cellcharter-local-optimized

What problem does it solve?

CellCharter Local-Optimized provides a comprehensive toolkit to identify, characterize, and compare spatial clusters in spatial-omics data, enabling researchers to map tissue organization efficiently.

Core Features & Use Cases

• Neighborhood-aware clustering: aggregate neighborhood features to identify spatial domains across tissue sections.
• Domain shape and boundary analysis: compute boundaries and shape metrics (linearity, curl, elongation, purity) for detailed domain characterization.
• Cross-sample and condition comparisons: align spatial clusters across multiple samples and perform differential neighborhood enrichment analyses.
• Flexible data types: supports spatial transcriptomics, spatial proteomics, spatial epigenomics, and multiomics data, with on-demand use of scripts, references, and assets.
• Visualization and exploration: generate intuitive plots of boundaries, enrichments, and domain metrics to facilitate interpretation.

Quick Start

Load your AnnData object with spatial coordinates, construct the spatial graph with a neighborhood approach, cluster cells using CellCharter's pipeline, and compute boundaries and shape metrics to visualize spatial domains. Then compare domains across samples and conditions.