In RajLabMSSM/echolocatoR: Automated genomic fine-mapping

#### Package name ####
pkg <- read.dcf("../DESCRIPTION", fields = "Package")[1]
library(pkg, character.only = TRUE)
## Docker containers must be lowercase
pkg <- tolower(pkg)
#### Username of DockerHub account ####
docker_user <- "bschilder"

DockerHub

r pkg is now available via DockerHub as a containerised environment with Rstudio and all necessary dependencies pre-installed.

Installation

Method 1: via Docker

First, install Docker if you have not already.

Create an image of the Docker container in command line:

docker pull `r docker_user`/`r pkg`

Once the image has been created, you can launch it with:

docker run \
  -d \
  -e ROOT=true \
  -e PASSWORD="<your_password>" \
  -v ~/Desktop:/Desktop \
  -v /Volumes:/Volumes \
  -p 8787:8787 \
  `r docker_user`/`r pkg`

NOTES

• Make sure to replace <your_password> above with whatever you want your password to be.
• Change the paths supplied to the -v flags for your particular use case.
• The -d ensures the container will run in "detached" mode, which means it will persist even after you've closed your command line session.
• The username will be "rstudio" by default.
• Optionally, you can also install the Docker Desktop to easily manage your containers.

Method 2: via Singularity

If you are using a system that does not allow Docker (as is the case for many institutional computing clusters), you can instead install Docker images via Singularity.

singularity pull docker://`r docker_user`/`r pkg`

Usage

Finally, launch the containerised Rstudio by entering the following URL in any web browser: http://localhost:8787/

Login using the credentials set during the Installation steps.

Session Info

utils::sessionInfo()

RajLabMSSM/echolocatoR documentation built on Jan. 29, 2023, 6:04 a.m.