This project conducts a reproducible data analysis using R and containerized environments to ensure consistent package versions and dependencies. We download and process a dataset, manage dependencies with micromamba, and run analysis scripts organized via a Makefile workflow. This README will guide users through setting up, executing, and understanding each component of the analysis pipeline.
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
## Setup Instructions
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
### Step 1: Install Micromamba
To install micromamba, please refer to the "01install.md" documentation, which provides detailed installation and setup instructions.
## Add your files
### Step 2: Data Download and Integrity Check
The data is sourced from an external URL and checked for integrity using an MD5 checksum to ensure reproducibility. Follow the instructions in `src/download_data.R` to download and verify the data.
-[ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
### Step 3: Creating the Virtual Image
-[ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
Before running the workflow, please refer to the complete documentation in the "01install.md" file for instructions on how to create the virtual image .sif. This ensures that all dependencies are properly encapsulated.
```
### Step 4: Analysis Pipeline Setup
cd existing_repo
The analysis is organized into four sequential scripts (`tp1.R` to `tp4.R`) and managed via a Makefile, ensuring that each step only executes if necessary. The Makefile enforces dependencies, avoiding redundant execution.
-[ ] [Set up project integrations](https://etulab.univ-amu.fr/t19016063/m2reprod/-/settings/integrations)
## Collaborate with your team
-[ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-[ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-[ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
Use the built-in continuous integration in GitLab.
-[ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-[ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-[ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-[ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-[ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
***
### Step 5 : Execute the Workflow
To run the workflow, please refer to the "02run" documentation, which provides detailed instructions on how to execute the workflow within the micromamba environment :
# Editing this README
```
apptainer exec results/containers/m2bsgreprod3.sif make -f workflows/makefile
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
```
This command will:
## Suggestions for a good README
1. Download and prepare data files.
2. Execute analysis scripts in sequence, as defined in the Makefile.
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
3. Generate output in the `results/` directory for each analysis stage.
## Name
Choose a self-explaining name for your project.
## Description
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
## Badges
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
## Visuals
### Scripts
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
## Installation
1.**download_data.R**: Downloads the dataset, verifies the MD5 checksum, and extracts the files if valid.
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
## Usage
2.**tp1.R**:
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
- Loads required libraries.
- Reads and processes genotype data.
- Selects a random subset of 250,000 SNPs for analysis.
- Outputs processed data to `results/tp1`.
## Support
3.**tp2.R**:
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
- Loads data from the previous script.
- Initializes additional libraries.
- Saves the intermediate processed data to `results/tp2`.
## Roadmap
4.**tp3.R**:
If you have ideas for releases in the future, it is a good idea to list them in the README.
- Loads data from `tp2`.
- Performs additional processing and saves to `results/tp3`.
## Contributing
5.**tp4.R**:
State if you are open to contributions and what your requirements are for accepting them.
- Loads data from `tp3`.
- Finalizes data processing and saves results to `results/tp4`.
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
## Data and Outputs
## Authors and acknowledgment
The `results/` directory contains subdirectories for each analysis stage (`tp1` through `tp4`), storing the results of each script. For example, the output from `tp1` is stored in `results/tp1`, and so forth.
Show your appreciation to those who have contributed to the project.
## License
---
For open source projects, say how it is licensed.
## Project status
This README provides a comprehensive guide for setting up, executing, and troubleshooting your project, ensuring clarity and reproducibility for each step.
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
