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 majiayu000/claude-skill-registry --skill 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.
# Spatial Domain Detection 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) ```python # Standard Leiden clustering (ignores spatial context) sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30) sc.tl.leiden(adata, resolution=0.5, key_added='leiden') # Visualize on tissue sq.pl.spatial_scatter(adata, color='leiden', size=1.3) ``` ## Spatial-Aware Clustering with Squidpy ```python # Build spatial neighbors sq.gr.spatial_neighbors(adata, coord_type='generic', n_neighs=6) # Run Leiden on spatial graph sc.tl.leiden(adata, resolution=0.5, key_added='spatial_leiden', neighbors_key='spatial_neighbors') sq.pl.spatial_scatter(adata, color='spatial_leiden', size=1.3) ``` ## Combined Expression + Spatial Graph ```python from scipy.sparse import csr_matrix from sklearn.preprocessing import normalize # Build both graphs sq.gr.spatial_neighbors(adata, coord_type='generic', n_neighs=6) sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30) # Combine graphs (weighted average) spatial_weight = 0.3 spat
- 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 majiayu000/claude-skill-registry --skill 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 majiayu000/claude-skill-registry, a repository with 534 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.