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---
title: "Galaxy API R Client"
output:html_notebook
---

The Galaxy container image

The README in the @bgruening repository contains detailed instructions for controlling the container behavior.

In particular, this section decsribes default values of the admin account name, and the way to override it. The default is:

[email protected]

and the password is admin.

You can override in this way any parameter from the galaxy config file. This is not a container feature, this is a core Galaxy feature. When its config parser sees an environment variable GALAXY_CONFIG_XXX, it will use its value to override the galaxy.yaml parameter xxx.

Starting the container(s)

Galaxy container

In a terminal on your host:

• docker create -v /export --name galaxy-store bgruening/galaxy-stable /bin/true This is a big image - will take a while to pull.
• docker run -d -p 8080:80 -p 8800:8800 --rm --name galaxy --privileged --volumes-from galaxy-store bgruening/galaxy-stable We use --privileged to auto-mount reference data through CVMFS (works on Windows?)
• Access container in Chrome at localhost:8080.
• Go to User->Preferences->Manage API key. Click Create a new key. Copy the key somewhere to use in your client R session.

R Studio Container

You can also use some other R, but we installed a couple of necessary packages into our container

In a terminal on your host:

• docker run -v /home/rstudio/work2 --name rstudio-work sliders/rstudio-bcbiornaseq:v2 bash -c "chown -R rstudio. /home/rstudio/work2" We created a volume owned by the user rstudio from the container
• docker run -d -p 8787:8787 -e PASSWORD=bcbio -e ROOT=TRUE --volumes-from rstudio-work sliders/rstudio-bcbiornaseq:v2
• Access the R Studio Server at localhost:8787
• Login with user rstudio and password bcbio
• Open Tools->Terminal, and cd work2 && git clone https://github.com/andreyto/galaxy_r_api_workshop.git
• sudo apt-get install python-pip
• sudo pip install parsec This is a command-line (CLI) client for the remote Galaxy API. Calling it in a subprocess from a R session is the easiest way to access Galaxy API from R since there are no R library bindings yet.

Working in R

The API and bindings from different languages are described here

Define API key and URL

For now, this is just to record them somewhere to paste later in the terminal.

Use the host IP for the URL. This should be the IP of the real host interface that existed before Docker has created a bunch of other interface. You can find it through your OS Network Properties control panel, whatever it is. Note: localhost or 127.0.0.1 will not work.

In your R console, do this:

GAL_URL="http://10.25.117.136:8080"
GAL_API_KEY="73c730a5ac10f80d363bcd46b4ca28a8"

At first, we will still run commands from the terminal. You can wrap them in R system() function call to program parsec CLI execution from R.

Run init and type your URL and key

parsec init

Download dataset by History Content API ID (get it by clicking on the dataset in Galaxy UI, then clicking on the I icon under the expanded dataset panel).

parsec datasets download_dataset --file_path . 5969b1f7201f12ae

In your R console, do this:

data = readRDS("Galaxy1-[copd_brushings_rnaseq_bcb.rds].binary")
data

You can play with other parsec commands, such as those that list datasets in histories. Look at parsec GitHub README for the inspiration. parsec reflects most of the Galaxy API implemented by the Bioblend Python client library.

Build Galaxy tool that works on our serialized BCBio SummarizedExperiment dataset

This is a very primitive prototype. We do not define a datatype and use default binary format for the rds file.

The primary objective is to demonstrate running a simple Rscript tool using our custom Docker container to provide the dependencies.

We bind-mount several Galaxy config files and the tool itself into the container,
and override the necessary locations through GALAXY_CONFIG_xxx environment variables passed into the container. We do it in a fairly simplistic way just for demonstration.

In any case, the real-life tool code development is much better done through Planemo and deployment through a local toolshed.

Note: To provide absolute host paths for bind-mounting, we use Unix shell command pwd, which is not avalibale on Windows. You will have to edit the corresponding paths in the docker run command if you are on Windows. Better still, activate Linux subsystem in Windows 10 and use Bash shell.

In a terminal on your host:

git clone https://github.com/andreyto/galaxy_r_api_workshop.git

docker stop galaxy

  docker run -d --rm -p 8080:80 -p 8800:8800 --name galaxy --privileged --volumes-from galaxy-store \
  -v `pwd`/galaxy_r_api_workshop:/extra \
  -e GALAXY_CONFIG_TOOL_CONFIG_FILE="/extra/config/tool_conf.xml,config/tool_conf.xml.sample,config/shed_tool_conf.xml" \
  -e GALAXY_CONFIG_JOB_CONFIG_FILE="/extra/config/job_conf.xml" \
  bgruening/galaxy-stable \
  bash -c "supervisorctl stop docker; service docker stop; service docker start; startup"

In your Chrome Web browser at the address localhost:8080:

• You should see a tool section on the left called R Tools. Under that, you should see a tool called "r_tool".
• Run the tool providing as input the rds file that you uploaded into Galaxy previously
• Once the output dataset status turns to finished (green), click on the eye icon to view the plot saved in the PDF format
• On your host, explore the content of the files that we bind-mounted from the cloned repository in order to get the tool deployed and working. Notice how we configured the local runner in config/job_conf.xml to execute Docker containers, and how the tool's XML definition file declares the dependency as a container.

Notes:

• When Docker inside the container pulls images, they do not get saved between container restarts. Expect to wait each time after a restart for the full pull when you run the tool for the first time.
• Sometimes, for reasons unclear, you can get an error status from the Galaxy tool, with the message saying that docker is not available (inside the container). This happens after the tool job was in a running (yellow) state for a while, and the Docker was pulling the container for the dependency. Apparently, Docker daemon dies inside the container. Open interactie shell inside the container using docker exec -it galaxy bash and start Docker there again with service docker stop; service docker start. Then, rerun the Galaxy tool.