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Added custom data prep and matrix combination steps meant to perform similar but improved functions to the R code. Added readme detailing the code.

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Gabe Richman
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# corto
# Corto Metabolomics Analysis Pipeline
A Python implementation of the corto algorithm for analyzing metabolomics and gene expression data, translated from the original R codebase. This project provides tools for preprocessing multi-omics data and performing network analysis to identify relationships between metabolites and gene expression.
## Background
## Getting started
The original corto algorithm was implemented in R for analyzing gene expression data and identifying master regulators. This project extends and modernizes the implementation by:
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
1. Translating core functionality to Python
2. Adding support for metabolomics data
3. Implementing memory-efficient processing for large datasets
4. Adding parallel processing capabilities
5. Providing a robust command-line interface
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)!
## Code Translation Overview
## Add your files
### Original R Components:
- [ ] [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
- [ ] [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:
The project translates code from several R source files:
- `corto.R`: Core algorithm implementation
- `functions.R`: Utility functions and statistical analysis
- `mra.R`: Master Regulator Analysis functionality
- `gsea.R`: Gene Set Enrichment Analysis components
```
cd existing_repo
git remote add origin https://gitlab.com/omic/next/registry/tools/clei2block.git
git branch -M master
git push -uf origin master
```
### Python Implementation:
## Integrate with your tools
The functionality has been reorganized into two main Python scripts:
- [ ] [Set up project integrations](https://gitlab.com/omic/next/registry/tools/clei2block/-/settings/integrations)
1. `corto-data-prep-final.py`:
- Data loading and validation
- Preprocessing pipeline
- CNV correction
- Quality control metrics
## 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)
- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
## Test and Deploy
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)
***
# Editing this README
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.
## Suggestions for a good README
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.
## 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
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.
2. `corto-matrix-combination-final.py`:
- Network analysis implementation
- Correlation calculations
- Bootstrap analysis
- Results generation
## Installation
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.
```bash
# Clone the repository
git clone https://github.com/yourusername/corto-metabolomics.git
# Install required packages
pip install -r requirements.txt
```
## Usage
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.
## Support
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.
### Data Preparation
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
```bash
python corto-data-prep-final.py \
--metabolomics_file data/metabolomics.csv \
--expression_file data/expression.txt \
--cnv_file data/cnv.csv \
--normalization standard \
--outlier_detection zscore \
--imputation knn
```
## Contributing
State if you are open to contributions and what your requirements are for accepting them.
### Network Analysis
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.
```bash
python corto-matrix-combination-final.py \
--mode corto \
--expression_file prepared_expression.csv \
--metabolomics_file prepared_metabolomics.csv \
--p_threshold 1e-30 \
--nbootstraps 100 \
--nthreads 4 \
--verbose
```
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.
## Key Features
## Authors and acknowledgment
Show your appreciation to those who have contributed to the project.
### Data Preprocessing
- Zero-variance feature removal
- CNV correction
- Outlier detection
- Missing value imputation
- Sample alignment
- Quality control metrics
## License
For open source projects, say how it is licensed.
### Network Analysis
- Two analysis modes:
- 'corto': Original approach keeping matrices separate
- 'combined': Matrix combination approach for higher-order relationships
- Parallel processing for bootstraps
- Memory-efficient chunked processing
- Comprehensive result reporting
## Project status
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.
## Output Files
The pipeline generates several output files:
1. Preprocessed Data:
- `prepared_metabolomics.csv`
- `prepared_expression.csv`
- `prepared_metrics.txt`
2. Network Analysis:
- `corto_network_{mode}.csv`: Network edges and statistics
- `corto_regulon_{mode}.txt`: Regulon object with relationship details