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Wisdom of crowds for robust gene network inference

Nature Methods volume 9, pages 796–804 (2012)Cite this article

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Abstract

Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ∼1,700 transcriptional interactions at a precision of ∼50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.

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Figure 1: The DREAM5 network inference challenge.
Figure 2: Evaluation of network inference methods.
Figure 3: Analysis of community networks compared to individual inference methods.
Figure 4: E. coli and S. aureus community networks.

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Acknowledgements

We thank all challenge participants for their invaluable contribution; R. Norel and J. Saez Rodriguez, who participated in different aspects of the organization and scoring of DREAM5; and P. Carr, M. Reich, J. Mesirov and the rest of the GenePattern team for providing software and support. This work was funded by the US National Institutes of Health (NIH) National Centers for Biomedical Computing Roadmap Initiative (U54CA121852), Howard Hughes Medical Institute, NIH Director's Pioneer Award DPI OD003644 and a fellowship from the Swiss National Science Foundation to D.M. Challenge participants acknowledge: grants ANR-07-BLAN-0311-03 and ANR-09-BLAN-0051-04 from the French National Research Agency (A.-C.H., P.V.-L., F.M., J.-P.V.); the Interuniversity Attraction Poles Programme (IAP P6/25 BIOMAGNET), initiated by the Belgian State, Science Policy Office, the French Community of Belgium (ARC Biomod) and the European Network of Excellence PASCAL2 (V.A.H.-T., A.I., L.W., Y.S., P.G.); the European Community's 7th Framework Program, grant no. HEALTH-F4-2007-200767 for the APO-SYS program, and a doctoral fellowship from the Edmond J. Safra Bioinformatics Program at Tel Aviv University (G.K., R.S.); the Irish Research Council for Science Engineering and Technology for financial support under the EMBARK scheme, and the Irish Centre for High-End Computing for provision of computational facilities and technical support (A. Sîrbu, H.J.R., M.C.); the US National Cancer Institute grant U54CA132383 and US National Science Foundation grant HRD-0420407 (Z.O., Y.Z., H.W., M.S.); and the Sardinian Regional Authorities (A.F., A.P., N.S., V.L.). V.A.H.-T. is recipient of a fellowship from the Fonds pour la formation à la Recherche dans l′Industrie et dans l′Agriculture (F.R.I.A., Belgium); Y.S. is a postdoctoral fellow of the Fonds voor Wetenschappelijk Onderzoek - Vlaanderen (FWO, Belgium); P.G. is Research Associate of the Fonds National de la Recherche Scientifique (FNRS, Belgium).

Author information

Author notes

  1. Yvan Saeys

    Present address: Department of Plant Systems Biology, Flanders Institute for Biotechnology (VIB), Gent, Belgium

  2. Diogo M Camacho

    Present address: Present address: Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, Massachusetts, USA.,

  3. Daniel Marbach, James C Costello, Robert Küffner and Yvan Saeys: These authors contributed equally to this work.

Authors and Affiliations

  1. Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USA

    Daniel Marbach, Benjamin Holmes, Manolis Kellis, Daniel Marbach & Manolis Kellis

  2. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Daniel Marbach, Benjamin Holmes, Manolis Kellis, Daniel Marbach & Manolis Kellis

  3. Howard Hughes Medical Institute, Boston, Massachusetts, USA

    James C Costello, Nicole M Vega, Diogo M Camacho, Kyle R Allison, Kyle R Allison, Diogo M Camacho, James J Collins, James C Costello, Nicci Vega & James J Collins

  4. Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA

    James C Costello, Nicole M Vega, Diogo M Camacho, Kyle R Allison, Kyle R Allison, Diogo M Camacho, James J Collins, James C Costello, Nicci Vega & James J Collins

  5. Center for BioDynamics, Boston University, Boston, Massachusetts, USA

    James C Costello, Nicole M Vega, Diogo M Camacho, Kyle R Allison, Kyle R Allison, Diogo M Camacho, James J Collins, James C Costello, Nicci Vega & James J Collins

  6. Department of Informatics, Ludwig-Maximilians University, Munich, Germany

    Robert Küffner, Robert Küffner, Tobias Petri, Lukas Windhager & Ralf Zimmer

  7. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Robert J Prill, Robert J Prill, Gustavo Stolovitzky & Gustavo Stolovitzky

  8. Wyss Institute, Harvard University, Boston, Massachusetts, USA

    James J Collins & James J Collins

  9. Biomathematics and Statistics Scotland, Edinburgh & Aberdeen, UK.,

    Andrej Aderhold, Frank Dondelinger & Dirk Husmeier

  10. School of Biology, University of St. Andrews, St. Andrews, UK

    Andrej Aderhold

  11. Department of Biology, New York University, New York, NY, USA

    Richard Bonneau, Aviv Madar & Christopher S Poultney

  12. Center for Genomics & Systems Biology, New York University, New York, NY, USA

    Richard Bonneau, Aviv Madar & Christopher S Poultney

  13. Computational Biology Program, New York University Sackler School of Medicine, New York, New York, USA

    Richard Bonneau & Alex Greenfield

  14. Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, New York, USA

    Richard Bonneau

  15. Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA

    Yukun Chen & Subramani Mani

  16. Department of Computer Science, University of Turin, Torino, Italy

    Francesca Cordero, Roberto Esposito & Alessia Visconti

  17. Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy

    Francesca Cordero

  18. Centre for Scientific Computing and Complex Systems Modelling, School of Computing, Dublin City University, Dublin, Ireland

    Martin Crane, Heather J Ruskin & Alina Sîrbu

  19. School of Informatics, University of Edinburgh, Edinburgh, UK

    Frank Dondelinger

  20. Department of Statistics, University of Chicago, Chicago, Illinois, USA

    Mathias Drton, Rina Foygel & Serena Rezny

  21. CRS4 Bioinformatica, Parco Tecnologico della Sardegna, Pula, Italy

    Alberto de la Fuente, Vincenzo De Leo, Andrea Pinna & Nicola Soranzo

  22. Department of Statistics, Ludwig-Maximilians University, Munich, Germany

    Jan Gertheiss & Torsten Hothorn

  23. Department of Electrical Engineering and Computer Science, Systems and Modeling, University of Liège, Liège, Belgium

    Pierre Geurts, Vân Anh Huynh-Thu, Alexandre Irrthum & Louis Wehenkel

  24. GIGA-Research, Bioinformatics and Modeling, University of Liège, Liège, Belgium

    Pierre Geurts, Vân Anh Huynh-Thu, Alexandre Irrthum & Louis Wehenkel

  25. Department of Statistics, TU Dortmund University, Germany

    Marco Grzegorczyk

  26. Mines ParisTech, Center for Computational Biology (CBIO), Fontainebleau, France

    Anne-Claire Haury, Fantine Mordelet, Paola Vera-Licona & Jean-Philippe Vert

  27. Institut Curie, Centre de Recherche, Paris, France

    Anne-Claire Haury, Fantine Mordelet, Paola Vera-Licona & Jean-Philippe Vert

  28. Institut National de la Santé et de la Recherche Médicale (INSERM), U900, Paris, France

    Anne-Claire Haury, Fantine Mordelet, Paola Vera-Licona & Jean-Philippe Vert

  29. The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel

    Guy Karlebach & Ron Shamir

  30. Computer Sciences and Remote Sensing Laboratory (LSIIT), Mixed Research Unit (UMR) UdS-CNRS 7005, Université de Strasbourg, Strasbourg, France

    Sophie Lèbre

  31. Linkalab, Complex Systems Computational Laboratory, Cagliari, Italy

    Vincenzo De Leo

  32. Centre de Recherche en Économie et Statistique (CREST), National Institute of Statistics and Economic Studies (INSEE), Malakoff, France

    Fantine Mordelet

  33. Department of Pediatrics, Human Genetics Program, New York University Langone Medical Center, New York, New York, USA

    Harry Ostrer

  34. Department of Computer Science, New Mexico State University, Las Cruces, New Mexico, USA

    Zhengyu Ouyang, Mingzhou Song, Haizhou Wang & Yang Zhang

  35. Microsoft Research, Redmond, Washington, USA

    Ravi Pandya

  36. Department of Plant Biotechnology and Bioinformatics, Ghent University, Gent, Belgium

    Yvan Saeys

  37. Center for Health Informatics and Bioinformatics, New York University, New York, New York, USA

    Alexander Statnikov

Authors
  1. Daniel Marbach
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  2. James C Costello
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  3. Robert Küffner
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  4. Nicole M Vega
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  5. Robert J Prill
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  6. Diogo M Camacho
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  7. Kyle R Allison
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  8. Manolis Kellis
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  10. Gustavo Stolovitzky
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Consortia

The DREAM5 Consortium

  • Andrej Aderhold
  • , Kyle R Allison
  • , Richard Bonneau
  • , Diogo M Camacho
  • , Yukun Chen
  • , James J Collins
  • , Francesca Cordero
  • , James C Costello
  • , Martin Crane
  • , Frank Dondelinger
  • , Mathias Drton
  • , Roberto Esposito
  • , Rina Foygel
  • , Alberto de la Fuente
  • , Jan Gertheiss
  • , Pierre Geurts
  • , Alex Greenfield
  • , Marco Grzegorczyk
  • , Anne-Claire Haury
  • , Benjamin Holmes
  • , Torsten Hothorn
  • , Dirk Husmeier
  • , Vân Anh Huynh-Thu
  • , Alexandre Irrthum
  • , Manolis Kellis
  • , Guy Karlebach
  • , Robert Küffner
  • , Sophie Lèbre
  • , Vincenzo De Leo
  • , Aviv Madar
  • , Subramani Mani
  • , Daniel Marbach
  • , Fantine Mordelet
  • , Harry Ostrer
  • , Zhengyu Ouyang
  • , Ravi Pandya
  • , Tobias Petri
  • , Andrea Pinna
  • , Christopher S Poultney
  • , Robert J Prill
  • , Serena Rezny
  • , Heather J Ruskin
  • , Yvan Saeys
  • , Ron Shamir
  • , Alina Sîrbu
  • , Mingzhou Song
  • , Nicola Soranzo
  • , Alexander Statnikov
  • , Gustavo Stolovitzky
  • , Nicci Vega
  • , Paola Vera-Licona
  • , Jean-Philippe Vert
  • , Alessia Visconti
  • , Haizhou Wang
  • , Louis Wehenkel
  • , Lukas Windhager
  • , Yang Zhang
  •  & Ralf Zimmer

Contributions

D.M., J.C.C., D.M.C., R.J.P., M.K., J.J.C. and G.S. conceived the challenge; R.J.P. and G.S. performed team scoring; N.M.V. and K.R.A. performed experimental validation; D.M., J.C.C., R.K., R.J.P. and G.S. performed research; D.M., J.C.C., R.K., N.M.V., R.J.P., K.R.A., M.K., J.J.C. and G.S. analyzed results; D.M., J.C.C., R.K., M.K., J.J.C. and G.S. wrote the paper; and challenge participants performed network inference and provided method descriptions.

Corresponding author

Correspondence to Gustavo Stolovitzky.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Notes 1–10 (PDF 19429 kb)

Supplementary Data 1

DREAM5 network inference challenge (expression data, experiment descriptions, gene names, gold standards and evaluation scripts) (ZIP 37067 kb)

Supplementary Data 2

DREAM5 method scores (AUPR, AUROC and overall score) and summaries (XLS 57 kb)

Supplementary Data 3

DREAM5 network predictions of all individual methods and the community (ZIP 77113 kb)

Supplementary Data 4

E. coli, S. aureus and S. cerevisiae community networks (all predictions) (ZIP 5322 kb)

Supplementary Data 5

E. coli and S. aureus community networks at 50% precision cutoff (ZIP 192 kb)

Supplementary Data 6

E. coli and S. aureus network modules (XLS 595 kb)

Supplementary Data 7

E. coli experimental support for tested interactions (XLS 390 kb)

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Marbach, D., Costello, J., Küffner, R. et al. Wisdom of crowds for robust gene network inference. Nat Methods 9, 796–804 (2012). https://doi.org/10.1038/nmeth.2016

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