Inferring causal molecular networks: Empirical assessment through a community-based effort

Steven M. Hill, Laura M. Heiser, Thomas Cokelaer, Michael Linger, Nicole K. Nesser, Daniel E. Carlin, Yang Zhang, Artem Sokolov, Evan O. Paull, Chris K. Wong, Kiley Graim, Adrian Bivol, Haizhou Wang, Fan Zhu, Bahman Afsari, Ludmila V. Danilova, Alexander V. Favorov, Wai Shing Lee, Dane Taylor, Chenyue W. HuByron L. Long, David P. Noren, Alexander J. Bisberg, Gordon B. Mills, Joe W. Gray, Michael Kellen, Thea Norman, Stephen Friend, Amina A. Qutub, Elana J. Fertig, Yuanfang Guan, Mingzhou Song, Joshua M. Stuart, Paul T. Spellman, Heinz Koeppl, Gustavo Stolovitzky, Julio Saez-Rodriguez, Sach Mukherjee, Rami Al-Ouran, Bernat Anton, Tomasz Arodz, Omid Askari Sichani, Neda Bagheri, Noah Berlow, Anwesha Bohler, Jaume Bonet, Richard Bonneau, Gungor Budak, Razvan Bunescu, Mehmet Caglar, Binghuang Cai, Chunhui Cai, Azzurra Carlon, Lujia Chen, Mark F. Ciaccio, Gregory Cooper, Susan Coort, Chad J. Creighton, Seyed Mohammad Hadi Daneshmand, Alberto De La Fuente, Barbara Di Camillo, Joyeeta Dutta-Moscato, Kevin Emmett, Chris Evelo, Mohammad Kasim H. Fassia, Francesca Finotello, Justin D. Finkle, Xi Gao, Javier Garcia-Garcia, Samik Ghosh, Alberto Giaretta, Ruth Großeholz, Justin Guinney, Christoph Hafemeister, Oliver Hahn, Saad Haider, Takeshi Hase, Jay Hodgson, Bruce Hoff, Chih Hao Hsu, Ying Hu, Xun Huang, Mahdi Jalili, Xia Jiang, Tim Kacprowski, Lars Kaderali, Mingon Kang, Venkateshan Kannan, Kaito Kikuchi, Dong Chul Kim, Hiroaki Kitano, Bettina Knapp, George Komatsoulis, Andreas Krämer, Miron Bartosz Kursa, Martina Kutmon, Yichao Li, Xiaoyu Liang, Zhaoqi Liu, Yu Liu, Songjian Lu, Xinghua Lu, Marco Manfrini, Marta R.A. Matos, Daoud Meerzaman, Wenwen Min, Christian Lorenz Müller, Richard E. Neapolitan, Baldo Oliva, Stephen Obol Opiyo, Ranadip Pal, Aljoscha Palinkas, Joan Planas-Iglesias, Daniel Poglayen, Francesco Sambo, Tiziana Sanavia, Ali Sharifi-Zarchi, Janusz Slawek, Adam Streck, Sonja Strunz, Jesper Tegnér, Kirste Thobe, Gianna Maria Toffolo, Emanuele Trifoglio, Michael Unger, Qian Wan, Lonnie Welch, Jia J. Wu, Albert Y. Xue, Ryota Yamanaka, Chunhua Yan, Sakellarios Zairis, Michael Zengerling, Hector Zenil, Zhike Zi

Research output: Contribution to journalArticle

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Abstract

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Original languageEnglish (US)
Pages (from-to)310-322
Number of pages13
JournalNature Methods
Volume13
Issue number4
DOIs
StatePublished - Mar 30 2016

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ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

Cite this

Hill, S. M., Heiser, L. M., Cokelaer, T., Linger, M., Nesser, N. K., Carlin, D. E., Zhang, Y., Sokolov, A., Paull, E. O., Wong, C. K., Graim, K., Bivol, A., Wang, H., Zhu, F., Afsari, B., Danilova, L. V., Favorov, A. V., Lee, W. S., Taylor, D., ... Zi, Z. (2016). Inferring causal molecular networks: Empirical assessment through a community-based effort. Nature Methods, 13(4), 310-322. https://doi.org/10.1038/nmeth.3773