Bio Imaging Mass Cytometry Spatial Analysis

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