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

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.

KEYWORDS:

• Gene coexpression
• transcriptomic time course
• covariance matrix estimation
• low-rank plus sparse
• WGCNA
• KEGG

Acknowledgements

We thank the Holland Computing Center (HCC) at UNL for computation resources and technical supports. A previous version of this paper is available on bioRxiv [24].

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

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

Additional information

Funding

This work has been supported by NSF ABI (Division of Biological Infrastructure) (Award# DBI-1564621), NSF EPSCoR (RII) Track II (Office of Integrative Activities) (Award# OIA-1736192) and NU Collaborative System Science Seed Grant to CZ and QZ.