Installation

Attention

ProteoPy is under rapid initial development. We recommend installing the development version from GitHub to benefit from the latest features and fixes. See Development Version below.

ProteoPy requires Python 3.10 or later. We recommend installing ProteoPy in a dedicated virtual environment.

Creating a Virtual Environment

venv

python -m venv proteopy-env
source proteopy-env/bin/activate  # Linux/macOS
# proteopy-env\Scripts\activate   # Windows

conda

conda create -n proteopy-env "python>=3.10"
conda activate proteopy-env

uv

uv venv proteopy-env
source proteopy-env/bin/activate

Installing ProteoPy

Basic Installation

pip install proteopy

With Notebook Support

For notebook-centric workflows, the [usage] extra installs ipykernel, jupyterlab, and scanpy (for extended analysis functionality such as batch control, PCA, UMAP and more):

pip install proteopy[usage]

Don’t forget to make your environment accessible via jupyter kernels.

python -m ipykernel install --user --name=proteopy-env

Development Version

To install the development version from GitHub:

pip install git+https://github.com/UKHD-NP/proteopy.git

# Update to the latest commit
# pip install --force-reinstall git+https://github.com/UKHD-NP/proteopy.git

Dependencies

ProteoPy is built on established open-source scientific libraries:

NumPy - Numerical computing
SciPy - Scientific computing
pandas - Data manipulation
AnnData - Annotated data structures
Matplotlib / Seaborn - Visualization
scikit-learn - Machine learning utilities

Optional dependencies for extended functionality:

scanpy - Single-cell analysis (batch correction, PCA, UMAP)
MuData / MUON - Multi-omics integration