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