TGS-package

The <code>TGS</code> package provides an implementation of the TGS algorithm
and its variants. This algorithm reconstructs time-varying gene
regulatory networks from time-series gene expression datasets. For
algorithmic details, please see: 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.

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.

Saptarshi Pyne

Rapid Reconstruction of Time-Varying Gene Regulatory Networks

Manan Gupta

Alok Kumar

Ashish Anand

TGS-package function

Please call <code>TGS::LearnTgs()</code> to invoke the TGS algorithm. See
the manual for the required parameters.

TGS-package: TGS: A package for Rapid Reconstruction of Time-Varying Gene Regulatory Networks

Description

Arguments

The <code>LearnTgs</code> function