A two-stage approach of gene network analysis for high-dimensional heterogeneous data

Gaussian graphical models have been widely used to construct gene regulatory networks from gene expression data. Most existing methods for Gaussian graphical models are designed to model homogeneous data, assuming a single Gaussian distribution. In practice, however, data may consist of gene expression studies with unknown confounding factors, such as study cohort, microarray platforms, experimental batches, which produce heterogeneous data, and hence lead to false positive edges or low detection power in resulting network, due to those unknown factors. To overcome this problem and improve the performance in constructing gene networks, we propose a two-stage approach to construct a gene network from heterogeneous data. The first stage is to perform a clustering analysis in order to assign samples to a few clusters where the samples in each cluster are approximately homogeneous, and the second stage is to conduct an integrative analysis of networks from each cluster. In particular, we first apply a model-based clustering method using the singular value decomposition for high-dimensional data, and then integrate the networks from each cluster using the integrative $\psi$-learning method. The proposed method is based on an equivalent measure of partial correlation coefficients in Gaussian graphical models, which is computed with a reduced conditional set and thus it is useful for high-dimensional data. We compare the proposed two-stage learning approach with some existing methods in various simulation settings, and demonstrate the robustness of the proposed method. Finally, it is applied to integrate multiple gene expression studies of lung adenocarcinoma to identify potential therapeutic targets and treatment biomarkers.