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.

Warning

🚨🚨🚨 Warning! 🚨🚨🚨

The original napari-plugin of Squidpy has been moved to napari-spatialdata.

All the functionalities previously available are also implemented in the new plugin, which also has many additional new features.

You can find a rich set of documentation and examples, and we suggest starting with this tutorial.

If you are new to SpatialData, we invite you to take a look at the documentation here.

Manuscript

Please see our manuscript [Palla 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.

Getting started with Squidpy

• Browse Tutorials and Examples.

• Discuss usage on discourse and development on github.

Contributing to Squidpy

We are happy about any contributions! Before you start, check out our contributing guide.