Tutorials

This section contains various tutorials showcasing spatial molecular data analysis with squidpy.

Analysis of spatial datasets using squidpy

This section contains tutorials showcasing core Squidpy functionalities by applying them to a diverse set of different spatial datasets.

• Analyze Imaging Mass Cytometry data
• Analyze seqFISH data
• Analyze Slide-seqV2 data
• Analyze Visium fluorescence data
• Analyze Visium H&E data
• Analyze Merfish data
• Analyze MIBI-TOF image data
• Analyze 4i data
• Analyze Nanostring data
• Analyze Vizgen data
• Vizgen Mouse Liver Squidpy Vignette
• Single-Cell Clustering of Vizgen MERFISH Mouse Liver Data
• Assign Cell Types
• Hepatocyte Zonation
• Neighborhood Enrichment
• Network Centrality Scores
• Autocorrelation: Moran’s I Score
• Conclusion
• Analyze Xenium data

Advanced Tutorials

This section contains tutorials showcasing advanced squidpy functionalities, such as:

• loading spatial data.

• interactive visualization.

• working with z-stacks.

• Import spatial data in AnnData and Squidpy
• ImageContainer object
• Interactive visualization with Napari
• Use z-stacks with ImageContainer

External tutorials

This section contains tutorials showcasing how squidpy can interface with external libraries.

• Predict cluster labels spots using Tensorflow
• Cell-type deconvolution using Tangram
• Nuclei segmentation using Cellpose
• Nuclei segmentation using StarDist
• Feature extraction using CellProfiler