Decoupler

Abstract

1. Introduction

decoupler is a python package containing different enrichment statistical methods to extract biologically driven scores from omics data within a unified framework. This is its faster and memory efficient Python implementation, a deprecated version in R can be found here.

decoupler is part of the scverse® project (website, governance) and is fiscally sponsored by NumFOCUS. If you like scverse® and want to support our mission, please consider making a tax-deductible donation to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs.

[![Tests][badge-tests]][tests] [![Documentation][badge-docs]][documentation]

2. Objective

Python package to perform enrichment analysis from omics data.

This project demonstrates how Python can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/scverse/decoupler.git
cd decoupler

1. Install the latest stable release from [PyPI][pypi] with minimal dependancies:
2. Install the latest stable full release from [PyPI][pypi] with extra dependancies:
3. Install the latest stable version from [conda-forge][conda] using mamba or conda (pay attention to the -py suffix at the end):
4. Install the latest development version:

pip install decoupler

pip install decoupler[full]

mamba create -n=dcp conda-forge::decoupler-py

pip install git+https://github.com/scverse/decoupler.git@main

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by scverse and published on GitHub under the BSD 3-Clause "New" or "Revised" License. Please follow the license terms and credit the original author when you use or modify this code.