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 
[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:
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