PharmacoGx: an R package for analysis of large pharmacogenomic datasets

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 realiz...

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Bibliographic Details
Published in:Bioinformatics 2016-04, Vol.32 (8), p.1244-1246
Main Authors: Smirnov, Petr, Safikhani, Zhaleh, El-Hachem, Nehme, Wang, Dong, She, Adrian, Olsen, Catharina, Freeman, Mark, Selby, Heather, Gendoo, Deena M A, Grossmann, Patrick, Beck, Andrew H, Aerts, Hugo J W L, Lupien, Mathieu, Goldenberg, Anna, Haibe-Kains, Benjamin
Format: Article
Language:English
Subjects:
Bioinformatics
Cancer
Design analysis
Drugs
Encyclopaedias
Genomics
Humans
Neoplasms
Packages
Pharmacogenetics
Pharmacology
Programming Languages
Software
Source code
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Summary: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. 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. bhaibeka@uhnresearch.ca or benjamin.haibe.kains@utoronto.ca Supplementary data are available at Bioinformatics online.
ISSN:1367-4803
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btv723