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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.

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.

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Alignment of time samples based on time lags. Time samples labeled with √ are used for calculating correlations after time lags being determined (here lga = 2, lgb = 3, lgc = 1, lgd = 2).
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Figure 8: Alignment of time samples based on time lags. Time samples labeled with √ are used for calculating correlations after time lags being determined (here lga = 2, lgb = 3, lgc = 1, lgd = 2).

Mentions: In aligning time samples based on the inferred time lags, we assume that the time-series data are not periodic. Recall that r is the maximum order of regulation between the target gene and its regulators. Then effectively we have T − r samples based on which we calculate the correlations between the target gene and its possible regulators. The procedure of aligning time samples is presented in Algorithm 2. Figure 8 is an example showing how to do the alignment of time samples based on the lags between the target gene g and its possible regulators (gene a, b, c or d). The symbol √ inside a slot indicates that the corresponding time sample will be used to calculate the correlation, while the empty slots mean that those samples will not be used to calculate the correlations between this target gene and its potential regulators. When the time-series data are periodic, a similar method of alignment can be used, except that in this case we can use all the T time samples, in which the time points are shifted circularly [6].


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)

Alignment of time samples based on time lags. Time samples labeled with √ are used for calculating correlations after time lags being determined (here lga = 2, lgb = 3, lgc = 1, lgd = 2).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Alignment of time samples based on time lags. Time samples labeled with √ are used for calculating correlations after time lags being determined (here lga = 2, lgb = 3, lgc = 1, lgd = 2).
Mentions: In aligning time samples based on the inferred time lags, we assume that the time-series data are not periodic. Recall that r is the maximum order of regulation between the target gene and its regulators. Then effectively we have T − r samples based on which we calculate the correlations between the target gene and its possible regulators. The procedure of aligning time samples is presented in Algorithm 2. Figure 8 is an example showing how to do the alignment of time samples based on the lags between the target gene g and its possible regulators (gene a, b, c or d). The symbol √ inside a slot indicates that the corresponding time sample will be used to calculate the correlation, while the empty slots mean that those samples will not be used to calculate the correlations between this target gene and its potential regulators. When the time-series data are periodic, a similar method of alignment can be used, except that in this case we can use all the T time samples, in which the time points are shifted circularly [6].

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