bio-spatial-transcriptomics-spatial-communication

npx skills add majiayu000/claude-skill-registry --skill spatial-communication-gptomics-bioskills-2 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# 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 ```python # Requires clustered data with cell type annotations adata = sc.read_h5ad('clustered_spatial.h5ad') # Build spatial neighbors if not already done sq.gr.spatial_neighbors(adata, coord_type='generic', n_neighs=6) # Run ligand-receptor analysis sq.gr.ligrec( adata, cluster_key='cell_type', # Column with cell type annotations n_perms=100, # Permutations for significance testing threshold=0.01, # P-value threshold copy=False, ) # Results stored in adata.uns['cell_type_ligrec'] ``` ## Access Ligand-Receptor Results ```python # Get results dictionary ligrec_results = adata.uns['cell_type_ligrec'] # Access different result components means = ligrec_results['means'] # Mean expression pvalues = ligrec_results['pvalues'] # P-values from permutation test metadata = ligrec_results['metadata'] # Ligand-receptor pair annotations print(f'Tested {len(means.columns)} ligand-receptor pairs') p