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.


Time-varying Gene regulatory networks with Shortlisted candidate regulators

Description:

Rapid advancement in high-throughput gene expression measurement technologies has 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 (GRNs) demands computationally fast, accurate and highly scalable algorithms. To provide a flexible framework in a significantly time-efficient manner, a novel algorithm, namely TGS, is proposed here. TGS is shown to consume only 29 minutes for a microarray dataset with 4028 genes. Moreover, it provides the flexibility and time-efficiency, without losing the accuracy. Nevertheless, TGS’s main memory requirement grows exponentially with the number of genes, which it tackles by restricting the maximum number of regulators for each gene. Relaxing this restriction remains an important challenge as the true number of regulators is not known a prior.

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TGS 1.0.0

Reference manual

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install.packages("TGS")

1.0.1 by Saptarshi Pyne, 6 months ago


https://www.biorxiv.org/content/early/2018/06/14/272484, https://github.com/sap01/TGS


Report a bug at https://github.com/sap01/TGS/issues


Browse source code at https://github.com/cran/TGS


Authors: Saptarshi Pyne [aut, cre] , Manan Gupta [aut] , Alok Kumar [aut] , Ashish Anand [aut]


Documentation:   PDF Manual  


CC BY-NC-SA 4.0 license


Imports rjson, bnstruct, ggm, foreach, doParallel, minet

Suggests R.rsp, testthat, knitr, rmarkdown


See at CRAN