Learning Gene Regulatory Networks with High-Dimensional Heterogeneous Data

TL;DR

This paper introduces a mixture Gaussian graphical model with an imputation-consistency algorithm to effectively learn gene regulatory networks from high-dimensional heterogeneous gene expression data, outperforming existing methods.

Contribution

It develops a novel approach combining mixture Gaussian graphical models with an imputation-consistency algorithm for clustering and network estimation in heterogeneous data.

Findings

Superior parameter estimation accuracy

Effective sample clustering into subgroups

Enhanced network construction performance

Abstract

The Gaussian graphical model is a widely used tool for learning gene regulatory networks with high-dimensional gene expression data. Most existing methods for Gaussian graphical models assume that the data are homogeneous, i.e., all samples are drawn from a single Gaussian distribution. However, for many real problems, the data are heterogeneous, which may contain some subgroups or come from different resources. This paper proposes to model the heterogeneous data using a mixture Gaussian graphical model, and apply the imputation-consistency algorithm, combining with the $\psi$-learning algorithm, to estimate the parameters of the mixture model and cluster the samples to different subgroups. An integrated Gaussian graphical network is learned across the subgroups along with the iterations of the imputation-consistency algorithm. The proposed method is compared with an existing method for learning mixture Gaussian graphical models as well as a few other methods developed for homogeneous data, such as graphical Lasso, nodewise regression and $\psi$-learning. The numerical results indicate superiority of the proposed method in all aspects of parameter estimation, cluster identification and network construction. The numerical results also indicate generality of the proposed method: it can be applied to homogeneous data without significant harms.