TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework

Authors

Jinyu Li, University of Nebraska-LincolnFollow
Yutong Lai, University of Nebraska-Lincoln
Chi Zhang, University of Nebraska-LincolnFollow
Qi Zhang, University of Nebraska - LincolnFollow

ORCID IDs

Qi Zhang http://orcid.org/0000-0001-6197-0973

Document Type

Article

Date of this Version

2020

Citation

JOURNAL OF APPLIED STATISTICS 2020, VOL. 47, NO. 6, 1064–1083 https://doi.org/10.1080/02664763.2019.1667311

PMCID: PMC9041782

Comments

© 2019 Informa UK Limited, trading as Taylor & Francis Group

Link to public access file at PubMed Central

Abstract

Various gene network models with distinct physical nature have been widely used in biological studies. For temporal transcriptomic studies, the current dynamic models either ignore the temporal variation in the network structure or fail to scale up to a large number of genes due to severe computational bottlenecks and sample size limitation. Although the correlation-based gene networks are computationally affordable, they have limitations after being applied to gene expression time-course data. We proposed Temporal Gene Coexpression Network Analysis (TGCnA) framework for the transcriptomic time-course data. The mathematical nature of TGCnA is the joint modeling of multiple covariance matrices across time points using a ‘low-rank plus sparse’ framework, in which the network similarity across time points is explicitly modeled in the low-rank component. We demonstrated the advantage of TGCnA in covariance matrix estimation and gene module discovery using both simulation data and real transcriptomic data. The code is available at https://github.com/QiZhangStat/TGCnA.