bio-spatial-transcriptomics-spatial-neighbors

1

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsAudit pendingSnapshot c10968d9405e

Summary

This source did not publish a separate summary. Review SKILL.md before using the skill.

SKILL.md

Version Compatibility

Reference examples tested with: matplotlib 3.8+, numpy 1.26+, scanpy 1.10+, scikit-learn 1.4+, 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 Neighbor Graphs

"Build a spatial neighborhood graph" → Construct spatial connectivity graphs using k-nearest neighbors, Delaunay triangulation, or radius-based methods for downstream spatial statistics.

Python: squidpy.gr.spatial_neighbors(adata, coord_type='generic', n_neighs=6)

Build spatial neighbor graphs for connectivity-based analyses.

Required Imports

import squidpy as sq
import scanpy as sc
import numpy as np

Build K-Nearest Neighbors Graph

Goal: Construct a spatial KNN graph connecting each spot to its nearest spatial neighbors.

Approach: Use Squidpy's spatial_neighbors with k-nearest neighbors on coordinate distances.

# Build spatial KNN graph
sq.gr.spatial_neighbors(adata, n_neighs=6, coord_type='generic')

# Check the graph
print(f"Connectivities shape: {adata.obsp['spatial_connectivities'].shape}")
print(f"Distances shape: {adata.obsp['spatial_distances'].shape}")

Build Delaunay Triangulation Graph

# Delaunay triangulation (natural neighbors)
sq.gr.spatial_neighbors(adata, delaunay=True, coord_type='generic')

Radius-Based Neighbors

# Connect all spots within a radius
sq.gr.spatial_neighbors(adata, radius=100, coord_type='generic')

# For Visium hexagonal grid, use n_rings
sq.gr.spatial_neighbors(adata, n_rings=1, coord_type='grid')  # 6 immediate neighbors
sq.gr.spatial_neighbors(adata, n_rings=2, coord_type='grid')  # Extended neighborhood

# Get connectivities as sparse matrix
conn = adata.obsp['spatial_connectivities']
print(f'Edges in graph: {conn.nnz}')
print(f'Mean neighbors per spot: {conn.nnz / adata.n_obs:.1f}')

# Get distances
dist = adata.obsp['spatial_distances']
nonzero_dist = dist.data[dist.data > 0]
print(f'Mean neighbor distance: {nonzero_dist.mean():.1f}')

from scipy.sparse import csr_matrix

spot_idx = 0
conn = adata.obsp['spatial_connectivities']

# Get neighbor indices
neighbor_indices = conn[spot_idx].nonzero()[1]
print(f'Spot {spot_idx} has {len(neighbor_indices)} neighbors: {neighbor_indices}')

# Get distances to neighbors
dist = adata.obsp['spatial_distances']
neighbor_distances = dist[spot_idx, neighbor_indices].toarray().flatten()
print(f'Distances: {neighbor_distances}')

# Standard expression-based neighbors (for comparison)
sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30)

# Now adata has both:
# - adata.obsp['spatial_connectivities'] (spatial)
# - adata.obsp['connectivities'] (expression)

# Build both graphs
sq.gr.spatial_neighbors(adata, n_neighs=6, coord_type='generic')
sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30)

# Weighted combination (manual)
alpha = 0.5  # Weight for spatial vs expression
spatial_conn = adata.obsp['spatial_connectivities']
expr_conn = adata.obsp['connectivities']

# Normalize and combine
from sklearn.preprocessing import normalize
spatial_norm = normalize(spatial_conn, norm='l1', axis=1)
expr_norm = normalize(expr_conn, norm='l1', axis=1)
combined = alpha * spatial_norm + (1 - alpha) * expr_norm

adata.obsp['combined_connectivities'] = combined

import matplotlib.pyplot as plt

# Get coordinates
coords = adata.obsm['spatial']
conn = adata.obsp['spatial_connectivities']

fig, ax = plt.subplots(figsize=(10, 10))

# Draw edges
rows, cols = conn.nonzero()
for i, j in zip(rows, cols):
    if i < j:  # Avoid drawing twice
        ax.plot([coords[i, 0], coords[j, 0]], [coords[i, 1], coords[j, 1]], 'k-', alpha=0.1, linewidth=0.5)

# Draw nodes
ax.scatter(coords[:, 0], coords[:, 1], s=10, c='blue', alpha=0.5)
ax.set_aspect('equal')
plt.title('Spatial neighbor graph')

import networkx as nx
from scipy.sparse import csr_matrix

conn = adata.obsp['spatial_connectivities']
G = nx.from_scipy_sparse_array(conn)

print(f'Nodes: {G.number_of_nodes()}')
print(f'Edges: {G.number_of_edges()}')
print(f'Average degree: {2 * G.number_of_edges() / G.number_of_nodes():.2f}')
print(f'Connected components: {nx.number_connected_components(G)}')

# Store different neighborhood sizes
for n_neighs in [4, 6, 10]:
    sq.gr.spatial_neighbors(adata, n_neighs=n_neighs, coord_type='generic')
    adata.obsp[f'spatial_conn_{n_neighs}'] = adata.obsp['spatial_connectivities'].copy()
    adata.obsp[f'spatial_dist_{n_neighs}'] = adata.obsp['spatial_distances'].copy()