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.

Riccadonna, S.; Jurman, G.; Visintainer, R.; Filosi, M.; Furlanello, C. (2016). DTW-MIC coexpression networks from time-course data. PLOS ONE, 11 (3): e0152648. doi: 10.1371/journal.pone.0152648 handle: http://hdl.handle.net/10449/33074

DTW-MIC coexpression networks from time-course data

Riccadonna, Samantha;
2016-01-01

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.
Time course
Network inference
Settore BIO/11 - BIOLOGIA MOLECOLARE
2016
Riccadonna, S.; Jurman, G.; Visintainer, R.; Filosi, M.; Furlanello, C. (2016). DTW-MIC coexpression networks from time-course data. PLOS ONE, 11 (3): e0152648. doi: 10.1371/journal.pone.0152648 handle: http://hdl.handle.net/10449/33074
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10449/33074
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