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arXiv · 1210.3149

DTW-MIC Coexpression Networks from Time-Course Data

Samantha Riccadonna·Giuseppe Jurman·Roberto Visintainer·Michele Filosi·Cesare Furlanello
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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 horizontal displacements (DTW). By using the Hamming-Ipsen-Mikhailov (HIM) metric to quantify network differences, the effectiveness of the DTW-MIC approach is demonstrated on both synthetic and transcriptomic datasets.

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Samantha Riccadonna, Giuseppe Jurman, Roberto Visintainer, Michele Filosi, Cesare Furlanello. 2012-10-11. DTW-MIC Coexpression Networks from Time-Course Data. https://arxiv.org/abs/1210.3149

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