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

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_genes

Export 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