bio-spatial-transcriptomics-spatial-communication
Analyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Infer intercellular signaling, identify communication pathways, and visualize interaction networks. Use when analyzing cell-cell communication in spatial context.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-spatial-transcriptomics-spatial-communication --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+, 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 Cell-Cell Communication Analyze ligand-receptor interactions and cell-cell communication in spatial data. ## Required Imports ```python import squidpy as sq import scanpy as sc import pandas as pd import numpy as np import matplotlib.pyplot as plt ``` ## Ligand-Receptor Analysis with Squidpy **Goal:** Identify significant ligand-receptor interactions between spatially proximal cell types. **Approach:** Build a spatial neighbor graph, then run permutation-based ligand-receptor analysis using Squidpy's built-in database. **"Find cell-cell communication in my spatial data"** -> Test ligand-receptor co-expression between neighboring cell types with permutation-based significance. ```python # Requires clustered data with cell type annotat
- Version Compatibility
- Required Imports
- Ligand-Receptor Analysis with Squidpy
- Access Ligand-Receptor Results
- Filter Significant Interactions
- Visualize Ligand-Receptor Results
- Specific Ligand-Receptor Pairs
- Custom Ligand-Receptor Database
- Interaction Heatmap
- Network Visualization
- Spatial Visualization of Communication
- Compare Communication Between Conditions
- Pathway Enrichment of Communication Partners
- Export Results
What does the bio-spatial-transcriptomics-spatial-communication skill do?
Analyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Infer intercellular signaling, identify communication pathways, and visualize interaction networks. Use when analyzing cell-cell communication in spatial context.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-spatial-transcriptomics-spatial-communication --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.