2023_kegg_analysis

Run with Binder

Local Setup

If you don't already have Git installed, you will be to do so. Follow the directions below to set up Git on your computer:

# Assuming you are using Linux install Git with this command
sudo apt-get update
sudo apt-get install git

git config --global user.name "Username"
git config --global user.email "[email protected]"

# Clone the repo
git clone [email protected]:nasiegel88/2023_kegg_analysis.git

# Change to the repo directory
cd 2023_kegg_analysis

Take a look at the directory contents

ls # Will list all the files in the directory

All the necessary packages are located in environment.yml. This environment file will be used with Docker to create a workspace image to run the analysis. We aren't going to interact directly with Docker. Instead, we will use a package called repo2docker to create a "virtual computer" with all our desired packages. To do this, you first need to install repo2docker:

# This will create an environment called `repo2docker` install the package `jupyter-repo2docker`
mamba create -n repo2docker -c conda-forge jupyter-repo2docker

Once jupyter-repo2docker is installed in your environment, you can activate it with mamba activate repo2docker. Once activated, the environment file mentioned earlier can be used to create an image with all the necessary packages to run the Kegg analysis.

With the repo2docker environment activated, you are ready to build the Docker image we will use to run the analysis.

jupyter-repo2docker . # Remember the period!

If you see an error message resembling PermissionError: [Errno 13] Permission denied: '/home/noah/start', the message indicates that the shell script to export the library path is not executable. This be fixed by running chmod u+x start. After running that command, try creating the workspace image gain.

Next, we will move to the Rmarkdown document to practice using KEGGREST. Open analysis.Rmd.

nasiegel88 / 2023_kegg_analysis Goto Github PK