Uncertainty quantification in high-dimensional linear models incorporating graphical structures with applications to gene set analysis.

ABSTRACT:

Motivation

The functions of genes in networks are typically correlated due to their functional connectivity. Variable selection methods have been developed to select important genes associated with a trait while incorporating network graphical information. However, no method has been proposed to quantify the uncertainty of individual genes under such settings.

Results

In this paper, we construct confidence intervals (CIs) and provide P-values for parameters of a high-dimensional linear model incorporating graphical structures where the number of variables p diverges with the number of observations. For combining the graphical information, we propose a graph-constrained desparsified LASSO (least absolute shrinkage and selection operator) (GCDL) estimator, which reduces dramat

SUBMITTER: Tan X 

PROVIDER: S-EPMC11434165 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature