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

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.

# 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

# 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')
print(f'Cell type combinations: {len(means.index)}')