bio-spatial-transcriptomics-spatial-domains

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.

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Repository: mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
Last source activity: February 4, 2026 at 19:55
Detected SKILL.md language: English
Stars: 31
Forks: 8

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# 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)

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 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 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')

# 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}')

# 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 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}')

# 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)

# 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)

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

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

bio-spatial-transcriptomics-spatial-domains

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Spatial Domain Detection

Required Imports

Standard Clustering (Expression Only)

Spatial Domain Detection

Required Imports

Standard Clustering (Expression Only)

Spatial-Aware Clustering with Squidpy

Combined Expression + Spatial Graph

BayesSpace (R Integration)

STAGATE for Spatial Domains

Evaluate Domain Quality

Refine Domain Boundaries

Compare Domain Methods

Domain Markers

Annotate Domains

Related Skills

bio-spatial-transcriptomics-spatial-domains

Source facts

Install options

Review the source files

More from this repository

More from this repository

Spatial Domain Detection

Required Imports

Standard Clustering (Expression Only)

Spatial Domain Detection

Required Imports

Standard Clustering (Expression Only)

Spatial-Aware Clustering with Squidpy

Combined Expression + Spatial Graph

BayesSpace (R Integration)

STAGATE for Spatial Domains

Evaluate Domain Quality

Refine Domain Boundaries

Compare Domain Methods

Domain Markers

Annotate Domains

Related Skills