Unsupervised GRN Ensemble

  • Pau BellotEmail author
  • Philippe Salembier
  • Ngoc C. Pham
  • Patrick E. Meyer
Part of the Methods in Molecular Biology book series (MIMB, volume 1883)


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 


  1. 1.
    Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 5(1):e8CrossRefGoogle Scholar
  2. 2.
    Meyer PE, Kontos K, Lafitte F, Bontempi G (2007) Information-theoretic inference of large transcriptional regulatory networks. EURASIP J Bioinform Syst Biol, pp 8–8Google Scholar
  3. 3.
    Meyer P, Kontos K, Bontempi G (2007) Biological network inference using redundancy analysis. In: Bioinformatics research and development, pp 16–27Google Scholar
  4. 4.
    Meyer PE, Marbach D, Roy S, Kellis M (2010) Information-theoretic inference of gene networks using backward elimination. In: BIOCOMP, pp 700–705Google Scholar
  5. 5.
    Altay G, Emmert-Streib F (2010) Inferring the conservative causal core of gene regulatory networks. BMC Syst Biol 4(1):132CrossRefGoogle Scholar
  6. 6.
    Altay G, Emmert-Streib F (2010) Revealing differences in gene network inference algorithms on the network level by ensemble methods. Bioinformatics 26(14): 1738–1744CrossRefGoogle Scholar
  7. 7.
    Marbach D, Costello JC, Küffner R, Vega NM, Prill RJ, Camacho DM, Allison KR, Kellis M, Collins JJ, Stolovitzky G et al (2012) Wisdom of crowds for robust gene network inference. Nat Methods 9(8):796–804CrossRefGoogle Scholar
  8. 8.
    Maetschke SR, Madhamshettiwar PB, Davis MJ, Ragan MA (2013) Supervised, semi-supervised and unsupervised inference of gene regulatory networks. Brief Bioinform p bbt034Google Scholar
  9. 9.
    Bellot P, Olsen C, Salembier P, Oliveras-Vergés A, Meyer PE (2015) Netbenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference. BMC Bioinf 16(1):312CrossRefGoogle Scholar
  10. 10.
    Hase T, Ghosh S, Yamanaka R, Kitano H (2013) Harnessing diversity towards the reconstructing of large scale gene regulatory networks. PLoS Comput Biol 9(11):e1003361CrossRefGoogle Scholar
  11. 11.
    Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, Stolovitzky G (2010) Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci 107(14):6286–6291CrossRefGoogle Scholar
  12. 12.
    Thomas S, Marbach D, Floreano D (2011) GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27(16):2263–2270CrossRefGoogle Scholar
  13. 13.
    Krishnan A, Giuliani A, Tomita M (2007) Indeterminacy of reverse engineering of gene regulatory networks: the curse of gene elasticity. PLoS One 2(6):e562CrossRefGoogle Scholar
  14. 14.
    Emmert-Streib F, Glazko GV, Altay G, Simoes RdM (2012) Statistical inference and reverse engineering of gene regulatory networks from observational expression data. Front Genet 3:8CrossRefGoogle Scholar
  15. 15.
    Marbach D, Roy S, Ay F, Meyer PE, Candeias R, Kahveci T, Bristow CA, Kellis M (2012) Predictive regulatory models in drosophilamelanogaster by integrative inference of transcriptional networks. Genome Res 22(7): 1334–1349CrossRefGoogle Scholar
  16. 16.
    Bellot P, Meyer PE (2014) Efficient combination of pairwise feature networks. In: NCW2014 ECMLGoogle Scholar
  17. 17.
    Capaldi AP, Kaplan T, Liu Y, Habib N, Regev A, Friedman N, O’Shea EK (2008) Structure and function of a transcriptional network activated by the MAPK Hog1. Nat Genet 40(11):1300–1306CrossRefGoogle Scholar
  18. 18.
    De Smet R, Marchal K (2010) Advantages and limitations of current network inference methods. Nat Rev Microbiol 8(10):717–729PubMedPubMedCentralGoogle Scholar
  19. 19.
    Gama-Castro S, Salgado H, Peralta-Gil M (2011) RegulonDB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units). Nucleic Acids Res 39:D98–D105CrossRefGoogle Scholar
  20. 20.
    Salgado H, Martínez-Flores I, Lopez-Fuentes A (2012) Extracting regulatory networks of Escherichia coli from RegulonDB. Methods Mol Biol 804:179–195CrossRefGoogle Scholar
  21. 21.
    Faith J, Driscoll M, Fusaro V (2008) Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata. Nucleic Acids Res 36:D866–D870CrossRefGoogle Scholar
  22. 22.
    Fong S, Joyce A, Palsson B (2005) Parallel adaptive evolution cultures of Escherichia coli lead to convergent growth phenotypes with different gene expression states. Genome Res 15:1365–1372CrossRefGoogle Scholar
  23. 23.
    Sangurdekar D, Srienc F (2006) A classification based framework for quantitative description of large-scale microarray data. Genome Biol 7:R32CrossRefGoogle Scholar
  24. 24.
    Xiao G, Wang X, Khodursky A (2011) Modeling three-dimensional chromosome structures using gene expression data. J Am Stat Assoc 106:61–72CrossRefGoogle Scholar
  25. 25.
    Bellot P (2017) Study of gene regulatory networks inference methods from gene expression data. PhD thesis, Universitat Politècnica de CatalunyaGoogle Scholar
  26. 26.
    Halfon M, Gallo S, Bergman C (2008) REDfly 2.0: an integrated database of cis-regulatory modules and transcription factor binding sites in Drosophila. Nucleic Acids Res 36: D594–D598CrossRefGoogle Scholar
  27. 27.
    Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P (2010) Inferring regulatory networks from expression data using tree-based methods. PloS One 5(9):e12776CrossRefGoogle Scholar
  28. 28.
    Pham NC, Haibe-Kains B, Bellot P, Bontempi G, Meyer PE (2016) Study of meta-analysis strategies for network inference using information-theoretic approaches. In: Biological knowledge discovery and data miningGoogle Scholar
  29. 29.
    Meyer PE, Olsen C, Bontempi G (2011) Transcriptional network inference based on information theory. In: Applied statistics for network biology: methods in systems biology. Wiley-Blackwell, Weinheim, pp 67–89CrossRefGoogle Scholar
  30. 30.
    Ruyssinck J, Demeester P, Dhaene T, Saeys Y (2016) Netter: re-ranking gene network inference predictions using structural network properties. BMC Bioinf 17(1):76CrossRefGoogle Scholar
  31. 31.
    Pržulj N (2007) Biological network comparison using graphlet degree distribution. Bioinformatics 23(2):e177–e183CrossRefGoogle Scholar
  32. 32.
    Hwang CR (1988) Simulated annealing: theory and applications. Acta Appl Math 12: 108–111Google Scholar

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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