title: hARACNe: improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests.
abstract: A key goal of systems biology is to elucidate molecular mechanisms associated with physiologic and pathologic phenotypes based on the systematic and genome-wide understanding of cell context-specific molecular interaction models. To this end, reverse engineering approaches have been used to systematically dissect regulatory interactions in a specific tissue, based on the availability of large molecular profile datasets, thus improving our mechanistic understanding of complex diseases, such as cancer. In this paper, we introduce high-order Algorithm for the Reconstruction of Accurate Cellular Network (hARACNe), an extension of the ARACNe algorithm for the dissection of transcriptional regulatory networks. ARACNe uses the data processing inequality (DPI), from information theory, to detect and prune indirect interactions that are unlikely to be mediated by an actual physical interaction. Whereas ARACNe considers only first-order indirect interactions, i.e. those mediated by only one extra regulator, hARACNe considers a generalized form of indirect interactions via two, three or more other regulators. We show that use of higher-order DPI resulted in significantly improved performance, based on transcription factor (TF)-specific ChIP-chip data, as well as on gene expression profile following RNAi-mediated TF silencing.
authors: Sage Bionetworks , 1100 Fairview Avenue North, Seattle, WA 98109 , USA.
authors: In Sock
authors: IS
authors: Jang
authors: Sage Bionetworks , 1100 Fairview Avenue North, Seattle, WA 98109 , USA.
authors: Adam
authors: A
authors: Margolin
authors: Department of Systems Biology, Biochemistry and Molecular Biophysics, Biomedical Informatics, and Herbert Irving Comprehensive Cancer Center , Columbia University Medical Center , New York, NY 10032 , USA.
authors: Andrea
authors: A
authors: Califano
keywords: ARACNe; higher-order data processing inequality; information theory; reverse engineering; transcriptional regulatory network