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name: bio-spatial-transcriptomics-spatial-domains description: Identify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use when identifying tissue domains or spatial regions. tool_type: python primary_tool: squidpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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Spatial Domain Detection

Identify spatial domains and tissue regions by combining expression and spatial information.

Required Imports

import squidpy as sq
import scanpy as sc
import numpy as np
import matplotlib.pyplot as plt

Standard Clustering (Expression Only)

# Standard Leiden clustering (ignores spatial context)
sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30)
sc.tl.leiden(adata, resolution=0.5, key_added='leiden')

# Visualize on tissue
sq.pl.spatial_scatter(adata, color='leiden', size=1.3)

Spatial-Aware Clustering with Squidpy

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

# Run Leiden on spatial graph
sc.tl.leiden(adata, resolution=0.5, key_added='spatial_leiden', neighbors_key='spatial_neighbors')

sq.pl.spatial_scatter(adata, color='spatial_leiden', size=1.3)

Combined Expression + Spatial Graph

from scipy.sparse import csr_matrix
from sklearn.preprocessing import normalize

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

# Combine graphs (weighted average)
spatial_weight = 0.3
spatial_conn = adata.obsp['spatial_connectivities']
expr_conn = adata.obsp['connectivities']

# Normalize
spatial_norm = normalize(spatial_conn, norm='l1', axis=1)
expr_norm = normalize(expr_conn, norm='l1', axis=1)

# Combine
combined = spatial_weight * spatial_norm + (1 - spatial_weight) * expr_norm
adata.obsp['combined_connectivities'] = csr_matrix(combined)

# Cluster on combined graph
sc.tl.leiden(adata, resolution=0.5, key_added='combined_leiden', adjacency=adata.obsp['combined_connectivities'])

BayesSpace (R Integration)

# BayesSpace provides spatial smoothing for domain detection
# Run in R, then import results

# R code (run separately):
# library(BayesSpace)
# sce <- readRDS("sce.rds")
# sce <- spatialPreprocess(sce, platform="Visium")
# sce <- spatialCluster(sce, q=7, nrep=10000)
# saveRDS(sce, "sce_bayesspace.rds")

# Import BayesSpace results
import rpy2.robjects as ro
from rpy2.robjects import pandas2ri
pandas2ri.activate()

ro.r('sce <- readRDS("sce_bayesspace.rds")')
spatial_clusters = ro.r('colData(sce)$spatial.cluster')
adata.obs['bayesspace'] = list(spatial_clusters)

STAGATE for Spatial Domains

# STAGATE uses graph attention for spatial domain detection
import STAGATE

# Build graph
STAGATE.Cal_Spatial_Net(adata, rad_cutoff=150)
STAGATE.Stats_Spatial_Net(adata)

# Train STAGATE
adata = STAGATE.train_STAGATE(adata, alpha=0)

# Cluster on STAGATE embeddings
sc.pp.neighbors(adata, use_rep='STAGATE')
sc.tl.leiden(adata, resolution=0.5, key_added='stagate_leiden')

Evaluate Domain Quality

# Check if domains are spatially coherent
from sklearn.metrics import silhouette_score

coords = adata.obsm['spatial']
labels = adata.obs['spatial_leiden'].values

# Spatial silhouette score
spatial_silhouette = silhouette_score(coords, labels)
print(f'Spatial silhouette score: {spatial_silhouette:.3f}')

# Expression silhouette score
expr_silhouette = silhouette_score(adata.obsm['X_pca'], labels)
print(f'Expression silhouette score: {expr_silhouette:.3f}')

Refine Domain Boundaries

# Smooth domain assignments using spatial neighbors
from scipy import sparse

def smooth_domains(adata, cluster_key, n_iter=1):
    conn = adata.obsp['spatial_connectivities']
    labels = adata.obs[cluster_key].values
    categories = adata.obs[cluster_key].cat.categories

    for _ in range(n_iter):
        new_labels = []
        for i in range(adata.n_obs):
            neighbors = conn[i].nonzero()[1]
            if len(neighbors) > 0:
                neighbor_labels = labels[neighbors]
                # Majority vote
                unique, counts = np.unique(neighbor_labels, return_counts=True)
                new_labels.append(unique[counts.argmax()])
            else:
                new_labels.append(labels[i])
        labels = np.array(new_labels)

    adata.obs[f'{cluster_key}_smoothed'] = pd.Categorical(labels, categories=categories)

smooth_domains(adata, 'leiden', n_iter=2)
sq.pl.spatial_scatter(adata, color=['leiden', 'leiden_smoothed'], ncols=2)

Compare Domain Methods

# Compare different clustering approaches
from sklearn.metrics import adjusted_rand_score

methods = ['leiden', 'spatial_leiden', 'combined_leiden']
for i, m1 in enumerate(methods):
    for m2 in methods[i+1:]:
        ari = adjusted_rand_score(adata.obs[m1], adata.obs[m2])
        print(f'{m1} vs {m2}: ARI = {ari:.3f}')

Domain Markers

# Find marker genes for each domain
sc.tl.rank_genes_groups(adata, groupby='spatial_leiden', method='wilcoxon')

# Get top markers
markers = sc.get.rank_genes_groups_df(adata, group=None)
print(markers.groupby('group').head(5))

# Plot top markers on tissue
top_markers = markers.groupby('group').head(1)['names'].tolist()
sq.pl.spatial_scatter(adata, color=top_markers[:6], ncols=3)

Annotate Domains

# Manual annotation based on markers
domain_annotations = {
    '0': 'White matter',
    '1': 'Cortex layer 1',
    '2': 'Cortex layer 2/3',
    '3': 'Cortex layer 4',
    '4': 'Cortex layer 5',
    '5': 'Cortex layer 6',
}

adata.obs['domain'] = adata.obs['spatial_leiden'].map(domain_annotations)
sq.pl.spatial_scatter(adata, color='domain', size=1.3)

Related Skills

• spatial-neighbors - Build spatial graphs (prerequisite)
• spatial-statistics - Compute spatial statistics per domain
• single-cell/clustering - Standard clustering methods

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