerous mathematical models have been developed. Network reconstruction methods can be used to study specific pathways, or can be applied at the whole-genome scale to analyze large compendiums of expression datasets to uncover genome-wide interactions. However, few methods can scale to such large number of genes and experiments, and to date, genome-scale comparative assessment of network reconstruction methods has largely been limited to simpler organisms such as E. coli. Results: In this paper, we analyze 11,760 microarray experiments on the model plant Arabidopsis thaliana drawn from public repositories. We generate genome scale networks of Arabidopsis using three different methods -- Pearson correlation, mutual information, and graphical Gaussian modeling -- and analyze and compare these networks to test for their robustness in successfully recovering relationships between functionally related genes. We demonstrate that functional grouping of microarray experiments into different tissue types and experimental conditions is important to discover context-specific interactions. Our comparisons include benchmarking against experimentally confirmed interactions, the Arabidopsis network resource AraNet, and study of specific pathways. Our results show that networks generated by the mutual information based method have better characteristics in terms of functional modularity as measured by both connected component and sub-network extraction analysis with respect to gene sets selected from brassinosteroid and stress regulation pathways. Availability: The classification datasets and constructed genome-scale networks are publicly available at the location http://alurulab.cc.gatech.edu/arabidopsis-networks