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Quantifying the multi-scale performance of network inference algorithms

Author

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  • Oates Chris J.

    (Department of Statistics, Zeeman Building, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UK)

  • Amos Richard

    (The MathWorks, 10 Cowley Park, Cambridge, CB4 0HH, UK)

  • Spencer Simon E.F.

    (Department of Statistics, Zeeman Building, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UK)

Abstract

Graphical models are widely used to study complex multivariate biological systems. Network inference algorithms aim to reverse-engineer such models from noisy experimental data. It is common to assess such algorithms using techniques from classifier analysis. These metrics, based on ability to correctly infer individual edges, possess a number of appealing features including invariance to rank-preserving transformation. However, regulation in biological systems occurs on multiple scales and existing metrics do not take into account the correctness of higher-order network structure. In this paper novel performance scores are presented that share the appealing properties of existing scores, whilst capturing ability to uncover regulation on multiple scales. Theoretical results confirm that performance of a network inference algorithm depends crucially on the scale at which inferences are to be made; in particular strong local performance does not guarantee accurate reconstruction of higher-order topology. Applying these scores to a large corpus of data from the DREAM5 challenge, we undertake a data-driven assessment of estimator performance. We find that the “wisdom of crowds” network, that demonstrated superior local performance in the DREAM5 challenge, is also among the best performing methodologies for inference of regulation on multiple length scales.

Suggested Citation

  • Oates Chris J. & Amos Richard & Spencer Simon E.F., 2014. "Quantifying the multi-scale performance of network inference algorithms," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(5), pages 611-631, October.
    • Handle: RePEc:bpj:sagmbi:v:13:y:2014:i:5:p:21:n:6
    DOI: 10.1515/sagmb-2014-0012
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    References listed on IDEAS

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    Cited by:

    1. Liang Yulan & Kelemen Arpad, 2016. "Bayesian state space models for dynamic genetic network construction across multiple tissues," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 273-290, August.

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