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Inferring dynamic gene regulatory networks with low-order conditional independencies – an evaluation of the method

Author

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  • Ajmal Hamda B.
  • Madden Michael G.

    (School of Computer Science, National University of Ireland, Galway, Ireland)

Abstract

Over a decade ago, Lèbre (2009) proposed an inference method, G1DBN, to learn the structure of gene regulatory networks (GRNs) from high dimensional, sparse time-series gene expression data. Their approach is based on concept of low-order conditional independence graphs that they extend to dynamic Bayesian networks (DBNs). They present results to demonstrate that their method yields better structural accuracy compared to the related Lasso and Shrinkage methods, particularly where the data is sparse, that is, the number of time measurements n is much smaller than the number of genes p. This paper challenges these claims using a careful experimental analysis, to show that the GRNs reverse engineered from time-series data using the G1DBN approach are less accurate than claimed by Lèbre (2009). We also show that the Lasso method yields higher structural accuracy for graphs learned from the simulated data, compared to the G1DBN method, particularly when the data is sparse (n

Suggested Citation

  • Ajmal Hamda B. & Madden Michael G., 2020. "Inferring dynamic gene regulatory networks with low-order conditional independencies – an evaluation of the method," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(4-6), pages 1-19, December.
    • Handle: RePEc:bpj:sagmbi:v:19:y:2020:i:4-6:p:19:n:3
    DOI: 10.1515/sagmb-2020-0051
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    References listed on IDEAS

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    1. Manfred Koranda & Alexander Schleiffer & Lukas Endler & Gustav Ammerer, 2000. "Forkhead-like transcription factors recruit Ndd1 to the chromatin of G2/M-specific promoters," Nature, Nature, vol. 406(6791), pages 94-98, July.
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    3. Sisi Ma & Patrick Kemmeren & David Gresham & Alexander Statnikov, 2014. "De-Novo Learning of Genome-Scale Regulatory Networks in S. cerevisiae," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-20, September.
    4. Wille Anja & Bühlmann Peter, 2006. "Low-Order Conditional Independence Graphs for Inferring Genetic Networks," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-34, January.
    5. Grzegorczyk Marco & Husmeier Dirk, 2012. "A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-62, July.
    6. Jamshid Pirgazi & Ali Reza Khanteymoori, 2018. "A robust gene regulatory network inference method base on Kalman filter and linear regression," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-17, July.
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