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.