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Gene regulatory network inference from sparsely sampled noisy data

Author

Listed:
  • Atte Aalto

    (University of Luxembourg; 6 avenue du Swing)

  • Lauri Viitasaari

    (University of Helsinki; P.O. Box 68, Gustaf Hällströmin katu 2b)

  • Pauliina Ilmonen

    (Aalto University School of Science; P.O. Box 11100)

  • Laurent Mombaerts

    (University of Luxembourg; 6 avenue du Swing)

  • Jorge Gonçalves

    (University of Luxembourg; 6 avenue du Swing
    University of Cambridge; Downing Street)

Abstract

The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. The major obstacle in inferring gene regulatory networks is the lack of data. While time series data are nowadays widely available, they are typically noisy, with low sampling frequency and overall small number of samples. This paper develops a method called BINGO to specifically deal with these issues. Benchmarked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The novelty of BINGO lies in a nonparametric approach featuring statistical sampling of continuous gene expression profiles. BINGO’s superior performance and ease of use, even by non-specialists, make gene regulatory network inference available to any researcher, helping to decipher the complex mechanisms of life.

Suggested Citation

  • Atte Aalto & Lauri Viitasaari & Pauliina Ilmonen & Laurent Mombaerts & Jorge Gonçalves, 2020. "Gene regulatory network inference from sparsely sampled noisy data," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17217-1
    DOI: 10.1038/s41467-020-17217-1
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    References listed on IDEAS

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    1. Andrea Pinna & Nicola Soranzo & Alberto de la Fuente, 2010. "From Knockouts to Networks: Establishing Direct Cause-Effect Relationships through Graph Analysis," PLOS ONE, Public Library of Science, vol. 5(10), pages 1-8, October.
    2. Penfold Christopher A. & Brown Paul E. & Wild David L. & Shifaz Ahmed & Nicholson Ann, 2015. "CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(3), pages 307-310, June.
    3. Balazs Gyorffy & Bela Molnar & Hermann Lage & Zoltan Szallasi & Aron C Eklund, 2009. "Evaluation of Microarray Preprocessing Algorithms Based on Concordance with RT-PCR in Clinical Samples," PLOS ONE, Public Library of Science, vol. 4(5), pages 1-6, May.
    4. Jose Casadiego & Mor Nitzan & Sarah Hallerberg & Marc Timme, 2017. "Model-free inference of direct network interactions from nonlinear collective dynamics," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    5. Fei Liu & Shao-Wu Zhang & Wei-Feng Guo & Ze-Gang Wei & Luonan Chen, 2016. "Inference of Gene Regulatory Network Based on Local Bayesian Networks," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-17, August.
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    Cited by:

    1. Aqib Hasnain & Shara Balakrishnan & Dennis M. Joshy & Jen Smith & Steven B. Haase & Enoch Yeung, 2023. "Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

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