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DTW-MIC Coexpression Networks from Time-Course Data

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  • Samantha Riccadonna
  • Giuseppe Jurman
  • Roberto Visintainer
  • Michele Filosi
  • Cesare Furlanello

Abstract

When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions. However, its reliability is limited since it cannot capture non-linear interactions and time shifts. Here we propose to overcome these two issues by employing a novel similarity function, Dynamic Time Warping Maximal Information Coefficient (DTW-MIC), combining a measure taking care of functional interactions of signals (MIC) and a measure identifying time lag (DTW). By using the Hamming-Ipsen-Mikhailov (HIM) metric to quantify network differences, the effectiveness of the DTW-MIC approach is demonstrated on a set of four synthetic and one transcriptomic datasets, also in comparison to TimeDelay ARACNE and Transfer Entropy.

Suggested Citation

  • Samantha Riccadonna & Giuseppe Jurman & Roberto Visintainer & Michele Filosi & Cesare Furlanello, 2016. "DTW-MIC Coexpression Networks from Time-Course Data," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-29, March.
    • Handle: RePEc:plo:pone00:0152648
    DOI: 10.1371/journal.pone.0152648
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    References listed on IDEAS

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    1. Emma Pierson & the GTEx Consortium & Daphne Koller & Alexis Battle & Sara Mostafavi, 2015. "Sharing and Specificity of Co-expression Networks across 35 Human Tissues," PLOS Computational Biology, Public Library of Science, vol. 11(5), pages 1-19, May.
    2. Jeffrey D Allen & Yang Xie & Min Chen & Luc Girard & Guanghua Xiao, 2012. "Comparing Statistical Methods for Constructing Large Scale Gene Networks," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-9, January.
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