Abstract
Differential network analysis investigates how the network of connected genes changes from one condition to another and has become a prevalent tool to provide a deeper and more comprehensive understanding of the molecular etiology of complex diseases. Based on the asymptotically normal estimation of large Gaussian graphical model (GGM) in the high-dimensional setting, we developed a computationally efficient test for differential network analysis through testing the equality of two precision matrices, which summarize the conditional dependence network structures of the genes. Additionally, we applied a multiple testing procedure to infer the differential network structure with false discovery rate (FDR) control. Through extensive simulation studies with different combinations of parameters including sample size, number of vertices, level of heterogeneity and graph structure, we demonstrated that our method performed much better than the current available methods in terms of accuracy and computational time. In real data analysis on lung adenocarcinoma, we revealed a differential network with 3503 nodes and 2550 edges, which consisted of 50 clusters with an FDR threshold at 0.05. Many of the top gene pairs in the differential network have been reported relevant to human cancers. Our method represents a powerful tool of network analysis for high-dimensional biological data.
Highlights
Differential network analysis investigates how the network of connected genes changes from one condition to another and has become a prevalent tool to provide a deeper and more comprehensive understanding of the molecular etiology of complex diseases
We first chose optimal tuning parameters by Bayesian information criterion (BIC) and calculated the averaged Area Under the Curve (AUC) for the Receiver Operating Characteristic (ROC) curves based on a sequence of tuning parameters
Motivated by an important biological question that how the network structure of cellular interactome change from one condition to another, we derived a formal statistical test for the differential network analysis based on the inference of Gaussian graphical model (GGM)
Summary
Differential network analysis investigates how the network of connected genes changes from one condition to another and has become a prevalent tool to provide a deeper and more comprehensive understanding of the molecular etiology of complex diseases. Based on the asymptotically normal estimation of large Gaussian graphical model (GGM) in the high-dimensional setting, we developed a computationally efficient test for differential network analysis through testing the equality of two precision matrices, which summarize the conditional dependence network structures of the genes. A fast algorithm, named “FastGGM9”, as an exact implementation to the asymptotically normal and efficient estimation established by Ren et al.[8], showed that the inference of partial correlation between genes becomes computationally feasible for whole-genome data sets[9] All of these methods addressed the problem of estimating and constructing a single Gaussian graphical model
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