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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: comparison networks.The Human t-cell network as reconstructed by PCC (top row), TimeDelay ARACNE (middle row) and Transfer Entropy (bottom row), from the tcell.10 (left column) and from the tcell.34 (right column) dataset. Edges with weights smaller than 0.1 for PCC and smaller than 0.0001 for Trasfer Entropy are not displayed.
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pone.0152648.g010: The T-cell example: comparison networks.The Human t-cell network as reconstructed by PCC (top row), TimeDelay ARACNE (middle row) and Transfer Entropy (bottom row), from the tcell.10 (left column) and from the tcell.34 (right column) dataset. Edges with weights smaller than 0.1 for PCC and smaller than 0.0001 for Trasfer Entropy are not displayed.

Mentions: Eight instances of the T-cell network are inferred, by the three similarity measures DTW-MIC, PCC and Transfer Entropy and the reconstruction algorithm TimeDelay ARACNE, starting from the two datasets tcell.34 and tcell.10. In both datasets, the dimension of the longitudinal data for each replicate (10 time points) cannot guarantee robustness in the inference process, since both PCC and MIC are not reliable for datasets of too small sample size [49, 68, 142]. Hence all replicates in the two datasets are consecutively joined so that time point 72h of replicate i is followed by time point 0h for replicate i + 1, thus yielding for each gene a single time course on 340 time points for tcell.34 and on 100 time points for tcell.10. The inferred networks are displayed in Figs 9 and 10, while in Table 3 the H/IM/HIM distances are reported between the true and the inferred T-cell networks.


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: comparison networks.The Human t-cell network as reconstructed by PCC (top row), TimeDelay ARACNE (middle row) and Transfer Entropy (bottom row), from the tcell.10 (left column) and from the tcell.34 (right column) dataset. Edges with weights smaller than 0.1 for PCC and smaller than 0.0001 for Trasfer Entropy are not displayed.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0152648.g010: The T-cell example: comparison networks.The Human t-cell network as reconstructed by PCC (top row), TimeDelay ARACNE (middle row) and Transfer Entropy (bottom row), from the tcell.10 (left column) and from the tcell.34 (right column) dataset. Edges with weights smaller than 0.1 for PCC and smaller than 0.0001 for Trasfer Entropy are not displayed.
Mentions: Eight instances of the T-cell network are inferred, by the three similarity measures DTW-MIC, PCC and Transfer Entropy and the reconstruction algorithm TimeDelay ARACNE, starting from the two datasets tcell.34 and tcell.10. In both datasets, the dimension of the longitudinal data for each replicate (10 time points) cannot guarantee robustness in the inference process, since both PCC and MIC are not reliable for datasets of too small sample size [49, 68, 142]. Hence all replicates in the two datasets are consecutively joined so that time point 72h of replicate i is followed by time point 0h for replicate i + 1, thus yielding for each gene a single time course on 340 time points for tcell.34 and on 100 time points for tcell.10. The inferred networks are displayed in Figs 9 and 10, while in Table 3 the H/IM/HIM distances are reported between the true and the inferred T-cell networks.

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