Spatial Analysis for IMC
Build Spatial Graph
python
import squidpy as sq
import anndata as ad
# Load phenotyped data
adata = ad.read_h5ad('imc_phenotyped.h5ad')
# Ensure spatial coordinates are set
# adata.obsm['spatial'] should contain (x, y) coordinates
# Build spatial neighbor graph
sq.gr.spatial_neighbors(adata, coord_type='generic', delaunay=True)
# Or by distance
sq.gr.spatial_neighbors(adata, coord_type='generic', radius=50) # 50 pixels
print(f'Built graph with {adata.obsp["spatial_connectivities"].nnz} edges')
Neighborhood Enrichment
python
# Test if cell types are enriched near each other sq.gr.nhood_enrichment(adata, cluster_key='cell_type') # Visualize sq.pl.nhood_enrichment(adata, cluster_key='cell_type', save='nhood_enrichment.png') # Get z-scores zscore = adata.uns['cell_type_nhood_enrichment']['zscore'] # Positive: enriched, Negative: depleted
Co-occurrence Analysis
python
# Analyze co-occurrence of cell types at multiple distances sq.gr.co_occurrence(adata, cluster_key='cell_type') # Plot sq.pl.co_occurrence(adata, cluster_key='cell_type', save='co_occurrence.png')
Ripley's Statistics
python
# Ripley's L function for spatial clustering sq.gr.ripley(adata, cluster_key='cell_type', mode='L') # Plot sq.pl.ripley(adata, cluster_key='cell_type', save='ripley.png') # Interpretation: # L(r) > r: clustering at distance r # L(r) < r: dispersion at distance r # L(r) = r: random distribution
Cell-Cell Interaction
python
# Permutation test for interactions sq.gr.interaction_matrix(adata, cluster_key='cell_type', normalized=True) # Get interaction matrix interaction = adata.uns['cell_type_interactions']
Custom Neighborhood Analysis
python
import pandas as pd
import numpy as np
from scipy.sparse import csr_matrix
def neighborhood_composition(adata, cluster_key='cell_type'):
'''Calculate cell type composition of each cell's neighborhood'''
# Get connectivity matrix
conn = adata.obsp['spatial_connectivities']
cell_types = adata.obs[cluster_key]
type_categories = cell_types.cat.categories
# One-hot encode cell types
type_onehot = pd.get_dummies(cell_types).values
# Neighborhood composition = connectivity * one-hot
nhood_composition = conn @ type_onehot
# Normalize to fractions
nhood_sum = np.array(nhood_composition.sum(axis=1)).flatten()
nhood_sum[nhood_sum == 0] = 1 # Avoid division by zero
nhood_frac = nhood_composition / nhood_sum[:, np.newaxis]
# Add to adata
for i, ct in enumerate(type_categories):
adata.obs[f'nhood_frac_{ct}'] = nhood_frac[:, i]
return nhood_frac
nhood_frac = neighborhood_composition(adata)
Spatial Clustering
python
# Leiden clustering on spatial + expression
# Weight spatial vs molecular information
# Combined graph
sq.gr.spatial_neighbors(adata, coord_type='generic', radius=30)
# Run spatial Leiden
sc.tl.leiden(adata, adjacency=adata.obsp['spatial_connectivities'],
resolution=0.5, key_added='spatial_cluster')
Interaction Hotspots
python
def find_interaction_hotspots(adata, type1, type2, cluster_key='cell_type', radius=50):
'''Find regions with high interaction between two cell types'''
# Get cells of each type
mask1 = adata.obs[cluster_key] == type1
mask2 = adata.obs[cluster_key] == type2
spatial = adata.obsm['spatial']
# For each type1 cell, count nearby type2 cells
from scipy.spatial import cKDTree
tree2 = cKDTree(spatial[mask2])
interaction_scores = np.zeros(mask1.sum())
for i, (x, y) in enumerate(spatial[mask1]):
neighbors = tree2.query_ball_point([x, y], r=radius)
interaction_scores[i] = len(neighbors)
return interaction_scores
cd8_tumor_interactions = find_interaction_hotspots(adata, 'CD8 T cell', 'Tumor', radius=30)
Visualize Spatial Patterns
python
import matplotlib.pyplot as plt
# Spatial plot by cell type
sq.pl.spatial_scatter(adata, color='cell_type', size=3, save='spatial_celltypes.png')
# Multiple markers
sq.pl.spatial_scatter(adata, color=['CD8', 'CD4', 'CD68'], size=2, save='spatial_markers.png')
# Highlight specific interaction
fig, ax = plt.subplots(figsize=(10, 10))
spatial = adata.obsm['spatial']
# Background: all cells gray
ax.scatter(spatial[:, 0], spatial[:, 1], c='lightgray', s=1, alpha=0.5)
# Highlight: CD8 and Tumor
for ct, color in [('CD8 T cell', 'red'), ('Tumor', 'blue')]:
mask = adata.obs['cell_type'] == ct
ax.scatter(spatial[mask, 0], spatial[mask, 1], c=color, s=5, label=ct)
ax.legend()
ax.set_aspect('equal')
plt.savefig('cd8_tumor_spatial.png', dpi=150)
Statistical Testing
python
from scipy import stats
def spatial_association_test(adata, type1, type2, cluster_key='cell_type', n_perm=1000):
'''Permutation test for spatial association between cell types'''
# Observed interaction count
sq.gr.nhood_enrichment(adata, cluster_key=cluster_key)
obs_zscore = adata.uns[f'{cluster_key}_nhood_enrichment']['zscore']
idx1 = list(adata.obs[cluster_key].cat.categories).index(type1)
idx2 = list(adata.obs[cluster_key].cat.categories).index(type2)
observed = obs_zscore[idx1, idx2]
# The z-score is already normalized, so we can use it directly
# p-value from z-score
pvalue = 2 * (1 - stats.norm.cdf(abs(observed)))
return {'zscore': observed, 'pvalue': pvalue}
result = spatial_association_test(adata, 'CD8 T cell', 'Tumor')
print(f"CD8-Tumor association: z={result['zscore']:.2f}, p={result['pvalue']:.4f}")
Export Results
python
# Save spatial analysis results
adata.write('imc_spatial_analyzed.h5ad')
# Export neighborhood enrichment
nhood_df = pd.DataFrame(
adata.uns['cell_type_nhood_enrichment']['zscore'],
index=adata.obs['cell_type'].cat.categories,
columns=adata.obs['cell_type'].cat.categories
)
nhood_df.to_csv('neighborhood_enrichment.csv')
Related Skills
- •phenotyping - Assign cell types first
- •spatial-transcriptomics/spatial-statistics - Similar spatial methods
- •single-cell/cell-communication - Interaction concepts