Parsing and utility functions for analyzing CMap data. To learn more about the CMap project at the Broad Institute, please visit clue.io.
cmapR has been accepted in Bioconductor. In accordance with Bioconductor standards, we have changed some of the function naming conventions. Function names that used to contain .
have been replaced with _
. Hence, parse.gctx
is now parse_gctx
and so on. The older function names will still work with a warning. There is additional info and examples in the vignettes/tutorial.Rmd.
Installing from Bioconductor
In R version 4.0 or newer:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("cmapR")
Dependencies are listed in DESCRIPTION
Docker
A docker container with cmapR can be obtained here: https://hub.docker.com/r/cmap/cmapr. This may be preferable for those who would like to use the package without installing on their system.
Installing from Github source
Perhaps the simplest way to install directly from github is using devtools::install_github("cmap/cmapR")
. Note that this requires having previously installed the devtools
package.
The script install_cmapR.R
takes care of installing all the dependencies and then running devtools::install_github("cmap/cmapR")
, so you can simply source this script after cloning this repository.
Alternatively, you can point your R's install.packages
function at a tarball of the cmapR
archive. You can generate this archive by cloning this repository and doing the following:
# make a gzip tar ball of the repo
R CMD build cmapR
# makes cmapR_1.0.tar.gz
# check that the package is ok
R CMD check cmapR_1.0.tar.gz
Once you have created the tarball, open an R terminal and execute the following:
install.packages("cmapR_1.0.tar.gz", type="source", repos=NULL)
library("cmapR")
You can also source individual files as needed instead of installing the entire package.
# For example, just load the IO methods
source("cmapR/R/io.R")
If you use GCTx and/or cmapR in your work, please cite Enache et al.
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