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bio-workflows-spatial-pipeline

Automate spatial transcriptomics analysis for Visium and Xenium data.

Updated Mar 30, 2026
Search Tags:#domain-detection#spatial-transcriptomics#scanpy#xenium#spatial-analysis#visium#squidpy
One-click install
npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-workflows-spatial-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-workflows-spatial-pipeline
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/bio-wf-spatial-pipeline
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-workflows-spatial-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

End-to-end spatial transcriptomics analysis is complex and time-consuming; this workflow automates data loading, QC, normalization, clustering, spatial analysis, domain detection, and visualization to deliver interpretable tissue maps.

Core Features & Use Cases

  • Load spatial data from Visium/Xenium outputs
  • Perform QC, normalization, HVG selection, clustering, and spatial analysis
  • Detect tissue domains and visualize spatial gene expression patterns
  • Use case: researchers can rapidly obtain spatial domains and marker genes for tissue sections

Quick Start

Run the spatial pipeline on your Visium spaceranger_output or Xenium results to begin analysis.

Frequently Asked Questions about bio-workflows-spatial-pipeline

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

FAQPage Schema
How do I automate spatial transcriptomics analysis for Visium data?▼

You can automate spatial transcriptomics analysis by running a workflow that handles data loading, QC, normalization, clustering, and tissue domain detection for Visium spaceranger outputs to produce interpretable tissue maps.

What is the best way to detect tissue domains from Xenium results?▼

Detecting tissue domains from Xenium results involves applying an automated pipeline that performs spatial analysis and visualization using Squidpy and Scanpy to identify spatial gene expression patterns.

Does this spatial analysis workflow work with both Visium and Xenium data?▼

Yes, the spatial analysis workflow supports both Visium and Xenium data, processing compatible inputs such as Space Ranger outputs or Xenium results to deliver spatial domains and marker genes.

Do I need Squidpy and Scanpy installed to run the spatial transcriptomics pipeline?▼

Yes, you need Python packages Squidpy and Scanpy installed to run the spatial transcriptomics pipeline, as these frameworks provide the foundational functions for loading, QC, normalization, and spatial analysis.

How do I visualize spatial gene expression patterns after clustering?▼

To visualize spatial gene expression patterns after clustering, the automated workflow generates interpretable tissue maps directly from your normalized spatial data.

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