The geneXplainR package provides an R client for the geneXplain platform [1]. The geneXplain platform is an online toolbox and workflow management system for a broad range of bioinformatic and systems biology applications. The platform is well-known for its upstream analysis [2], that has been developed to identify causal signalling molecules on the basis of experimental data like expression measurements.
geneXplainR is based on and extends the rbiouml package. A goal of this project is to add functionality that helps to make building R pipelines that use the geneXplain platform easier.
The geneXplainR package can be easily installed from its github repository using devtools.
library(devtools)
install_github("genexplain/geneXplainR")We hope to make geneXplainR available through other channels as well, so that there will be further options to download and install the software.
A script using geneXplain may look like this (please note that shown parameters won't work):
library(geneXplainR)
gx.login("https://platform.genexplain.com","user","password")
# Get a listing of your research projects
gx.ls("data/Projects")Login to a personal platform workspace requires valid user name and password which can be obtained for free on the geneXplain website. If you just wish to try out the functionality of geneXplainR, you can sign into the demo account on our public server platform.genexplain.com by simply calling gx.login(), without parameters. To access another server that provides a demo workspace, you can call gx.login with the server URL as only argument.
For information about geneXplainR, please refer to the vignettes that come with this package. Furthermore, the examples branch of this repository contains a number of examples.
If you find an issue, we would be happy if you let us know either by writing an e-mail to geneXplain or to the GitHub issue system.
- Kel, A., Kolpakov, F., Poroikov, V., Selivanova, G. (2011) GeneXplain — Identification of Causal Biomarkers and Drug Targets in Personalized Cancer Pathways. J. Biomol. Tech. 22(suppl.), S1
- Koschmann, J., Bhar, A., Stegmaier,P., Kel, A. E. and Wingender, E. (2015) “Upstream Analysis”: An integrated promoter-pathway analysis approach to causal interpretation of microarray data. Microarrays 4, 270-286.