bio-spatial-transcriptomics-spatial-domains
Identify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use when identifying tissue domains or spatial regions.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-spatial-transcriptomics-spatial-domains --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, scikit-learn 1.4+, scipy 1.12+, squidpy 1.3+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Spatial Domain Detection **"Identify tissue domains in my spatial data"** → Cluster spots/cells considering both gene expression and physical proximity to define anatomically coherent spatial domains. - Python: `squidpy.gr.spatial_neighbors()` → Leiden clustering with spatial graph, or BayesSpace/SpaGCN Identify spatial domains and tissue regions by combining expression and spatial information. ## Required Imports ```python import squidpy as sq import scanpy as sc import numpy as np import matplotlib.pyplot as plt ``` ## Standard Clustering (Expression Only) **Goal:** Cluster spots based purely on gene expression, ignoring spatial location. **Approach:** Build an expression-based neighbor graph, then apply Lei
- Version Compatibility
- Required Imports
- Standard Clustering (Expression Only)
- Spatial-Aware Clustering with Squidpy
- Combined Expression + Spatial Graph
- BayesSpace (R Integration)
- STAGATE for Spatial Domains
- Evaluate Domain Quality
- Refine Domain Boundaries
- Compare Domain Methods
- Domain Markers
- Annotate Domains
- Related Skills
What does the bio-spatial-transcriptomics-spatial-domains skill do?
Identify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use when identifying tissue domains or spatial regions.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-spatial-transcriptomics-spatial-domains --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.