integration-scanorama

Name: integration-scanorama
Availability: InStock
Author: vvvv

Correct batch effects across spatial slices using Scanorama integration.

3|Updated Apr 3, 2026

Search Tags:#spatial-transcriptomics #umap #leiden #adata #multi-slice #scanorama #batch-integration

Ready to run

npx skills add https://github.com/chenyhvvvv/STAT-agent --skill integration-scanorama

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: integration-scanorama
Source: https://github.com/chenyhvvvv/STAT-agent/tree/main/stat_agent/skills/integration-scanorama
Command: npx skills add https://github.com/chenyhvvvv/STAT-agent --skill integration-scanorama

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Batch integration across multiple spatial slices to remove technical variation while preserving biological signal.

Core Features & Use Cases

Collect and prepare slices, identify common genes, and normalize data.
Run Scanorama integration to generate a corrected embedding and joint visualization.
Store results back to slices (e.g., Leiden clustering and embedding) for downstream analysis.
Use Case: combine two or more slices to compare batch effects and biological variation.

Quick Start

Activate the skill with at least two slices loaded in the session to generate a joint Scanorama embedding.

Frequently Asked Questions about integration-scanorama

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

FAQPage Schema

How do I perform batch integration across multiple spatial transcriptomics slices?▼

Batch integration across multiple spatial transcriptomics slices is performed by learning a shared embedding using Scanorama. This process removes technical variation while preserving biological signal across slices.

What does Scanorama batch correction do to my AnnData slices?▼

Scanorama batch correction learns a shared embedding across multiple slices, storing the corrected results in adata.obsm['X_scanorama']. It also generates downstream joint UMAP visualization and Leiden clustering results.

How do I prepare multiple slices for Scanorama integration?▼

Preparing slices for Scanorama integration requires loading at least two slices into the session. The tool then identifies common genes, normalizes the data, and runs the integration to generate a joint corrected embedding.

Can I use Scanorama stitching to compare biological variation between two slices?▼

Yes, you can use Scanorama stitching to combine two or more slices to compare batch effects and biological variation. It generates a joint embedding that enables direct comparison across the corrected slices.

Do I need to identify common genes before running multi-slice batch integration with Scanorama?▼

You do not need to manually identify common genes before running Scanorama. The integration process automatically collects slices, identifies common genes, normalizes data, and applies the batch correction.