ArticleJournal of applied statistics2020
TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework.
Article in Journal of applied statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- CoRTE: a web-service for constructing temporal networks from genotype-tissue expression data.Bioinformatics advances · 2025Article
- eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research.PLoS computational biology · 2020Article
- TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework.Journal of applied statistics · 2020Article
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Authors and funding
4 authors.
Funding
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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.
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