GeneNetTools: tests for Gaussian graphical models with shrinkage.

ABSTRACT:

Motivation

Gaussian graphical models (GGMs) are network representations of random variables (as nodes) and their partial correlations (as edges). GGMs overcome the challenges of high-dimensional data analysis by using shrinkage methodologies. Therefore, they have become useful to reconstruct gene regulatory networks from gene-expression profiles. However, it is often ignored that the partial correlations are 'shrunk' and that they cannot be compared/assessed directly. Therefore, accurate (differential) network analyses need to account for the number of variables, the sample size, and also the shrinkage value, otherwise, the analysis and its biological interpretation would turn biased. To date, there are no appropriate methods to account for these factors and address these issues.

SUBMITTER: Bernal V 

PROVIDER: S-EPMC9665865 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature