harpy-analysis 0.3.0

single-cell spatial proteomics analysis that makes you happy

Harpy: single-cell spatial proteomics analysis that makes you happy

💫 If you find Harpy useful, please give us a ⭐! It helps others discover the project and supports continued development.

Installation

Recommended for end-users.

uv venv --python=3.12 # set python version
source .venv/bin/activate # activate the virtual environment
uv pip install "git+https://github.com/saeyslab/harpy.git#egg=harpy-analysis[extra]" # use uv to pip install dependencies
python -c 'import harpy; print(harpy.__version__)' # check if the package is installed

Only for developers. Clone this repository locally, install the .[dev] instead of the [extra] dependencies and read the contribution guide.

# Clone repository from GitHub
uv venv --python=3.12 # set python version
source .venv/bin/activate # activate the virtual environment
uv pip install -e '.[dev]' # use uv to pip install dependencies
python -c 'import harpy; print(harpy.__version__)' # check if the package is installed
# make changes
python -m pytest # run the tests

Checkout the docs for installation instructions using conda.

🧭 Tutorials and Guides

Explore how to use Harpy for segmentation, shallow and deep feature extraction, clustering, and spatial analysis of gigapixel-scale multiplexed data with these step-by-step notebooks:

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• 🚀 Basic Usage of Harpy

  Learn how to read in data, perform tiled segmentation using Cellpose and Dask-CUDA, extract features, and carry out clustering. 👉 Tutorial

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• 🔧 Technology specific advice

  Learn which technologies Harpy supports. 👉 Notebook

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• 🧩 Pixel and Cell Clustering

  Learn how to perform unsupervised pixel- and cell-level clustering using Harpy together with FlowSOM. 👉 Tutorial

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• ✂️ Cell Segmentation

  Explore segmentation workflows in Harpy using different tools:

  • With Instanseg 👉 Tutorial

  • With Cellpose 👉 Tutorial

  💡 Want us to add support for another segmentation method? 👉 Open an issue and let us know!

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• 🧪 Single-cell representations from highly multiplexed images and downstream use with PyTorch

  Learn how single-cell representations can be generated from highly multiplexed images. These representations can then be used downstream to train classifiers in PyTorch. 👉 Tutorial

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• 🧠 Deep Feature Extraction

  Discover how Harpy enables fast, scalable extraction of deep, cell-level features from multiplex imaging data with the KRONOS foundation model for proteomics. 👉 Tutorial

  💡 Want us to add support for another deep feature extraction method? 👉 Open an issue and let us know!

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• 🔬 Shallow Feature Extraction

  Learn to extract shallow features—such as mean, median, and standard deviation of intensities—from multiplex imaging data with Harpy. 👉 Tutorial

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• 🧬 Spatial Transcriptomics

  Learn how to analyze spatial transcriptomics data with Harpy. For detailed information, refer to the SPArrOW documentation.

  👉 Tutorial (Mouse Liver, Resolve Molecular Cartography)

  👉 Tutorial (Human Ovarian Cancer, Xenium 10x Genomics)

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• 🌐 Multiple samples and coordinate systems

  Learn how to work with multiple samples, instrinsic and micron coordinates. 👉 Tutorial

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• 📐 Rasterize and vectorize labels and shapes

  Learn how to convert a segmentation mask (array) into its vectorized form, and segmentation boundaries (polygons) into their rasterized equivalents. This conversion is useful, for example, when integrating annotations (e.g., from QuPath) into downstream spatial omics analysis.👉 Tutorial

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📚 For a complete list of tutorials, visit the Harpy documentation.

Computational benchmark

Explore the benchmark performance of Harpy on a large MACSima tonsil proteomics dataset. 👉 Results

Usage

Learn how Harpy can be integrated into your workflow.

Contributing

See here for info on how to contribute to Harpy.

References

• https://github.com/ashleve/lightning-hydra-template

License

Check the license. Harpy is free for academic usage. For commercial usage, please contact Saeyslab.

Issues

If you encounter any problems, please file an issue along with a detailed description.