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Bioinformatics. 2016 Apr 15;32(8):1244-6. doi: 10.1093/bioinformatics/btv723. Epub 2015 Dec 9.

PharmacoGx: an R package for analysis of large pharmacogenomic datasets.

Author information

1
Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
2
Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
3
Institut De Recherches Cliniques De Montréal, Montreal, QC, Canada.
4
Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
5
Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA, Department of Bioinformatics, Boston University, Boston, MA, USA.
6
Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
7
Beth Israel Deaconess Medical Center, Boston, MA, USA.
8
Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Machine Learning Group (MLG), Department d'Informatique, Université libre de Bruxelles (ULB), Brussels, Belgium, Ontario Institute of Cancer Research, Toronto, ON, Canada.
9
Hospital for Sick Children, Toronto, ON, Canada and Department of Computer Science, University of Toronto, Toronto, ON, Canada.

Abstract

Pharmacogenomics holds great promise for the development of biomarkers of drug response and the design of new therapeutic options, which are key challenges in precision medicine. However, such data are scattered and lack standards for efficient access and analysis, consequently preventing the realization of the full potential of pharmacogenomics. To address these issues, we implemented PharmacoGx, an easy-to-use, open source package for integrative analysis of multiple pharmacogenomic datasets. We demonstrate the utility of our package in comparing large drug sensitivity datasets, such as the Genomics of Drug Sensitivity in Cancer and the Cancer Cell Line Encyclopedia. Moreover, we show how to use our package to easily perform Connectivity Map analysis. With increasing availability of drug-related data, our package will open new avenues of research for meta-analysis of pharmacogenomic data.

AVAILABILITY AND IMPLEMENTATION:

PharmacoGx is implemented in R and can be easily installed on any system. The package is available from CRAN and its source code is available from GitHub.

CONTACT:

bhaibeka@uhnresearch.ca or benjamin.haibe.kains@utoronto.ca

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
26656004
DOI:
10.1093/bioinformatics/btv723
[Indexed for MEDLINE]

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