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TGS
View on CRAN: Click
here
Download and install TGS package within the R console
Install from CRAN:
install.packages("TGS")
Install from Github:
library("remotes")
install_github("cran/TGS")
Install by package version:
library("remotes")
install_version("TGS", "1.0.1")
Attach the package and use:
library("TGS")
Maintained by
Saptarshi Pyne
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.TGS . https://github.com/sap01/TGS/issues . https://github.com/sap01/TGS . TGS results . TGS.pdf . TGS_1.0.1.tar.gz . TGS_1.0.1.zip . TGS_1.0.1.zip . TGS_1.0.1.zip . TGS_1.0.1.tgz . TGS_1.0.1.tgz . TGS_1.0.1.tgz . TGS_1.0.1.tgz . TGS archive . https://CRAN.R-project.org/package=TGS .
First Published: 2018-11-16
Latest Update: 2020-05-07
Description:
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.
How to cite:
Saptarshi Pyne (2018). TGS: Rapid Reconstruction of Time-Varying Gene Regulatory Networks. R package version 1.0.1, https://cran.r-project.org/web/packages/TGS. Accessed 29 Apr. 2025.
Previous versions and publish date:
1.0.0 (2018-11-16 17:10)
Other packages that cited TGS R package
View TGS citation profile
Other R packages that TGS depends,
imports, suggests or enhances
Complete documentation for TGS
Functions, R codes and Examples using
the TGS R package
Some associated functions: LearnTgs . TGS-package .
Some associated R codes: CompareNet.R . CondMutualInfo.R . RDataToCytoscape.R . TGS-package.R . calcPerfDiNet.R . checkUnrolledDbn.R . compute_cmi.R . convert_dinet_to_undinet.R . count_feed_fwd_edges_undi.R . discretizeData.R . genTrueNetFunc.R . graphFns.R . learnCmiNetStruct.R . learnDbnStruct3dParDeg1.R . learn_TGS.R . learn_mi_net_struct.R . rollDbn.R . Full TGS package functions and examples
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