IsoGeneGUI: Multiple Approaches for Dose-Response Analysis of Microarray Data Using R

Otava, Martin; Sengupta, Rudradev; Shkedy, Ziv; Lin, Dan; Pramana, Setia; Verbeke, Tobias; Haldermans, Philippe; A. Hothorn, Ludwig; Gerhard, Daniel; Kuiper, Rebecca M.; Klinglmueller, Florian; Kasim, Adetayo

IsoGeneGUI: Multiple Approaches for Dose-Response Analysis of Microarray Data Using R

Otava, Martin; Sengupta, Rudradev; Shkedy, Ziv; Lin, Dan; Pramana, Setia; Verbeke, Tobias; Haldermans, Philippe; A. Hothorn, Ludwig; Gerhard, Daniel; Kuiper, Rebecca M.; Klinglmueller, Florian; Kasim, Adetayo

Authors

Martin Otava

Rudradev Sengupta

Ziv Shkedy

Dan Lin

Setia Pramana

Tobias Verbeke

Philippe Haldermans

Ludwig A. Hothorn

Daniel Gerhard

Rebecca M. Kuiper

Florian Klinglmueller

Adetayo Kasim

Abstract

The analysis of transcriptomic experiments with ordered covariates, such as dose-response data, has become a central topic in bioinformatics, in particular in omics studies. Consequently, multiple R packages on CRAN and Bioconductor are designed to analyse microarray data from various perspectives under the assumption of order restriction. We introduce the new R package IsoGene Graphical User Interface (IsoGeneGUI), an extension of the original IsoGene package that includes methods from most of available R packages designed for the analysis of order restricted microarray data, namely orQA, ORIClust, goric and ORCME. The methods included in the new IsoGeneGUI range from inference and estimation to model selection and clustering tools. The IsoGeneGUI is not only the most complete tool for the analysis of order restricted microarray experiments available in R but also it can be used to analyse other types of dose-response data. The package provides all the methods in a user friendly fashion, so analyses can be implemented by users with limited knowledge of R programming.

Citation

Otava, M., Sengupta, R., Shkedy, Z., Lin, D., Pramana, S., Verbeke, T., …Kasim, A. (2017). IsoGeneGUI: Multiple Approaches for Dose-Response Analysis of Microarray Data Using R. The R journal, 9(1),

Journal Article Type	Article
Acceptance Date	Jul 22, 2015
Online Publication Date	May 10, 2017
Publication Date	Jun 1, 2017
Deposit Date	Jul 14, 2017
Publicly Available Date	Jul 14, 2017
Journal	The R journal
Publisher	The R Foundation
Peer Reviewed	Peer Reviewed
Volume	9
Issue	1
Public URL	https://durham-repository.worktribe.com/output/1373967
Publisher URL	https://journal.r-project.org/archive/2017/RJ-2017-002/index.html