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Gene Regulatory Networks pp 283–302Cite as

Unsupervised GRN Ensemble

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1883))

Abstract

Inferring gene regulatory networks from expression data is a very challenging problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensus mechanism naturally arises. In this chapter, a common framework to standardize already proposed consensus methods is presented, and based on this framework different proposals are introduced and analyzed in two different scenarios: Homogeneous and Heterogeneous. The first scenario reflects situations where the networks to be aggregated are rather similar because they are obtained with inference algorithms working on the same data, whereas the second scenario deals with very diverse networks because various sources of data are used to generate the individual networks. A procedure for combining multiple network inference algorithms is analyzed in a systematic way. The results show that there is a very significant difference between these two scenarios, and that the best way to combine networks in the Heterogeneous scenario is not the most commonly used. We show in particular that aggregation in the Heterogeneous scenario can be very beneficial if the individual networks are combined with our new proposed method ScaleLSum.

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Author information

Authors and Affiliations

  1. Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, Bellaterra, Barcelona, Spain

    Pau Bellot

  2. Universitat Politecnica de Catalunya, Barcelona, Spain

    Philippe Salembier

  3. Bioinformatics and Systems Biology (BioSys) Unit, Université de Liège, Liège, Belgium

    Ngoc C. Pham & Patrick E. Meyer

Authors
  1. Pau Bellot
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  2. Philippe Salembier
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  3. Ngoc C. Pham
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  4. Patrick E. Meyer
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Corresponding author

Correspondence to Pau Bellot .

Editor information

Editors and Affiliations

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

    Guido Sanguinetti

  2. Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium

    Vân Anh Huynh-Thu

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Bellot, P., Salembier, P., Pham, N.C., Meyer, P.E. (2019). Unsupervised GRN Ensemble. In: Sanguinetti, G., Huynh-Thu, V. (eds) Gene Regulatory Networks. Methods in Molecular Biology, vol 1883. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8882-2_12

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  • DOI: https://doi.org/10.1007/978-1-4939-8882-2_12

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8881-5

  • Online ISBN: 978-1-4939-8882-2

  • eBook Packages: Springer Protocols

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