A Statistical Test for Differential Network Analysis Based on Inference of Gaussian Graphical Model.

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

SUBMITTER: He H 

PROVIDER: S-EPMC6659630 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature