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spatial-annotate

Name: spatial-annotate
Availability: InStock
Author: TianGzlab

Annotate cell types in spatial transcriptomics AnnData objects using marker-based scoring, Tangram, scANVI, or CellAssign.

155|26|Updated Mar 14, 2026

Search Tags:#bioinformatics #spatial transcriptomics #cell type annotation #marker genes #tangram #scanvi

Ready to run

npx skills add https://github.com/TianGzlab/OmicsClaw --skill spatial-annotate

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: spatial-annotate
Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-annotate
Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill spatial-annotate

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, anndata, numpy, pandas, scipy, matplotlib, tangram-sc, scvi-tools, and includes scripts (resource) and references (resource) components.

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.

Frequently Asked Questions about spatial-annotate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I annotate cell types in spatial transcriptomics data?▼

Spatial transcriptomics cell type annotation assigns labels to tissue spots using methods like marker-based scoring, Tangram, scANVI, and CellAssign. It requires AnnData objects with spatial coordinates and optionally a single-cell RNA-seq reference dataset for label transfer.

What is the best way to transfer cell type labels from single-cell RNA-seq to spatial data?▼

Label transfer from single-cell RNA-seq to spatial data maps reference identities onto tissue spots. Tangram and scANVI use the single-cell reference to align molecular profiles, generating annotated spatial data matrices and visualizations of cellular distributions.

Do I need a single-cell reference dataset to perform spatial transcriptomics annotation?▼

A single-cell reference dataset is not required for spatial transcriptomics annotation. While Tangram and scANVI leverage reference data for label transfer, the marker-based scoring method annotates spots directly using predefined marker genes.

Can I use Tangram and scANVI together for spatial data annotation?▼

Tangram and scANVI are supported annotation methods that can be applied to spatial data. Tangram performs mapping integration, while scANVI transfers labels probabilistically, offering flexibility based on available reference data and desired accuracy.

What outputs do I get after annotating spatial transcriptomics cell types?▼

Annotating spatial transcriptomics cell types generates annotated data matrices, spatial maps, UMAP visualizations, and summary reports. These outputs visualize the distribution of identified cell types across the tissue architecture.

Why does spatial transcriptomics annotation require spatial coordinates in the AnnData object?▼

Spatial transcriptomics annotation requires spatial coordinates in the AnnData object to map assigned cell type labels back onto the physical tissue architecture. Without coordinates, the skill cannot generate spatial maps showing cellular distributions.