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Highly sensitive inference of time-delayed gene regulation by network deconvolution.

Chen H, Mundra PA, Zhao LN, Lin F, Zheng J - BMC Syst Biol (2014)

Bottom Line: Experiments on real-life gene expression datasets show that our method achieves overall better performance than existing methods for inferring time-delayed GRNs.The effectiveness of our method has been shown by the experiments on three real-life gene expression datasets of yeast.Compared with other existing methods which were designed for learning time-delayed GRN, our method has significantly higher sensitivity without much reduction of specificity.

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ABSTRACT

Background: Gene regulatory network (GRN) is a fundamental topic in systems biology. The dynamics of GRN can shed light on the cellular processes, which facilitates the understanding of the mechanisms of diseases when the processes are dysregulated. Accurate reconstruction of GRN could also provide guidelines for experimental biologists. Therefore, inferring gene regulatory networks from high-throughput gene expression data is a central problem in systems biology. However, due to the inherent complexity of gene regulation, noise in measuring the data and the short length of time-series data, it is very challenging to reconstruct accurate GRNs. On the other hand, a better understanding into gene regulation could help to improve the performance of GRN inference. Time delay is one of the most important characteristics of gene regulation. By incorporating the information of time delays, we can achieve more accurate inference of GRN.

Results: In this paper, we propose a method to infer time-delayed gene regulation based on cross-correlation and network deconvolution (ND). First, we employ cross-correlation to obtain the probable time delays for the interactions between each target gene and its potential regulators. Then based on the inferred delays, the technique of ND is applied to identify direct interactions between the target gene and its regulators. Experiments on real-life gene expression datasets show that our method achieves overall better performance than existing methods for inferring time-delayed GRNs.

Conclusion: By taking into account the time delays among gene interactions, our method is able to infer GRN more accurately. The effectiveness of our method has been shown by the experiments on three real-life gene expression datasets of yeast. Compared with other existing methods which were designed for learning time-delayed GRN, our method has significantly higher sensitivity without much reduction of specificity.

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The flow chart of time-delayed ND.
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Figure 7: The flow chart of time-delayed ND.

Mentions: We incorporate time delays into network deconvolution to enhance its strength in GRN inference. The flow chart of time-delayed ND is shown in Figure 7. Starting with the time-series gene expression data, for each target gene we perform the following steps. We first identify the time lags of regulation based on cross-correlation. Then we align the time samples based on the inferred time lags. After that, we calculate the correlations for each gene pairs based on the aligned samples, and apply ND on the correlation matrix. Finally, we obtain a GRN with time-delayed regulation.


Highly sensitive inference of time-delayed gene regulation by network deconvolution.

Chen H, Mundra PA, Zhao LN, Lin F, Zheng J - BMC Syst Biol (2014)

The flow chart of time-delayed ND.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4290726&req=5

Figure 7: The flow chart of time-delayed ND.
Mentions: We incorporate time delays into network deconvolution to enhance its strength in GRN inference. The flow chart of time-delayed ND is shown in Figure 7. Starting with the time-series gene expression data, for each target gene we perform the following steps. We first identify the time lags of regulation based on cross-correlation. Then we align the time samples based on the inferred time lags. After that, we calculate the correlations for each gene pairs based on the aligned samples, and apply ND on the correlation matrix. Finally, we obtain a GRN with time-delayed regulation.

Bottom Line: Experiments on real-life gene expression datasets show that our method achieves overall better performance than existing methods for inferring time-delayed GRNs.The effectiveness of our method has been shown by the experiments on three real-life gene expression datasets of yeast.Compared with other existing methods which were designed for learning time-delayed GRN, our method has significantly higher sensitivity without much reduction of specificity.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Gene regulatory network (GRN) is a fundamental topic in systems biology. The dynamics of GRN can shed light on the cellular processes, which facilitates the understanding of the mechanisms of diseases when the processes are dysregulated. Accurate reconstruction of GRN could also provide guidelines for experimental biologists. Therefore, inferring gene regulatory networks from high-throughput gene expression data is a central problem in systems biology. However, due to the inherent complexity of gene regulation, noise in measuring the data and the short length of time-series data, it is very challenging to reconstruct accurate GRNs. On the other hand, a better understanding into gene regulation could help to improve the performance of GRN inference. Time delay is one of the most important characteristics of gene regulation. By incorporating the information of time delays, we can achieve more accurate inference of GRN.

Results: In this paper, we propose a method to infer time-delayed gene regulation based on cross-correlation and network deconvolution (ND). First, we employ cross-correlation to obtain the probable time delays for the interactions between each target gene and its potential regulators. Then based on the inferred delays, the technique of ND is applied to identify direct interactions between the target gene and its regulators. Experiments on real-life gene expression datasets show that our method achieves overall better performance than existing methods for inferring time-delayed GRNs.

Conclusion: By taking into account the time delays among gene interactions, our method is able to infer GRN more accurately. The effectiveness of our method has been shown by the experiments on three real-life gene expression datasets of yeast. Compared with other existing methods which were designed for learning time-delayed GRN, our method has significantly higher sensitivity without much reduction of specificity.

Show MeSH