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Squidpy - Spatial Single Cell Analysis in Python

Squidpy is the scverse toolkit for scalable analysis and visualization of spatial molecular data. It builds on scanpy and anndata, providing streamlined APIs for feature extraction, spatial statistics, and interactive exploration of tissue sections together with microscopy images.

Squidpy overview

Documentation

Head over to the documentation for installation instructions, tutorials, how-to guides, and reference material.

Installation

We recommend running Squidpy on a recent Linux or macOS system with Python ≥3.11, but it also works on Windows via WSL.

Install from PyPI with:

pip install squidpy

or from conda-forge:

conda install -c conda-forge squidpy

Interactive visualization

For interactive visualization with napari, please use napari-spatialdata. The original napari plugin from Squidpy has been deprecated and replaced by napari-spatialdata, which offers improved functionality and support for the SpatialData ecosystem.

Key capabilities

  • Build and analyze spatial neighbor graphs directly from Visium, Slide-seq, Xenium, and other spatial omics assays.
  • Compute spatial statistics for cell types and genes, including neighborhood enrichment, co-occurrence, and Moran's I.
  • Efficiently store, featurize, and visualize high-resolution tissue microscopy images via scikit-image.
  • Explore annotated datasets interactively with napari-spatialdata.

Contributing

Contributions are welcome! Please read the contributing guide for instructions on setting up your environment, running tests, and submitting pull requests.

Citation

If you use Squidpy in your research, cite the original publication:

@article{palla:22,
author = {Palla, Giovanni and Spitzer, Hannah and Klein, Michal and Fischer, David and Schaar, Anna Christina and Kuemmerle, Louis Benedikt and Rybakov, Sergei and Ibarra, Ignacio L. and Holmberg, Olle and Virshup, Isaac and Lotfollahi, Mohammad and Richter, Sabrina and Theis, Fabian J.},
title = {Squidpy: a scalable framework for spatial omics analysis},
journal = {Nature Methods},
year = {2022},
month = {Feb},
volume = {19},
number = {2},
pages = {171--178},
issn = {1548-7105},
doi = {10.1038/s41592-021-01358-2},
}

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.

NumFOCUS

About

Spatial Single Cell Analysis in Python

squidpy.readthedocs.io/en/stable/

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Readme
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GitHub - schrf/squidpy: Spatial Single Cell Analysis in Python · GitHub
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Squidpy - Spatial Single Cell Analysis in Python
Squidpy is the scverse toolkit for scalable analysis and visualization of spatial molecular data.
It builds on scanpy and anndata, providing streamlined APIs for feature extraction, spatial statistics, and interactive exploration of tissue sections together with microscopy images.
Squidpy overview
Documentation
Head over to the documentation for installation instructions, tutorials, how-to guides, and reference material.
Installation
We recommend running Squidpy on a recent Linux or macOS system with Python ≥3.11, but it also works on Windows via WSL.
Install from PyPI with:
pip install squidpy
or from conda-forge:
conda install -c conda-forge squidpy
Interactive visualization
For interactive visualization with napari, please use napari-spatialdata.
The original napari plugin from Squidpy has been deprecated and replaced by napari-spatialdata, which offers
improved functionality and support for the SpatialData ecosystem.
Key capabilities
  • Build and analyze spatial neighbor graphs directly from Visium, Slide-seq, Xenium, and other spatial omics assays.
  • Compute spatial statistics for cell types and genes, including neighborhood enrichment, co-occurrence, and Moran's I.
  • Efficiently store, featurize, and visualize high-resolution tissue microscopy images via scikit-image.
  • Explore annotated datasets interactively with napari-spatialdata.
Contributing
Contributions are welcome! Please read the contributing guide for instructions on setting up your environment, running tests, and submitting pull requests.
Citation
If you use Squidpy in your research, cite the original publication:
@article{palla:22,
author = {Palla, Giovanni and Spitzer, Hannah and Klein, Michal and Fischer, David and Schaar, Anna Christina and Kuemmerle, Louis Benedikt and Rybakov, Sergei and Ibarra, Ignacio L. and Holmberg, Olle and Virshup, Isaac and Lotfollahi, Mohammad and Richter, Sabrina and Theis, Fabian J.},
title = {Squidpy: a scalable framework for spatial omics analysis},
journal = {Nature Methods},
year = {2022},
month = {Feb},
volume = {19},
number = {2},
pages = {171--178},
issn = {1548-7105},
doi = {10.1038/s41592-021-01358-2},
}
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.
NumFOCUS
About
Spatial Single Cell Analysis in Python
squidpy.readthedocs.io/en/stable/
Resources
Readme
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GitHub - schrf/squidpy: Spatial Single Cell Analysis in Python · GitHub
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BranchesTags
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BuildTestcodecovLicensePyPIPython VersionRead the Docspre-commit
Squidpy - Spatial Single Cell Analysis in Python
Squidpy is the scverse toolkit for scalable analysis and visualization of spatial molecular data.
It builds on scanpy and anndata, providing streamlined APIs for feature extraction, spatial statistics, and interactive exploration of tissue sections together with microscopy images.
Squidpy overview
Documentation
Head over to the documentation for installation instructions, tutorials, how-to guides, and reference material.
Installation
We recommend running Squidpy on a recent Linux or macOS system with Python ≥3.11, but it also works on Windows via WSL.
Install from PyPI with:
pip install squidpy
or from conda-forge:
conda install -c conda-forge squidpy
Interactive visualization
For interactive visualization with napari, please use napari-spatialdata.
The original napari plugin from Squidpy has been deprecated and replaced by napari-spatialdata, which offers
improved functionality and support for the SpatialData ecosystem.
Key capabilities
  • Build and analyze spatial neighbor graphs directly from Visium, Slide-seq, Xenium, and other spatial omics assays.
  • Compute spatial statistics for cell types and genes, including neighborhood enrichment, co-occurrence, and Moran's I.
  • Efficiently store, featurize, and visualize high-resolution tissue microscopy images via scikit-image.
  • Explore annotated datasets interactively with napari-spatialdata.
Contributing
Contributions are welcome! Please read the contributing guide for instructions on setting up your environment, running tests, and submitting pull requests.
Citation
If you use Squidpy in your research, cite the original publication:
@article{palla:22,
author = {Palla, Giovanni and Spitzer, Hannah and Klein, Michal and Fischer, David and Schaar, Anna Christina and Kuemmerle, Louis Benedikt and Rybakov, Sergei and Ibarra, Ignacio L. and Holmberg, Olle and Virshup, Isaac and Lotfollahi, Mohammad and Richter, Sabrina and Theis, Fabian J.},
title = {Squidpy: a scalable framework for spatial omics analysis},
journal = {Nature Methods},
year = {2022},
month = {Feb},
volume = {19},
number = {2},
pages = {171--178},
issn = {1548-7105},
doi = {10.1038/s41592-021-01358-2},
}
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.
NumFOCUS
About
Spatial Single Cell Analysis in Python
squidpy.readthedocs.io/en/stable/
Resources
Readme
License
Contributing
Contributing
Activity
Stars
0 stars
Watchers
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GitHub - schrf/squidpy: Spatial Single Cell Analysis in Python · GitHub
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BranchesTags
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BuildTestcodecovLicensePyPIPython VersionRead the Docspre-commit
Squidpy - Spatial Single Cell Analysis in Python
Squidpy is the scverse toolkit for scalable analysis and visualization of spatial molecular data.
It builds on scanpy and anndata, providing streamlined APIs for feature extraction, spatial statistics, and interactive exploration of tissue sections together with microscopy images.
Squidpy overview
Documentation
Head over to the documentation for installation instructions, tutorials, how-to guides, and reference material.
Installation
We recommend running Squidpy on a recent Linux or macOS system with Python ≥3.11, but it also works on Windows via WSL.
Install from PyPI with:
pip install squidpy
or from conda-forge:
conda install -c conda-forge squidpy
Interactive visualization
For interactive visualization with napari, please use napari-spatialdata.
The original napari plugin from Squidpy has been deprecated and replaced by napari-spatialdata, which offers
improved functionality and support for the SpatialData ecosystem.
Key capabilities
  • Build and analyze spatial neighbor graphs directly from Visium, Slide-seq, Xenium, and other spatial omics assays.
  • Compute spatial statistics for cell types and genes, including neighborhood enrichment, co-occurrence, and Moran's I.
  • Efficiently store, featurize, and visualize high-resolution tissue microscopy images via scikit-image.
  • Explore annotated datasets interactively with napari-spatialdata.
Contributing
Contributions are welcome! Please read the contributing guide for instructions on setting up your environment, running tests, and submitting pull requests.
Citation
If you use Squidpy in your research, cite the original publication:
@article{palla:22,
author = {Palla, Giovanni and Spitzer, Hannah and Klein, Michal and Fischer, David and Schaar, Anna Christina and Kuemmerle, Louis Benedikt and Rybakov, Sergei and Ibarra, Ignacio L. and Holmberg, Olle and Virshup, Isaac and Lotfollahi, Mohammad and Richter, Sabrina and Theis, Fabian J.},
title = {Squidpy: a scalable framework for spatial omics analysis},
journal = {Nature Methods},
year = {2022},
month = {Feb},
volume = {19},
number = {2},
pages = {171--178},
issn = {1548-7105},
doi = {10.1038/s41592-021-01358-2},
}
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.
NumFOCUS
About
Spatial Single Cell Analysis in Python
squidpy.readthedocs.io/en/stable/
Resources
Readme
License
Contributing
Contributing
Activity
Stars
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GitHub - schrf/squidpy: Spatial Single Cell Analysis in Python · GitHub
Skip to content
BranchesTags
Repository files navigation
BuildTestcodecovLicensePyPIPython VersionRead the Docspre-commit
Squidpy - Spatial Single Cell Analysis in Python
Squidpy is the scverse toolkit for scalable analysis and visualization of spatial molecular data.
It builds on scanpy and anndata, providing streamlined APIs for feature extraction, spatial statistics, and interactive exploration of tissue sections together with microscopy images.
Squidpy overview
Documentation
Head over to the documentation for installation instructions, tutorials, how-to guides, and reference material.
Installation
We recommend running Squidpy on a recent Linux or macOS system with Python ≥3.11, but it also works on Windows via WSL.
Install from PyPI with:
pip install squidpy
or from conda-forge:
conda install -c conda-forge squidpy
Interactive visualization
For interactive visualization with napari, please use napari-spatialdata.
The original napari plugin from Squidpy has been deprecated and replaced by napari-spatialdata, which offers
improved functionality and support for the SpatialData ecosystem.
Key capabilities
  • Build and analyze spatial neighbor graphs directly from Visium, Slide-seq, Xenium, and other spatial omics assays.
  • Compute spatial statistics for cell types and genes, including neighborhood enrichment, co-occurrence, and Moran's I.
  • Efficiently store, featurize, and visualize high-resolution tissue microscopy images via scikit-image.
  • Explore annotated datasets interactively with napari-spatialdata.
Contributing
Contributions are welcome! Please read the contributing guide for instructions on setting up your environment, running tests, and submitting pull requests.
Citation
If you use Squidpy in your research, cite the original publication:
@article{palla:22,
author = {Palla, Giovanni and Spitzer, Hannah and Klein, Michal and Fischer, David and Schaar, Anna Christina and Kuemmerle, Louis Benedikt and Rybakov, Sergei and Ibarra, Ignacio L. and Holmberg, Olle and Virshup, Isaac and Lotfollahi, Mohammad and Richter, Sabrina and Theis, Fabian J.},
title = {Squidpy: a scalable framework for spatial omics analysis},
journal = {Nature Methods},
year = {2022},
month = {Feb},
volume = {19},
number = {2},
pages = {171--178},
issn = {1548-7105},
doi = {10.1038/s41592-021-01358-2},
}
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.
NumFOCUS
About
Spatial Single Cell Analysis in Python
squidpy.readthedocs.io/en/stable/
Resources
Readme
License
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
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Report repository
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GitHub - schrf/squidpy: Spatial Single Cell Analysis in Python · GitHub
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BranchesTags
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BuildTestcodecovLicensePyPIPython VersionRead the Docspre-commit
Squidpy - Spatial Single Cell Analysis in Python
Squidpy is the scverse toolkit for scalable analysis and visualization of spatial molecular data.
It builds on scanpy and anndata, providing streamlined APIs for feature extraction, spatial statistics, and interactive exploration of tissue sections together with microscopy images.
Squidpy overview
Documentation
Head over to the documentation for installation instructions, tutorials, how-to guides, and reference material.
Installation
We recommend running Squidpy on a recent Linux or macOS system with Python ≥3.11, but it also works on Windows via WSL.
Install from PyPI with:
pip install squidpy
or from conda-forge:
conda install -c conda-forge squidpy
Interactive visualization
For interactive visualization with napari, please use napari-spatialdata.
The original napari plugin from Squidpy has been deprecated and replaced by napari-spatialdata, which offers
improved functionality and support for the SpatialData ecosystem.
Key capabilities
  • Build and analyze spatial neighbor graphs directly from Visium, Slide-seq, Xenium, and other spatial omics assays.
  • Compute spatial statistics for cell types and genes, including neighborhood enrichment, co-occurrence, and Moran's I.
  • Efficiently store, featurize, and visualize high-resolution tissue microscopy images via scikit-image.
  • Explore annotated datasets interactively with napari-spatialdata.
Contributing
Contributions are welcome! Please read the contributing guide for instructions on setting up your environment, running tests, and submitting pull requests.
Citation
If you use Squidpy in your research, cite the original publication:
@article{palla:22,
author = {Palla, Giovanni and Spitzer, Hannah and Klein, Michal and Fischer, David and Schaar, Anna Christina and Kuemmerle, Louis Benedikt and Rybakov, Sergei and Ibarra, Ignacio L. and Holmberg, Olle and Virshup, Isaac and Lotfollahi, Mohammad and Richter, Sabrina and Theis, Fabian J.},
title = {Squidpy: a scalable framework for spatial omics analysis},
journal = {Nature Methods},
year = {2022},
month = {Feb},
volume = {19},
number = {2},
pages = {171--178},
issn = {1548-7105},
doi = {10.1038/s41592-021-01358-2},
}
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.
NumFOCUS
About
Spatial Single Cell Analysis in Python
squidpy.readthedocs.io/en/stable/
Resources
Readme
License
Contributing
Contributing
Activity
Stars
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0 watching
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GitHub - schrf/squidpy: Spatial Single Cell Analysis in Python · GitHub
Skip to content
BranchesTags
Repository files navigation
BuildTestcodecovLicensePyPIPython VersionRead the Docspre-commit
Squidpy - Spatial Single Cell Analysis in Python
Squidpy is the scverse toolkit for scalable analysis and visualization of spatial molecular data.
It builds on scanpy and anndata, providing streamlined APIs for feature extraction, spatial statistics, and interactive exploration of tissue sections together with microscopy images.
Squidpy overview
Documentation
Head over to the documentation for installation instructions, tutorials, how-to guides, and reference material.
Installation
We recommend running Squidpy on a recent Linux or macOS system with Python ≥3.11, but it also works on Windows via WSL.
Install from PyPI with:
pip install squidpy
or from conda-forge:
conda install -c conda-forge squidpy
Interactive visualization
For interactive visualization with napari, please use napari-spatialdata.
The original napari plugin from Squidpy has been deprecated and replaced by napari-spatialdata, which offers
improved functionality and support for the SpatialData ecosystem.
Key capabilities
  • Build and analyze spatial neighbor graphs directly from Visium, Slide-seq, Xenium, and other spatial omics assays.
  • Compute spatial statistics for cell types and genes, including neighborhood enrichment, co-occurrence, and Moran's I.
  • Efficiently store, featurize, and visualize high-resolution tissue microscopy images via scikit-image.
  • Explore annotated datasets interactively with napari-spatialdata.
Contributing
Contributions are welcome! Please read the contributing guide for instructions on setting up your environment, running tests, and submitting pull requests.
Citation
If you use Squidpy in your research, cite the original publication:
@article{palla:22,
author = {Palla, Giovanni and Spitzer, Hannah and Klein, Michal and Fischer, David and Schaar, Anna Christina and Kuemmerle, Louis Benedikt and Rybakov, Sergei and Ibarra, Ignacio L. and Holmberg, Olle and Virshup, Isaac and Lotfollahi, Mohammad and Richter, Sabrina and Theis, Fabian J.},
title = {Squidpy: a scalable framework for spatial omics analysis},
journal = {Nature Methods},
year = {2022},
month = {Feb},
volume = {19},
number = {2},
pages = {171--178},
issn = {1548-7105},
doi = {10.1038/s41592-021-01358-2},
}
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.
NumFOCUS
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squidpy.readthedocs.io/en/stable/
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GitHub - schrf/squidpy: Spatial Single Cell Analysis in Python · GitHub
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Squidpy - Spatial Single Cell Analysis in Python
Squidpy is the scverse toolkit for scalable analysis and visualization of spatial molecular data.
It builds on scanpy and anndata, providing streamlined APIs for feature extraction, spatial statistics, and interactive exploration of tissue sections together with microscopy images.
Squidpy overview
Documentation
Head over to the documentation for installation instructions, tutorials, how-to guides, and reference material.
Installation
We recommend running Squidpy on a recent Linux or macOS system with Python ≥3.11, but it also works on Windows via WSL.
Install from PyPI with:
pip install squidpy
or from conda-forge:
conda install -c conda-forge squidpy
Interactive visualization
For interactive visualization with napari, please use napari-spatialdata.
The original napari plugin from Squidpy has been deprecated and replaced by napari-spatialdata, which offers
improved functionality and support for the SpatialData ecosystem.
Key capabilities
  • Build and analyze spatial neighbor graphs directly from Visium, Slide-seq, Xenium, and other spatial omics assays.
  • Compute spatial statistics for cell types and genes, including neighborhood enrichment, co-occurrence, and Moran's I.
  • Efficiently store, featurize, and visualize high-resolution tissue microscopy images via scikit-image.
  • Explore annotated datasets interactively with napari-spatialdata.
Contributing
Contributions are welcome! Please read the contributing guide for instructions on setting up your environment, running tests, and submitting pull requests.
Citation
If you use Squidpy in your research, cite the original publication:
@article{palla:22,
author = {Palla, Giovanni and Spitzer, Hannah and Klein, Michal and Fischer, David and Schaar, Anna Christina and Kuemmerle, Louis Benedikt and Rybakov, Sergei and Ibarra, Ignacio L. and Holmberg, Olle and Virshup, Isaac and Lotfollahi, Mohammad and Richter, Sabrina and Theis, Fabian J.},
title = {Squidpy: a scalable framework for spatial omics analysis},
journal = {Nature Methods},
year = {2022},
month = {Feb},
volume = {19},
number = {2},
pages = {171--178},
issn = {1548-7105},
doi = {10.1038/s41592-021-01358-2},
}
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.
NumFOCUS
About
Spatial Single Cell Analysis in Python
squidpy.readthedocs.io/en/stable/
Resources
Readme
License
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages