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Estimating Sample-Specific Regulatory Networks

49 Pages Posted: 24 Sep 2018 Sneak Peek Status: Under Review

See all articles by Marieke Lydia Kuijjer

Marieke Lydia Kuijjer

Dana-Farber/Harvard Cancer Center - Department of Biostatistics and Computational Biology; Harvard University - T.H. Chan School of Public Health

Matthew Tung

Harvard University - Harvard Medical School; Harvard University - Massachusetts General Hospital

Guocheng Yuan

Dana-Farber/Harvard Cancer Center - Department of Biostatistics and Computational Biology; Harvard University - T.H. Chan School of Public Health

John Quackenbush

Dana-Farber/Harvard Cancer Center - Department of Biostatistics and Computational Biology; Harvard University - T.H. Chan School of Public Health

Kimberly Glass

Harvard University - Channing Division of Network Medicine; Harvard University - Harvard Medical School

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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 of those interactions. These methods often draw on large numbers of samples with measured gene expression profiles to 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 lymphoblastoid cell lines. We show that these sample-specific networks can be used to study changes in network topology across time and to characterize shifts in gene regulation that may not be apparent in expression data. We believe the ability to generate sample-specific networks will greatly facilitate the application of network methods to the increasingly large, complex, and heterogeneous multi-omic data-sets that are currently being generated, and ultimately support the emerging field of precision network medicine.

Suggested Citation

Kuijjer, Marieke Lydia and Tung, Matthew and Yuan, Guocheng and Quackenbush, John and Glass, Kimberly, Estimating Sample-Specific Regulatory Networks (September 22, 2018). Available at SSRN: https://ssrn.com/abstract=3253573 or http://dx.doi.org/10.2139/ssrn.3253573
This is a paper under consideration at Cell Press and has not been peer-reviewed.

Marieke Lydia Kuijjer (Contact Author)

Dana-Farber/Harvard Cancer Center - Department of Biostatistics and Computational Biology

450 Brookline Avenue
Boston, MA 02215
United States

Harvard University - T.H. Chan School of Public Health

677 Huntington Avenue
Boston, MA MA 02115
United States

Matthew Tung

Harvard University - Harvard Medical School

25 Shattuck St
Boston, MA 02115
United States

Harvard University - Massachusetts General Hospital

55 Fruit Street Boston
Boston, MA 02114
United States

Guocheng Yuan

Dana-Farber/Harvard Cancer Center - Department of Biostatistics and Computational Biology

450 Brookline Avenue
Boston, MA 02215
United States

Harvard University - T.H. Chan School of Public Health

677 Huntington Avenue
Boston, MA MA 02115
United States

John Quackenbush

Dana-Farber/Harvard Cancer Center - Department of Biostatistics and Computational Biology

450 Brookline Avenue
Boston, MA 02215
United States

Harvard University - T.H. Chan School of Public Health

677 Huntington Avenue
Boston, MA MA 02115
United States

Kimberly Glass

Harvard University - Channing Division of Network Medicine ( email )

75 Francis St.
Boston, MA 02115
United States

Harvard University - Harvard Medical School

25 Shattuck St
Boston, MA 02115
United States