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. 2017 Feb 23;16:1176935117690778.
doi: 10.1177/1176935117690778. eCollection 2017.

Integrative Analysis of Gene Networks and Their Application to Lung Adenocarcinoma Studies

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Free PMC article

Integrative Analysis of Gene Networks and Their Application to Lung Adenocarcinoma Studies

Sangin Lee et al. Cancer Inform. .
Free PMC article

Abstract

The construction of gene regulatory networks (GRNs) is an essential component of biomedical research to determine disease mechanisms and identify treatment targets. Gaussian graphical models (GGMs) have been widely used for constructing GRNs by inferring conditional dependence among a set of gene expressions. In practice, GRNs obtained by the analysis of a single data set may not be reliable due to sample limitations. Therefore, it is important to integrate multiple data sets from comparable studies to improve the construction of a GRN. In this article, we introduce an equivalent measure of partial correlation coefficients in GGMs and then extend the method to construct a GRN by combining the equivalent measures from different sources. Furthermore, we develop a method for multiple data sets with a natural missing mechanism to accommodate the differences among different platforms in multiple sources of data. Simulation results show that this integrative analysis outperforms the standard methods and can detect hub genes in the true network. The proposed integrative method was applied to 12 lung adenocarcinoma data sets collected from different studies. The constructed network is consistent with the current biological knowledge and reveals new insights about lung adenocarcinoma.

Keywords: Gaussian graphical model; gene regulatory network; integrative analysis; multiple hypothesis test; partial correlation coefficient.

Conflict of interest statement

DECLARATION OF CONFLICTING INTERESTS: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
The topology of 2 networks with different sizes used in the simulation. The large nodes with red color represent the hub gene node whose node degrees are greater than upper 95% quantile for each network: (A) small size network and (B) large size network.
Figure 2.
Figure 2.
ROC curve and partial ROC curve under FPR<0.05 for all methods where the sample and network sizes are n=100 and p=83.FPR indicates false-positive rate; JGL, joint graphical lasso; ROC, receiver operating characteristic curve.
Figure 3.
Figure 3.
ROC curve and partial ROC curve under FPR<0.05 for all methods where the sample and network sizes are n=100 and p=612, respectively. FPR indicates false-positive rate; JGL, joint graphical lasso; ROC, receiver operating characteristic curve.
Figure 4.
Figure 4.
Path of networks constructed by various q values in the simulation on large size network: (A) true, (B) q value = 0.000001, (C) q value = 0.001, (D) q value = 0.01, (E) q value = 0.1, and (F) q value = 0.3, where the large nodes with red color represent the hub gene node whose node degrees are greater than 9.
Figure 5.
Figure 5.
Proportion of each true hub gene being detected by the proposed method in the simulation on large size network: (upper panel) q value = 0.1; (bottom panel) q value = 0.3.
Figure 6.
Figure 6.
Networks constructed by the integrative ψ-learning method: (A) q value = 0.0001, τ=7 and (B) q value = 0.001, τ=9. The large nodes with red color represent the hub genes whose node degrees are greater than 95% quantile τ of node degrees for each network.

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