TY - JOUR
T1 - Prophetic Granger Causality to infer gene regulatory networks
AU - Carlin, Daniel E.
AU - Paull, Evan O.
AU - Graim, Kiley
AU - Wong, Christopher K.
AU - Bivol, Adrian
AU - Ryabinin, Peter
AU - Ellrott, Kyle
AU - Sokolov, Artem
AU - Stuart, Joshua M.
N1 - Funding Information:
Supported by National Cancer Institute U24-CA143858, National Cancer Institute 1R01CA180778, National Human Genome Research Institute 5U54HG006097, National Institute for General Medical Sciences 5R01GM109031, and a National Science Foundation Office of Cyberinfrastructure 0845783. Supported by the West Coast Prostate Cancer Dream Team supported by Stand Up to Cancer/ AACR/Prostate Cancer Foundation SU2C-AACR-DT0812 (O.N.W. co-PI). This research Grant is made possible by the generous support of the Movember Foundation. Stand Up To Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2017 Carlin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2017/12
Y1 - 2017/12
N2 - We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory networks (GRNs) from protein-level time series data. The method uses an L1-penalized regression adaptation of Granger Causality to model protein levels as a function of time, stimuli, and other perturbations. When combined with a data-independent network prior, the framework outperformed all other methods submitted to the HPN-DREAM 8 breast cancer network inference challenge. Our investigations reveal that PGC provides complementary information to other approaches, raising the performance of ensemble learners, while on its own achieves moderate performance. Thus, PGC serves as a valuable new tool in the bioinformatics toolkit for analyzing temporal datasets. We investigate the general and cell-specific interactions predicted by our method and find several novel interactions, demonstrating the utility of the approach in charting new tumor wiring.
AB - We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory networks (GRNs) from protein-level time series data. The method uses an L1-penalized regression adaptation of Granger Causality to model protein levels as a function of time, stimuli, and other perturbations. When combined with a data-independent network prior, the framework outperformed all other methods submitted to the HPN-DREAM 8 breast cancer network inference challenge. Our investigations reveal that PGC provides complementary information to other approaches, raising the performance of ensemble learners, while on its own achieves moderate performance. Thus, PGC serves as a valuable new tool in the bioinformatics toolkit for analyzing temporal datasets. We investigate the general and cell-specific interactions predicted by our method and find several novel interactions, demonstrating the utility of the approach in charting new tumor wiring.
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U2 - 10.1371/journal.pone.0170340
DO - 10.1371/journal.pone.0170340
M3 - Article
C2 - 29211761
AN - SCOPUS:85037341151
VL - 12
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 12
M1 - e0170340
ER -