DTW-MIC Coexpression Networks from Time-Course Data

Samantha Riccadonna, Giuseppe Jurman, Roberto Visintainer, Michele Filosi and Cesare Furlanello

PLOS ONE, 2016, vol. 11, issue 3, 1-29

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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0152648

DOI: 10.1371/journal.pone.0152648

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