TGS: Rapid Reconstruction of Time-Varying Gene Regulatory Networks

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
Author: Saptarshi Pyne ORCID iD [aut, cre], Manan Gupta [aut], Alok Kumar [aut], Ashish Anand ORCID iD [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
CRAN checks: TGS results

Downloads:

Reference manual: TGS.pdf
Vignettes: Chap 1: A Quick Start Guide
Package source: TGS_1.0.1.tar.gz
Windows binaries: r-devel: TGS_1.0.1.zip, r-release: TGS_1.0.1.zip, r-oldrel: TGS_1.0.1.zip
macOS binaries: r-release (arm64): TGS_1.0.1.tgz, r-release (x86_64): TGS_1.0.1.tgz, r-oldrel: TGS_1.0.1.tgz
Old sources: TGS archive

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