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DTW-MIC Coexpression Networks from Time-Course Data.

Riccadonna S, Jurman G, Visintainer R, Filosi M, Furlanello C - PLoS ONE (2016)

Bottom Line: When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions.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.

View Article: PubMed Central - PubMed

Affiliation: Fondazione Bruno Kessler, Trento, Italy.

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.

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Metric multidimensional scaling of HIM distances.Planar projection conserving the mutual distances between the true Human t-cell network (TN) and the eight networks inferred from the two datasets tcell.34 (⋅34) and tcell.10 (⋅10) by the four reconstruction algorithms DTW-MIC (D), PCC (P), Transfer Entropy (T) and TimeDelay ARACNE (A).
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pone.0152648.g011: Metric multidimensional scaling of HIM distances.Planar projection conserving the mutual distances between the true Human t-cell network (TN) and the eight networks inferred from the two datasets tcell.34 (⋅34) and tcell.10 (⋅10) by the four reconstruction algorithms DTW-MIC (D), PCC (P), Transfer Entropy (T) and TimeDelay ARACNE (A).

Mentions: In Fig 11 we show the plot of the metric multidimensional scaling of all mutual HIM distances.


DTW-MIC Coexpression Networks from Time-Course Data.

Riccadonna S, Jurman G, Visintainer R, Filosi M, Furlanello C - PLoS ONE (2016)

Metric multidimensional scaling of HIM distances.Planar projection conserving the mutual distances between the true Human t-cell network (TN) and the eight networks inferred from the two datasets tcell.34 (⋅34) and tcell.10 (⋅10) by the four reconstruction algorithms DTW-MIC (D), PCC (P), Transfer Entropy (T) and TimeDelay ARACNE (A).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4816347&req=5

pone.0152648.g011: Metric multidimensional scaling of HIM distances.Planar projection conserving the mutual distances between the true Human t-cell network (TN) and the eight networks inferred from the two datasets tcell.34 (⋅34) and tcell.10 (⋅10) by the four reconstruction algorithms DTW-MIC (D), PCC (P), Transfer Entropy (T) and TimeDelay ARACNE (A).
Mentions: In Fig 11 we show the plot of the metric multidimensional scaling of all mutual HIM distances.

Bottom Line: When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions.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.

View Article: PubMed Central - PubMed

Affiliation: Fondazione Bruno Kessler, Trento, Italy.

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.

Show MeSH