TGCnA swMATH ID: 41317 Software Authors: Li, Jinyu; Lai, Yutong; Zhang, Chi; Zhang, Qi Description: TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework. 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 url{https://github.com/QiZhangStat/TGCnA}. Homepage: https://www.tandfonline.com/doi/abs/10.1080/02664763.2019.1667311 Source Code: https://github.com/QiZhangStat/TGCnA Keywords: gene coexpression; transcriptomic time course; covariance matrix estimation; low-rank plus sparse; WGCNA; KEGG Related Software: PLANEX; GeneFriends; COXPRESdb; clusterProfiler; GitHub; KEGG; WGCNA Cited in: 1 Publication Standard Articles 1 Publication describing the Software, including 1 Publication in zbMATH Year TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework. Zbl 07481456Li, Jinyu; Lai, Yutong; Zhang, Chi; Zhang, Qi 2020 Cited by 4 Authors 1 Lai, Yutong 1 Li, Jinyu 1 Zhang, Chi 1 Zhang, Qi Cited in 1 Serial 1 Journal of Applied Statistics Cited in 1 Field 1 Statistics (62-XX) Citations by Year