A comparative study of Gaussian Graphical Model approaches for genomic data

TL;DR

This paper compares three Gaussian Graphical Model methods for inferring gene networks from high-dimensional genomic data, highlighting their stability, speed, and applicability to biological pathways.

Contribution

It provides a comparative analysis of PINV, RCM, and $ ext{l}_{2C}$ methods, demonstrating their performance differences and practical utility in genomics.

Findings

PINV is less stable with variable number of variables

$ ext{l}_{2C}$ is faster than RCM

$ ext{l}_{2C}$ effectively infers gene networks in Arabidopsis

Abstract

The inference of networks of dependencies by Gaussian Graphical models on high-throughput data is an open issue in modern molecular biology. In this paper we provide a comparative study of three methods to obtain small sample and high dimension estimates of partial correlation coefficients: the Moore-Penrose pseudoinverse (PINV), residual correlation (RCM) and covariance-regularized method $(\ell_{2C})$. We first compare them on simulated datasets and we find that PINV is less stable in terms of AUC performance when the number of variables changes. The two regularized methods have comparable performances but $\ell_{2C}$ is much faster than RCM. Finally, we present the results of an application of $\ell_{2C}$ for the inference of a gene network for isoprenoid biosynthesis pathways in Arabidopsis thaliana.