visium

What problem does it solve?

Visium spatial transcriptomics data require specialized workflows to integrate gene expression with tissue context across platforms. This skill provides a guided approach to processing Visium outputs and extracting meaningful spatial patterns.

Core Features & Use Cases

• End-to-end Visium data processing from raw outputs to clustering and spatial visualization.
• Python (Scanpy + Squidpy) workflow for preprocessing, dimensionality reduction, and spatial analysis.
• R workflow using Giotto or Seurat for spatial mapping and visualization.
• Use cases across tissue architecture discovery, spot-level clustering, and spatial gene expression patterning.

Quick Start

Load your Visium output into Scanpy with Squidpy and run the standard preprocessing and spatial clustering workflow.