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iScience. 2019 Apr 26;14:226-240. doi: 10.1016/j.isci.2019.03.021. Epub 2019 Mar 28.

Estimating Sample-Specific Regulatory Networks.

Author information

1
Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway.
2
Department of Anesthesiology, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.
3
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
4
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
5
Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. Electronic address: kimberly.glass@channing.harvard.edu.

Abstract

Biological systems are driven by intricate interactions among molecules. Many methods have been developed that draw on large numbers of expression samples to infer connections between genes (or their products). The result is an aggregate network representing a single estimate for the likelihood of each interaction, or "edge," in the network. Although informative, aggregate models fail to capture population heterogeneity. Here we propose a method to reverse engineer sample-specific networks from aggregate networks. We demonstrate our approach in several contexts, including simulated, yeast microarray, and human lymphoblastoid cell line RNA sequencing data. We use these sample-specific networks to study changes in network topology across time and to characterize shifts in gene regulation that were not apparent in the expression data. We believe that generating sample-specific networks will greatly facilitate the application of network methods to large, complex, and heterogeneous multi-omic datasets, supporting the emerging field of precision network medicine.

KEYWORDS:

Bioinformatics; Biological Sciences; Complex Systems

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