Regularized estimation of large-scale gene association networks using graphical Gaussian models

TL;DR

This paper develops and compares regularized regression methods, including new approaches, for estimating gene association networks using graphical Gaussian models in high-dimensional microarray data.

Contribution

It introduces a flexible framework combining regularized regression with graphical Gaussian models, including two novel methods based on ridge regression and adaptive lasso.

Findings

New methods outperform standard approaches in simulations

The methods effectively handle high-dimensional data

All algorithms are implemented in the R package 'parcor'

Abstract

Graphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. A key issue when the number of variables greatly exceeds the number of samples is the estimation of the matrix of partial correlations. Since the (Moore-Penrose) inverse of the sample covariance matrix leads to poor estimates in this scenario, standard methods are inappropriate and adequate regularization techniques are needed. In this article, we investigate a general framework for combining regularized regression methods with the estimation of Graphical Gaussian models. This framework includes various existing methods as well as two new approaches based on ridge regression and adaptive lasso, respectively. These methods are extensively compared both qualitatively and quantitatively within a simulation study and through an application to six diverse real data sets. In addition, all proposed algorithms are implemented in the R package "parcor", available from the R repository CRAN.