squidpy

Spatial neighborhood analysis identifies tissue microenvironments by building spatial graphs from Visium, Xenium, or MERFISH coordinates, then quantifying gene expression patterns and cellular interactions across neighboring regions.

Yes, Squidpy integrates with Scanpy workflows, enabling end-to-end pipelines that combine spatial neighborhood analysis and visualization with standard single-cell data processing within the Python ecosystem.

Visualizing spatial gene expression from Visium datasets is done by loading the data into Squidpy, which provides interactive visualization tools to map expression patterns directly onto tissue coordinates.

Spatial omics analysis in Python supports MERFISH and Xenium platforms through Squidpy, which applies spatial statistics, neighborhood graphs, and clustering to reveal gene expression patterns across these datasets.

Spatial neighborhood analysis and clustering on tissue data is performed by constructing neighborhood graphs from spatial coordinates in Squidpy, then applying clustering algorithms to identify tissue architectures and expression patterns.