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 pltLigand-Receptor Analysis with Squidpy
# 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)}')Filter Significant Interactions
# Get significant interactions
pval_threshold = 0.05
# Flatten results to DataFrame
interactions = []
for source_target in pvalues.index:
for lr_pair in pvalues.columns:
pval = pvalues.loc[source_target, lr_pair]
mean_expr = means.loc[source_target, lr_pair]
if pval < pval_threshold and not np.isnan(mean_expr):
source, target = source_target
ligand, receptor = lr_pair
interactions.append({
'source': source,
'target': target,
'ligand': ligand,
'receptor': receptor,
'mean': mean_expr,
'pvalue': pval,
})
interactions_df = pd.DataFrame(interactions)
print(f'Significant interactions: {len(interactions_df)}')
print(interactions_df.head(10))Visualize Ligand-Receptor Results
# Dot plot of top interactions
sq.pl.ligrec(
adata,
cluster_key='cell_type',
source_groups=['Macrophage', 'T_cell'], # Filter source cell types
target_groups=['Epithelial', 'Fibroblast'], # Filter target cell types
pvalue_threshold=0.05,
remove_empty_interactions=True,
)Specific Ligand-Receptor Pairs
# Analyze specific pairs of interest
pairs_of_interest = [
('CD40LG', 'CD40'),
('TGFB1', 'TGFBR1'),
('CCL2', 'CCR2'),
]
sq.pl.ligrec(
adata,
cluster_key='cell_type',
means_range=(0.5, 5), # Filter by expression level
pvalue_threshold=0.01,
)Custom Ligand-Receptor Database
# Use custom ligand-receptor pairs
custom_pairs = pd.DataFrame({
'ligand': ['GENE1', 'GENE2', 'GENE3'],
'receptor': ['GENE4', 'GENE5', 'GENE6'],
})
sq.gr.ligrec(
adata,
cluster_key='cell_type',
interactions=custom_pairs,
n_perms=100,
)Interaction Heatmap
# Create heatmap of interaction counts per cell type pair
def count_interactions_per_pair(pvalues, threshold=0.05):
counts = {}
for source_target in pvalues.index:
sig_count = (pvalues.loc[source_target] < threshold).sum()
counts[source_target] = sig_count
return counts
counts = count_interactions_per_pair(pvalues)
# Convert to matrix
cell_types = adata.obs['cell_type'].unique()
count_matrix = pd.DataFrame(0, index=cell_types, columns=cell_types)
for (source, target), count in counts.items():
count_matrix.loc[source, target] = count
plt.figure(figsize=(8, 8))
plt.imshow(count_matrix.values, cmap='Reds')
plt.xticks(range(len(cell_types)), cell_types, rotation=45, ha='right')
plt.yticks(range(len(cell_types)), cell_types)
plt.colorbar(label='Number of significant interactions')
plt.title('Cell-cell communication strength')
plt.tight_layout()
plt.savefig('interaction_heatmap.png', dpi=150)Network Visualization
import networkx as nx
# Build interaction network
G = nx.DiGraph()
# Add nodes (cell types)
for ct in adata.obs['cell_type'].unique():
G.add_node(ct)
# Add edges (interactions)
for _, row in interactions_df.iterrows():
if G.has_edge(row['source'], row['target']):
G[row['source']][row['target']]['weight'] += 1
else:
G.add_edge(row['source'], row['target'], weight=1)
# Draw network
pos = nx.spring_layout(G, k=2, seed=42)
weights = [G[u][v]['weight'] for u, v in G.edges()]
plt.figure(figsize=(10, 10))
nx.draw_networkx_nodes(G, pos, node_size=1000, node_color='lightblue')
nx.draw_networkx_labels(G, pos, font_size=10)
nx.draw_networkx_edges(G, pos, width=[w/max(weights)*5 for w in weights],
edge_color='gray', arrows=True, arrowsize=20)
plt.title('Cell-cell communication network')
plt.axis('off')
plt.savefig('communication_network.png', dpi=150)Spatial Visualization of Communication
# Visualize ligand and receptor expression spatially
ligand = 'CCL2'
receptor = 'CCR2'
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
# Ligand expression
sc.pl.spatial(adata, color=ligand, ax=axes[0], show=False, title=f'{ligand} (ligand)')
# Receptor expression
sc.pl.spatial(adata, color=receptor, ax=axes[1], show=False, title=f'{receptor} (receptor)')
# Cell types
sc.pl.spatial(adata, color='cell_type', ax=axes[2], show=False, title='Cell types')
plt.tight_layout()
plt.savefig('ligand_receptor_spatial.png', dpi=150)Compare Communication Between Conditions
# Run separately for each condition
for condition in adata.obs['condition'].unique():
adata_cond = adata[adata.obs['condition'] == condition].copy()
sq.gr.spatial_neighbors(adata_cond, coord_type='generic', n_neighs=6)
sq.gr.ligrec(adata_cond, cluster_key='cell_type', n_perms=100)
adata_cond.uns[f'ligrec_{condition}'] = adata_cond.uns['cell_type_ligrec']
# Compare interaction counts
for condition in ['control', 'treated']:
results = adata.uns[f'ligrec_{condition}']
n_sig = (results['pvalues'] < 0.05).sum().sum()
print(f'{condition}: {n_sig} significant interactions')Pathway Enrichment of Communication Partners
# Get genes involved in significant interactions
ligands = interactions_df['ligand'].unique()
receptors = interactions_df['receptor'].unique()
comm_genes = list(set(ligands) | set(receptors))
print(f'Genes involved in communication: {len(comm_genes)}')
# Use for pathway enrichment with pathway-analysis skills
# genes_for_enrichment = comm_genesExport Results
# Save significant interactions
interactions_df.to_csv('significant_interactions.csv', index=False)
# Save as edge list for network tools
edges = interactions_df[['source', 'target', 'ligand', 'receptor', 'mean', 'pvalue']]
edges.to_csv('communication_edges.csv', index=False)Related Skills
- spatial-neighbors - Build spatial graphs (prerequisite)
- spatial-domains - Identify cell types for communication analysis
- pathway-analysis - Enrich communication genes for pathways
- single-cell/markers-annotation - Annotate cell types