decoupler is a Python package designed to infer biologically relevant scores from omics data using an ensemble of statistical enrichment methods. It provides a unified framework for researchers, offering a faster and more memory-efficient implementation than its predecessor, streamlining the analysis of biological activities.

How It Works

The package implements an ensemble of computational methods within a unified Python framework to infer enrichment scores from omics data. This approach allows for the extraction of biologically driven insights by consolidating various statistical techniques. The Python implementation is specifically engineered for speed and memory efficiency, offering a significant advantage over older versions.

Quick Start & Requirements

Requires Python 3.11 or newer. Installation options include:

• Minimal: pip install decoupler
• Full dependencies: pip install decoupler[full]
• Conda-forge: mamba create -n=dcp conda-forge::decoupler-py
• Development version: pip install git+https://github.com/scverse/decoupler.git@main An integrated Claude Code Skill can be installed via decoupler-install-skills. Refer to the official documentation, particularly the API documentation, for detailed usage.

Highlighted Details

• Ensemble of diverse statistical methods for enrichment analysis.
• Unified framework for consistent omics data interpretation.
• Optimized for speed and memory efficiency in Python.
• Features an integrated Agent Skill for Claude Code, enabling natural language interaction for analysis tasks.

Maintenance & Community

decoupler is part of the scverse project and fiscally sponsored by NumFOCUS. For questions and help, users can engage on the scverse discourse. Bug reports should be filed via the issue tracker.

Licensing & Compatibility

The license type is not explicitly stated in the provided README. Compatibility for commercial use or closed-source linking is therefore undetermined without clarification.

Limitations & Caveats

A deprecated R version of the tool exists, indicating a shift in focus to the Python implementation. No other specific limitations, alpha status, or known bugs are detailed in the provided text.