Inferring gene expression networks with hubs using a degree weighted Lasso approach

TL;DR

This paper introduces DW-Lasso, a novel two-stage degree weighted Lasso method designed to efficiently infer gene networks with hub genes from high-dimensional data, outperforming traditional methods in simulations and real datasets.

Contribution

The paper proposes a new two-stage degree weighted Lasso approach that effectively captures hub genes in gene network inference from high-dimensional data.

Findings

DW-Lasso outperforms traditional Lasso in simulations

Accurately identifies hub genes in real datasets

Demonstrates robustness with small sample sizes

Abstract

Genome-scale gene networks contain regulatory genes called hubs that have many interaction partners. These genes usually play an essential role in gene regulation and cellular processes. Despite recent advancements in high-throughput technology, inferring gene networks with hub genes from high-dimensional data still remains a challenging problem. Novel statistical network inference methods are needed for efficient and accurate reconstruction of hub networks from high-dimensional data. To address this challenge we propose DW-Lasso, a degree weighted Lasso (least absolute shrinkage and selection operator) method which infers gene networks with hubs efficiently under the low sample size setting. Our network reconstruction approach is formulated as a two stage procedure: first, the degree of networks is estimated iteratively, and second, the gene regulatory network is reconstructed using degree information. A useful property of the proposed method is that it naturally favors the accumulation of neighbors around hub genes and thereby helps in accurate modeling of the high-throughput data under the assumption that the underlying network exhibits hub structure. In a simulation study, we demonstrate good predictive performance of the proposed method in comparison to traditional Lasso type methods in inferring hub and scale-free graphs. We show the effectiveness of our method in an application to microarray data of \textit{E.coli} and RNA sequencing data of Kidney Clear Cell Carcinoma from The Cancer Genome Atlas datasets.