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sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters

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sgnesR : An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters. / Tripathi, Shailesh; Lloyd-Price, Jason; Ribeiro, Andre; Yli-Harja, Olli; Dehmer, Matthias; Emmert-Streib, Frank.

In: BMC Bioinformatics, Vol. 18, No. 1, 325, 04.07.2017.

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Tripathi, Shailesh ; Lloyd-Price, Jason ; Ribeiro, Andre ; Yli-Harja, Olli ; Dehmer, Matthias ; Emmert-Streib, Frank. / sgnesR : An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters. In: BMC Bioinformatics. 2017 ; Vol. 18, No. 1.

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@article{6a45434d6a864625a2d55e52e1107f36,
title = "sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters",
abstract = "Background: sgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA). The package allows various options for delay parameters and can easily included in reactions for promoter delay, RNA delay and Protein delay. A user can tune these parameters to model various types of reactions within a cell. As examples, we present two network models to generate expression profiles. We also demonstrated the inference of networks and the evaluation of association measure of edge and non-edge components from the generated expression profiles. Results: The purpose of sgnesR is to enable an easy to use and a quick implementation for generating realistic gene expression data from biologically relevant networks that can be user selected. Conclusions: sgnesR is freely available for academic use. The R package has been tested for R 3.2.0 under Linux, Windows and Mac OS X.",
keywords = "Gene expression data, Gene network, Simulation",
author = "Shailesh Tripathi and Jason Lloyd-Price and Andre Ribeiro and Olli Yli-Harja and Matthias Dehmer and Frank Emmert-Streib",
year = "2017",
month = "7",
day = "4",
doi = "10.1186/s12859-017-1731-8",
language = "English",
volume = "18",
journal = "BMC Bioinformatics",
issn = "1471-2105",
publisher = "Springer Verlag",
number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - sgnesR

T2 - An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters

AU - Tripathi, Shailesh

AU - Lloyd-Price, Jason

AU - Ribeiro, Andre

AU - Yli-Harja, Olli

AU - Dehmer, Matthias

AU - Emmert-Streib, Frank

PY - 2017/7/4

Y1 - 2017/7/4

N2 - Background: sgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA). The package allows various options for delay parameters and can easily included in reactions for promoter delay, RNA delay and Protein delay. A user can tune these parameters to model various types of reactions within a cell. As examples, we present two network models to generate expression profiles. We also demonstrated the inference of networks and the evaluation of association measure of edge and non-edge components from the generated expression profiles. Results: The purpose of sgnesR is to enable an easy to use and a quick implementation for generating realistic gene expression data from biologically relevant networks that can be user selected. Conclusions: sgnesR is freely available for academic use. The R package has been tested for R 3.2.0 under Linux, Windows and Mac OS X.

AB - Background: sgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA). The package allows various options for delay parameters and can easily included in reactions for promoter delay, RNA delay and Protein delay. A user can tune these parameters to model various types of reactions within a cell. As examples, we present two network models to generate expression profiles. We also demonstrated the inference of networks and the evaluation of association measure of edge and non-edge components from the generated expression profiles. Results: The purpose of sgnesR is to enable an easy to use and a quick implementation for generating realistic gene expression data from biologically relevant networks that can be user selected. Conclusions: sgnesR is freely available for academic use. The R package has been tested for R 3.2.0 under Linux, Windows and Mac OS X.

KW - Gene expression data

KW - Gene network

KW - Simulation

U2 - 10.1186/s12859-017-1731-8

DO - 10.1186/s12859-017-1731-8

M3 - Article

VL - 18

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

IS - 1

M1 - 325

ER -