squidpy

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Squidpy - Spatial Single Cell Analysis in Python

Squidpy is a tool for the analysis and visualization of spatial molecular data. It builds on top of scanpy_ and anndata_, from which it inherits modularity and scalability. It provides analysis tools that leverages the spatial coordinates of the data, as well as tissue images if available.

Visit our documentation_ for installation, tutorials, examples and more.

Manuscript

Please see our manuscript Palla, Spitzer et al. (2022)_ in Nature Methods to learn more.

Squidpy's key applications

• Build and analyze the neighborhood graph from spatial coordinates.
• Compute spatial statistics for cell-types and genes.
• Efficiently store, analyze and visualize large tissue images, leveraging skimage_.
• Interactively explore anndata_ and large tissue images in napari_.

Installation

Install Squidpy via PyPI by running::

pip install squidpy
# or with napari included
pip install 'squidpy[interactive]'

or via Conda as::

conda install -c conda-forge squidpy

Contributing to Squidpy

We are happy about any contributions! Before you start, check out our contributing guide <CONTRIBUTING.rst>_.

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.. _Palla, Spitzer et al. (2022): https://doi.org/10.1038/s41592-021-01358-2 .. _scanpy: https://scanpy.readthedocs.io/en/stable/ .. _anndata: https://anndata.readthedocs.io/en/stable/ .. _napari: https://napari.org/ .. _skimage: https://scikit-image.org/ .. _documentation: https://squidpy.readthedocs.io/en/stable/ .. _website: https://scverse.org/ .. _governance: https://scverse.org/about/roles/ .. _NumFOCUS: https://numfocus.org/ .. _donation: https://numfocus.org/donate-to-scverse/

Squidpy is part of the scverse® project (website, governance) and is fiscally sponsored by NumFOCUS. 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.

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