Time-varying differential network analysis for revealing network rewiring over cancer progression

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

3 Scopus Citations
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Detail(s)

Original languageEnglish
Article number8880525
Pages (from-to)1632-1642
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume18
Issue number4
Online published23 Oct 2019
Publication statusPublished - Jul 2021

Abstract

To reveal how gene regulatory networks change over cancer development, multiple time-varying differential networks between adjacent cancer stages should be estimated simultaneously. Since the network rewiring may be driven by the perturbation of certain individual genes, there may be some hub nodes shared by these differential networks. Although several methods have been developed to estimate differential networks from gene expression data, most of them are designed for estimating a single differential network, which neglect the similarities between different differential networks. In this article, we propose a new Gaussian graphical model-based method to jointly estimate multiple time-varying differential networks for identifying network rewiring over cancer development. A D-trace loss is used to determine the differential networks. A tree-structured group Lasso penalty is designed to identify the common hub nodes shared by different differential networks and the specific hub nodes unique to individual differential networks. Simulation experiment results demonstrate that our method outperforms other state-of-the-art techniques in most cases. We also apply our method to The Cancer Genome Atlas data to explore gene network rewiring over different breast cancer stages. Hub nodes in the estimated differential networks rediscover well known genes associated with the development and progression of breast cancer.

Research Area(s)

  • Differential network analysis, Graphical model, Network rewiring, Tree-structured group Lasso penalty

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