/bio-imaging-mass-cytometry-spatial-analysis

name: bio-imaging-mass-cytometry-spatial-analysis
description: Spatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying cell-cell interaction patterns in imaging mass cytometry data.
tool_type: python
primary_tool: squidpy

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>` then `help(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 =