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Estimating Drivers of Cell State Transitions Using Gene Regulatory Network Models

Daniel Schlauch, Kimberly Glass, Craig P. Hersh, Edwin K. Silverman, John Quackenbush
doi: https://doi.org/10.1101/089003
Daniel Schlauch
aDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA 02115
bChanning Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115
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Kimberly Glass
bChanning Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115
cDepartment of Medicine, Harvard Medical School, Boston, MA 02115
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Craig P. Hersh
bChanning Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115
cDepartment of Medicine, Harvard Medical School, Boston, MA 02115
dPulmonary and Critical Care Division, Brigham and Women’s Hospital and Harvard Medical School, Boston, USA
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Edwin K. Silverman
bChanning Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115
cDepartment of Medicine, Harvard Medical School, Boston, MA 02115
dPulmonary and Critical Care Division, Brigham and Women’s Hospital and Harvard Medical School, Boston, USA
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John Quackenbush
aDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA 02115
bChanning Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115
cDepartment of Medicine, Harvard Medical School, Boston, MA 02115
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Abstract

Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. Our results demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 21, 2016.
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Estimating Drivers of Cell State Transitions Using Gene Regulatory Network Models
Daniel Schlauch, Kimberly Glass, Craig P. Hersh, Edwin K. Silverman, John Quackenbush
bioRxiv 089003; doi: https://doi.org/10.1101/089003
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Estimating Drivers of Cell State Transitions Using Gene Regulatory Network Models
Daniel Schlauch, Kimberly Glass, Craig P. Hersh, Edwin K. Silverman, John Quackenbush
bioRxiv 089003; doi: https://doi.org/10.1101/089003

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