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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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The T-cell example: True and DTW-MIC network.The (true) network as reconstructed by Opgen-Rhein and Strimmer [128] (top left); the time course for three example genes EGR1 (blue), CD69 (red) and SCYA2 (orange), from replicate 1 of the tcell.34 (circles) and of the tcell.10 (squares) dataset. In the second row, the networks inferred by DTW-MIC from the tcell.10 (left) and from the tcell.34 (right) dataset; in these last two graphs, edges with weight smaller than 0.225 are not displayed.
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pone.0152648.g009: The T-cell example: True and DTW-MIC network.The (true) network as reconstructed by Opgen-Rhein and Strimmer [128] (top left); the time course for three example genes EGR1 (blue), CD69 (red) and SCYA2 (orange), from replicate 1 of the tcell.34 (circles) and of the tcell.10 (squares) dataset. In the second row, the networks inferred by DTW-MIC from the tcell.10 (left) and from the tcell.34 (right) dataset; in these last two graphs, edges with weight smaller than 0.225 are not displayed.

Mentions: Rangel and colleagues in [62] investigated the dynamics of the activation of T-lymphocites by analysing the response of the human Jurkat T-cell line subjected to a treatment with phorbol 12-myristate 13-acetate (PMA) and ionomycin. Operatively, they measured the expression of 58 genes across 10 time points (0, 2, 4, 6, 8, 18, 24, 32, 48, and 72 hours after treatment) with two series of respectively 34 and 10 replicates on a custom microarray built by spotting PCR products on amino-modified glass slides using a Microgrid II spotter. The preprocessed array data tcell.34 and tcell.10, log-transformed and quantile normalized, are publicly available in the R package longitudinal. This package was developed by Opgen-Rhein and Strimmer who inferred the corresponding network by shrinkage estimation of the (partial) dynamical correlation [128, 141]. Their result is considered here as the true network, displayed in the top left panel of Fig 9. As an example of the data in the tcell.34 and tcell.10, in the top right panel of the same Fig 9 we show the time course data for the three genes EGR1, CD69 and SCYA2 in the first out of 34 replicates of tcell.34 and in the first out of 10 replicates of tcell.10.


DTW-MIC Coexpression Networks from Time-Course Data.

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

The T-cell example: True and DTW-MIC network.The (true) network as reconstructed by Opgen-Rhein and Strimmer [128] (top left); the time course for three example genes EGR1 (blue), CD69 (red) and SCYA2 (orange), from replicate 1 of the tcell.34 (circles) and of the tcell.10 (squares) dataset. In the second row, the networks inferred by DTW-MIC from the tcell.10 (left) and from the tcell.34 (right) dataset; in these last two graphs, edges with weight smaller than 0.225 are not displayed.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0152648.g009: The T-cell example: True and DTW-MIC network.The (true) network as reconstructed by Opgen-Rhein and Strimmer [128] (top left); the time course for three example genes EGR1 (blue), CD69 (red) and SCYA2 (orange), from replicate 1 of the tcell.34 (circles) and of the tcell.10 (squares) dataset. In the second row, the networks inferred by DTW-MIC from the tcell.10 (left) and from the tcell.34 (right) dataset; in these last two graphs, edges with weight smaller than 0.225 are not displayed.
Mentions: Rangel and colleagues in [62] investigated the dynamics of the activation of T-lymphocites by analysing the response of the human Jurkat T-cell line subjected to a treatment with phorbol 12-myristate 13-acetate (PMA) and ionomycin. Operatively, they measured the expression of 58 genes across 10 time points (0, 2, 4, 6, 8, 18, 24, 32, 48, and 72 hours after treatment) with two series of respectively 34 and 10 replicates on a custom microarray built by spotting PCR products on amino-modified glass slides using a Microgrid II spotter. The preprocessed array data tcell.34 and tcell.10, log-transformed and quantile normalized, are publicly available in the R package longitudinal. This package was developed by Opgen-Rhein and Strimmer who inferred the corresponding network by shrinkage estimation of the (partial) dynamical correlation [128, 141]. Their result is considered here as the true network, displayed in the top left panel of Fig 9. As an example of the data in the tcell.34 and tcell.10, in the top right panel of the same Fig 9 we show the time course data for the three genes EGR1, CD69 and SCYA2 in the first out of 34 replicates of tcell.34 and in the first out of 10 replicates of tcell.10.

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