bio-spatial-transcriptomics-spatial-domains

Identify spatial domains by combining gene expression similarity with spatial proximity using graph-based clustering workflows in Squidpy and Scanpy. This approach integrates tissue architecture and expression patterns to define coherent tissue regions in Visium-like datasets.

Expression-only clustering groups spots by gene similarity, while spatial clustering integrates tissue coordinates. Comparing multiple strategies helps identify robust tissue regions by evaluating whether domains hold when spatial proximity constraints are applied.

Yes, you can annotate spatial domains using domain markers detected through Scanpy and Squidpy. The workflow identifies marker genes for each cluster, helping biologically interpret and label the distinct tissue regions discovered.

Validate spatial domain boundaries by applying smoothing techniques and silhouette-like assessments to the clustering results. This refines domain edges by evaluating cluster consistency across neighboring spots in the tissue section.

Yes, you need a Python environment with Squidpy, Scanpy, and supporting graph-based clustering workflows installed. These dependencies are required to process spatial transcriptomics data, compute spatial graphs, and output domain labels.

Domain detection is applicable to Visium-like datasets and other spatial transcriptomics experiments where spatial context guides domain definition. The workflow processes data that contains both gene expression matrices and tissue coordinate information.