Rapid advancements in high-throughput gene sequencing technologies have resulted in genome-scale time-series datasets. Uncovering the underlying temporal sequence of gene regulatory events in the form of time-varying gene regulatory networks demands accurate and computationally efficient algorithms. Such an algorithm is 'TGS'. It is proposed in Saptarshi Pyne, Alok Ranjan Kumar, and Ashish Anand. Rapid reconstruction of time-varying gene regulatory networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(1):278{291, Jan-Feb 2020. The TGS algorithm is shown to consume only 29 minutes for a microarray dataset with 4028 genes. This package provides an implementation of the TGS algorithm and its variants.
Version: 1.0.1 Imports: rjson, bnstruct, ggm, foreach, doParallel, minet (≥ 3.38.0) Suggests: R.rsp, testthat (≥ 2.1.0), knitr, rmarkdown Published: 2020-05-07 DOI: 10.32614/CRAN.package.TGS Author: Saptarshi Pyne [aut, cre], Manan Gupta [aut], Alok Kumar [aut], Ashish Anand [aut] Maintainer: Saptarshi Pyne <saptarshipyne01 at gmail.com> BugReports: https://github.com/sap01/TGS/issues License: CC BY-NC-SA 4.0 URL: https://www.biorxiv.org/content/early/2018/06/14/272484, https://github.com/sap01/TGS NeedsCompilation: no Materials: README NEWS In views: Omics CRAN checks: TGS results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=TGS to link to this page.
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