RT Journal Article
SR Electronic
T1 Fused regression for multi-source gene regulatory network inference
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 049775
DO 10.1101/049775
A1 Kari Y. Lam
A1 Zachary M. Westrick
A1 Christian L. Müller
A1 Lionel Christiaen
A1 Richard Bonneau
YR 2016
UL http://biorxiv.org/content/early/2016/04/22/049775.abstract
AB Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method’s utility in learning from data collected on different experimental platforms.