bio-workflows-spatial-pipeline
End-to-end spatial transcriptomics workflow for Visium/Xenium data. Covers data loading, preprocessing, spatial analysis, domain detection, and visualization with Squidpy. Use when analyzing spatial transcriptomics data.
npx skills add majiayu000/claude-skill-registry --skill spatial-pipeline --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 Transcriptomics Pipeline Complete workflow for analyzing Visium, Xenium, or other spatial transcriptomics data. ## Workflow Overview ``` Spatial data (Space Ranger output) | v [1. Load Data] ---------> Read Visium/Xenium | v [2. QC & Preprocessing] -> Filter, normalize | v [3. Clustering] --------> Standard scRNA-seq clustering | v [4. Spatial Analysis] --> Neighbors, statistics | v [5. Domain Detection] --> Spatial domains | v [6. Visualization] -----> Spatial plots | v Annotated spatial data ``` ## Primary Path: Squidpy + Scanpy ### Step 1: Load Data ```python import scanpy as sc import squidpy as sq import numpy as np import matplotlib.pyplot as plt # Load Visium data (Space Ranger output) adata = sq.read.visium('spaceranger_output/') # Or load from specific files adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5') adata.uns['spatial'] = ... # Add spatial info # For Xenium adata = sq.read.xenium('xenium_output/') print(f'Loaded: {adata.n_obs} spots/cells, {adata.n_vars} genes') ``` ### Step 2: Quality Control ```python # QC metrics adata.var['mt'] = adata.var_names.str.startswith('MT-') sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True) # Visualize QC
- Workflow Overview
- Primary Path: Squidpy + Scanpy
- Step 1: Load Data
- Step 2: Quality Control
- Step 3: Normalization and Clustering
- Step 4: Spatial Analysis
- Step 5: Domain Detection
- Step 6: Visualization
- Complete Workflow Script
- Related Skills
What does the bio-workflows-spatial-pipeline skill do?
End-to-end spatial transcriptomics workflow for Visium/Xenium data. Covers data loading, preprocessing, spatial analysis, domain detection, and visualization with Squidpy. Use when analyzing spatial transcriptomics data.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill spatial-pipeline --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.