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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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GeneNetWeaver data.box and whisker plot of the HIM distance between the networks inferred from time series and the true graphs, listed in Table 1. For each true network Yeast20, Ecoli20 and Ecoli50, 10 different graphs are reconstructed by PCC, DTW-MIC, TimeDelay ARACNE and Transfer Entropy similarity measures.
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pone.0152648.g008: GeneNetWeaver data.box and whisker plot of the HIM distance between the networks inferred from time series and the true graphs, listed in Table 1. For each true network Yeast20, Ecoli20 and Ecoli50, 10 different graphs are reconstructed by PCC, DTW-MIC, TimeDelay ARACNE and Transfer Entropy similarity measures.

Mentions: The results are reported in Table 1 and summarized in the box and whisker plots of Fig 8. The networks inferred by DTW-MIC are consistently closer to the true network than the graphs created with other inference methods, apart from Ecoli50 with TimeDelay ARACNE, with also smaller standard deviation over the 10 experiments in almost all cases.


DTW-MIC Coexpression Networks from Time-Course Data.

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

GeneNetWeaver data.box and whisker plot of the HIM distance between the networks inferred from time series and the true graphs, listed in Table 1. For each true network Yeast20, Ecoli20 and Ecoli50, 10 different graphs are reconstructed by PCC, DTW-MIC, TimeDelay ARACNE and Transfer Entropy similarity measures.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0152648.g008: GeneNetWeaver data.box and whisker plot of the HIM distance between the networks inferred from time series and the true graphs, listed in Table 1. For each true network Yeast20, Ecoli20 and Ecoli50, 10 different graphs are reconstructed by PCC, DTW-MIC, TimeDelay ARACNE and Transfer Entropy similarity measures.
Mentions: The results are reported in Table 1 and summarized in the box and whisker plots of Fig 8. The networks inferred by DTW-MIC are consistently closer to the true network than the graphs created with other inference methods, apart from Ecoli50 with TimeDelay ARACNE, with also smaller standard deviation over the 10 experiments in almost all cases.

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