Exact hypothesis testing for shrinkage-based Gaussian graphical models.

ABSTRACT:

Motivation

One of the main goals in systems biology is to learn molecular regulatory networks from quantitative profile data. In particular, Gaussian graphical models (GGMs) are widely used network models in bioinformatics where variables (e.g. transcripts, metabolites or proteins) are represented by nodes, and pairs of nodes are connected with an edge according to their partial correlation. Reconstructing a GGM from data is a challenging task when the sample size is smaller than the number of variables. The main problem consists in finding the inverse of the covariance estimator which is ill-conditioned in this case. Shrinkage-based covariance estimators are a popular approach, producing an invertible 'shrunk' covariance. However, a proper significance test for the 'shrunk' partia

SUBMITTER: Bernal V 

PROVIDER: S-EPMC6901079 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature