spatial-annotate

What problem does it solve?

This Skill automates the complex and time-consuming process of assigning cell type labels to spatial transcriptomics data, which is crucial for understanding tissue architecture and cellular interactions.

Core Features & Use Cases

• Multiple Annotation Methods: Supports marker-based scoring, Tangram mapping, scANVI transfer, and CellAssign probabilistic models, offering flexibility based on available reference data and desired accuracy.
• Automated Workflow: Handles data preparation, execution of the chosen annotation mechanism, assessment of results, and generation of reports and visualizations.
• Use Case: Annotate cell types in a spatial transcriptomics dataset of a tumor microenvironment using a pre-existing single-cell RNA-seq reference, generating spatial maps of immune cells, fibroblasts, and tumor cells.

Quick Start

Use the spatial-annotate skill to assign cell types to my spatial tissue spots using the default marker-based method.