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.git | ||
*.Rproj | ||
.Rproj.user | ||
.gitignore | ||
.Rhistory | ||
*.md |
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<a href = http://www.grcalculator.org> | ||
<img src="www/images/GRcalculator-logo.jpg" width="250"> | ||
</a> | ||
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## GRcalculator: an online tool for calculating and mining drug response data | ||
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GRcalculator is a Shiny application (http://www.grcalculator.org) developed to accompany the Nature paper *Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs* by Hafner et al. (2016) http://dx.doi.org/10.1038/nmeth.3853. | ||
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For a brief overview of the GRcalculator and the importance of the newly developed GR metrics methodology (Hafner et al. 2016), see this poster: | ||
https://figshare.com/articles/GRcalculator_an_online_tool_for_calculating_and_mining_drug_response_data/4244408 | ||
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#### Citation | ||
Hafner M, Niepel M, Chung M, Sorger PK: Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nature Methods 2016, 13(6):521-527. | ||
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#### Website design and online tool development | ||
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Nick Clark<sup>1</sup>, Marc Hafner<sup>2</sup>, Michal Kouril<sup>1</sup>, Mario Niepel<sup>2</sup>, Elizabeth Williams<sup>2</sup>, Jeremy Muhlich<sup>2</sup> and Mario Medvedovic<sup>1</sup> | ||
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<sup>1</sup> LINCS-BD2K Data Coordination and Integration Center, **University of Cincinnati** | ||
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<sup>2</sup> HMS LINCS Center, **Harvard Medical School** | ||
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## Offline installation | ||
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Install R package dependencies: | ||
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```r | ||
# Install our "GRmetrics" Bioconductor package and others | ||
source("http://bioconductor.org/biocLite.R") | ||
biocLite("GRmetrics") | ||
biocLite("S4Vectors") | ||
# Install our "shinyLi" package | ||
install.packages("devtools") | ||
devtools::install_github("uc-bd2k/shinyLi") | ||
# Install CRAN package dependencies | ||
install.packages(c("shiny","shinyjs","shinyBS","ggplot2","plotly","drc","stringr","readr")) | ||
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``` | ||
Run the application from the R command line: | ||
```r | ||
shiny::runGitHub('uc-bd2k/grcalculator') | ||
``` | ||
## Related software | ||
The GRcalculator tool (http://www.grcalculator.org) is implemented in the form of three integrated Shiny applications (grcalculator, grbrowser and grtutorial). | ||
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### grtutorial (https://github.com/uc-bd2k/grtutorial) | ||
The grtutorial Shiny application (“About GR Metrics” link in the toolbar) provides background information about the advantages of the GR metrics over traditional metrics for quantifying dose-response assays as well as descriptions of the GRcalculator tools. | ||
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### grbrowser (https://github.com/uc-bd2k/grbrowser) | ||
The grbrowser Shiny application (“LINCS Dose-Response Datasets” section in the toolbar) facilitates interactive browsing and mining of drug response data generated by the LINCS project. | ||
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### GRmetrics R package (https://github.com/uc-bd2k/GRmetrics) | ||
This package re-creates the online calculation and visualization tools available at http://www.grcalculator.org/grcalculator/. | ||
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**Bioconductor page**: https://bioconductor.org/packages/GRmetrics | ||
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**Vignette**: https://bioconductor.org/packages/release/bioc/vignettes/GRmetrics/inst/doc/GRmetrics-vignette.html | ||
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```R | ||
# Use the following code to install the GRmetrics package in R | ||
source("http://bioconductor.org/biocLite.R") | ||
biocLite("GRmetrics") | ||
``` | ||
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### MATLAB and Python tools (https://github.com/sorgerlab/gr50_tools) | ||
This repository contains the MATLAB, Python, and R implementations of GR metrics calculations with examples and supplementary information. |