Easy Installation Guide
The installation process of Docker should not take more than 15 minutes. To ensure a successful installation, follow these steps:
Step 1: Verify Docker Installation
Make sure that Docker is installed on your system. If you need help with the installation process, refer to the official Docker installation guide. Once you have installed Docker, open a terminal window and enter the following command to verify the installation:
Step 2: Clone the GitHub Repository
Clone the Ratingperson GitHub repository by running the following command:
git clone https://github.com/ratingperson/RRST.git
Once the repository is downloaded, navigate to the newly created folder.
Step 3: Generate a Docker Container
To generate a container from the
ludlar/rrst image, follow these instructions:
- Make sure you are in the correct folder (i.e., the RRST folder cloned from GitHub).
- Adjust the
-memoryflag according to your system’s capabilities. Please note that the code has only been tested with 16GiB of RAM.
- Optionally, you can set your own password for the RStudio server by replacing
YOURPASSWORDwith your preferred password using the
Now, execute the following code in the terminal (don’t forget to replace
docker run --rm -p 8787:8787 -e PASSWORD=YOURPASSWORD -e ROOT=TRUE -v $PWD:/home/rstudio -m 16g ludlar/rrst
If you encounter any issues, you can try running the code with
sudo; however, it should work fine without it.
Step 4: Verify Container Status
After executing the
docker run command, check if the container is running by typing the following command in the terminal:
Step 5: Access RStudio Server
Open your preferred browser (e.g., Chrome) and enter
localhost:8787 in the address bar. This will redirect you to the login page of the RStudio server.
Enter the following login credentials:
- Username: rstudio
- Password: YOURPASSWORD
Once logged in, you should see the RStudio server interface. For more information on how to get started with the analysis, refer to the “Instruction for use” section below.
If you encounter any issues with Docker, try the following troubleshooting steps:
Start and Stop the Container
You can start and stop the container using the following commands in the terminal:
docker start CONTAINER_ID
docker stop CONTAINER_ID
Adjust Memory Allocation
If you run out of memory in the container, check if you can allocate more by using the
--memory flag with the
docker run command.
Additionally, you might want to increase the memory limit in Docker. To check how much memory is available to Docker, execute the following command:
If you have more system resources available, you can increase the memory limit. If you are using the Docker desktop app, go to “Preferences” -> “Resources” and adjust the memory setting. Keep in mind that Docker’s memory limit restricts the memory available to the container.
For more information on managing Docker resources, visit the official Docker website.
Instructions for Running Analyses
To run all analyses, it should take less than an hour on a laptop with comparable system specifications as mentioned below.
Once you have accessed the RStudio server, you will find a folder for each main figure in the file viewer pane (bottom right). Inside each folder, there is an
.Rmd notebook containing instructions for reproducing the analyses and generating the plots used in the manuscript.
The data required for running the analyses is stored at Mendeley Data. Please refer to the beginning of each
.Rmd notebook for instructions on downloading this data.
Supplementary figures are also produced within the same
.Rmd notebooks. For example, you can find supplementary figures related to Figure 1 in the
All main figure plots exported in the notebooks are available in the
plots/ subfolders within each figure folder. Supplementary figures can be found in the
Suppl_figures/ folder. The expected output from the notebooks should be comparable to the exported figures and the figures in the manuscript.
The code has been thoroughly tested on a MacBook Pro (2017) with specifications similar to those mentioned below:
- Operating System: macOS
- RAM: 16GiB
For detailed information about the R environment used in this project, please refer to the
session_info.txt file. We highly recommend using the Docker image to run the analyses in a reproducible R environment. Note that using more recent installations of certain R packages may lead to slightly different results, particularly for analyses using functions from the R packages Seurat and sctransform.