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

Jinyu Li, Yutong Lai, Chi Zhang, Qi Zhang

Research output: Contribution to journalArticle

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1064-1083
Number of pages20
JournalJournal of Applied Statistics
Volume47
Issue number6
DOIs
StatePublished - Apr 25 2020

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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