--- name: freedomintelligence/bio-spatial-transcriptomics-spatial-domains source: https://app.decimal.ai/s/freedomintelligence-bio-spatial-transcriptomics-spatial-domains@1/SKILL.md source_sha256: 03133f19db1d --- # Spatial Domain Detection Identify spatial domains and tissue regions by combining expression and spatial information. ## Required Imports ```python import squidpy as sq import scanpy as sc import numpy as np import matplotlib.pyplot as plt ``` ## Standard Clustering (Expression Only) ```python # 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 ```python # 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 ```python 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) ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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