tomevault-io Version Compatibility
Reference examples tested with: anndata 0.10+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, scipy 1.12+, squidpy 1.3+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(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 Analysis for IMC
"Analyze spatial cell interactions in my IMC data" → Build spatial neighborhood graphs, test for cell-cell interaction enrichment, and identify spatial domains from multiplexed imaging data.
- Python:
squidpy.gr.spatial_neighbors(),squidpy.gr.nhood_enrichment()
Build Spatial Graph
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
# 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
# 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
# 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
# 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
Goal: Characterize the local cellular microenvironment around each cell by quantifying the cell type composition of its spatial neighbors.
Approach: Multiply the spatial connectivity matrix by a one-hot encoding of cell types, then normalize each row to produce fractional neighborhood composition vectors per cell.
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
# 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
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
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
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
# 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
Converted and distributed by TomeVault — claim your Tome and manage your conversions.