Estimating Sample-Specific Regulatory Networks

Abstract

Biological systems are driven by intricate interactions among the complex array of molecules that comprise the cell. Many methods have been developed to reconstruct network models that attempt to capture those interactions. These methods often draw on large numbers of samples with measured gene expression profiles to tease out subtle signals and infer connections between genes (or gene products). The result is an aggregate network model representing a single estimate for the likelihood of each interaction, or “edge,” in the network. While informative, aggregate models fail to capture the heterogeneity that is represented in any population. Here we propose a method to reverse engineer sample-specific networks from aggregate network models. We demonstrate the accuracy and applicability of our approach in several data sets, including simulated data, microarray expression data from synchronized yeast cells, and RNA-seq data collected from human subjects. We show that these sample-specific networks can be used to study the evolution of network topology across time and to characterize shifts in gene regulation that may not be apparent in the expression data. We believe the ability to generate sample-specific networks has the potential to greatly advance research in network biology and support the emerging field of precision network medicine.