In neurogenomics/MungeSumstats: Standardise summary statistics from GWAS

#### 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_org <- params$docker_org
docker_registry <- params$docker_registry 
cont <- params$cont 
docker_url <- if(grepl("ghcr.io",docker_registry)){
  paste("https://ghcr.io",cont,sep="/")
} else {
  paste("https://hub.docker.com/repository/docker",docker_org,pkg,sep="/")
}

Installation

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

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 cont`

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 8900:8787 \
  `r cont`

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.