spatial-integrate

Name: spatial-integrate
Availability: InStock
Author: TianGzlab

Integrate multi-sample spatial transcriptomics data with Harmony, BBKNN, or Scanorama.

155|26|Updated Mar 14, 2026

Search Tags:#integration #harmony #batch correction #spatial transcriptomics #bbknn #scanorama

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, harmonypy, bbknn, scanorama, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of batch effects in multi-sample spatial transcriptomics data, ensuring that biological signals are not obscured by technical variations between samples.

Core Features & Use Cases

Multi-sample Integration: Combines data from different spatial experiments into a unified dataset.
Batch Correction: Employs methods like Harmony, BBKNN, or Scanorama to remove technical biases.
Use Case: Integrate spatial transcriptomics data from multiple donors or experimental batches to identify conserved cell populations and biological pathways across all samples.

Quick Start

Run Harmony to integrate my spatial slices.

Frequently Asked Questions about spatial-integrate

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

FAQPage Schema

How do I perform batch correction on multi-sample spatial transcriptomics data?▼

Batch correction on multi-sample spatial transcriptomics data aligns multiple samples into a shared embedding using methods like Harmony, BBKNN, or Scanorama to mitigate technical batch effects while preserving biological variation.

What is the best way to integrate spatial transcriptomics slices from different experimental batches?▼

Integrating spatial transcriptomics slices from different batches is best handled by aligning them into a unified dataset using Harmony, BBKNN, or Scanorama, which removes technical biases to identify conserved cell populations across samples.

Do I need a batch key in my AnnData objects to run spatial data integration?▼

Yes, spatial data integration requires AnnData objects containing a batch key. This metadata field allows the integration algorithms to distinguish between different experimental samples and correctly apply batch correction.

Can I use Scanorama and Harmony for spatial omics batch correction in Python?▼

Yes, you can use Scanorama and Harmony for spatial omics batch correction in Python. The Skill leverages these dependencies alongside scanpy to align multiple spatial samples into a shared embedding.

Why does batch correction matter for multi-sample spatial transcriptomics analysis?▼

Batch correction matters for multi-sample spatial transcriptomics analysis because technical variations between samples can obscure true biological signals. Correcting these batch effects ensures accurate identification of conserved biological pathways.

When should I use BBKNN instead of Harmony for spatial transcriptomics integration?▼

You should choose between BBKNN and Harmony based on your specific spatial transcriptomics integration needs. Both methods align multiple spatial samples into a shared embedding, but differ in their algorithmic approach to mitigating technical batch effects.