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cdesc_char.RData 100644 5 kb
ds.RData 100644 900 kb
gene_set.RData 100644 3 kb
kd_gct.RData 100644 808 kb
README.md
# cmapR (CMap R code) Parsing and utility functions for analyzing CMap data. To learn more about the CMap project at the Broad Institute, please visit [clue.io](https://clue.io). ## NOTICE - Updates for Bioconductor cmapR has been accepted in [Bioconductor](https://www.bioconductor.org/packages/release/bioc/html/cmapR.html). 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. ### Install instructions **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 a slightly earlier version of 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") ### Citation information If you use GCTx and/or cmapR in your work, please cite [Enache et al.](https://www.biorxiv.org/content/early/2017/11/30/227041)