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Unsupervised GRN Ensemble

  • Pau BellotEmail author
  • Philippe Salembier
  • Ngoc C. Pham
  • Patrick E. Meyer
Protocol
Part of the Methods in Molecular Biology book series (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.

Key words

Consensus network algorithms Meta-analysis Gene regulatory networks Gene expression data 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Pau Bellot
    • 1
    Email author
  • Philippe Salembier
    • 2
  • Ngoc C. Pham
    • 3
  • Patrick E. Meyer
    • 3
  1. 1.Centre for Research in Agricultural Genomics (CRAG)CSIC-IRTA-UAB-UB ConsortiumBellaterra, BarcelonaSpain
  2. 2.Universitat Politecnica de CatalunyaBarcelonaSpain
  3. 3.Bioinformatics and Systems Biology (BioSys) UnitUniversité de LiègeLiègeBelgium

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