Spatial Preprocessing

name: bio-spatial-transcriptomics-spatial-preprocessing description: Quality control, filtering, normalization, and feature selection for spatial transcriptomics data. Calculate QC metrics, filter spots/cells, normalize counts, and identify highly variable genes. Use when filtering and normalizing spatial transcriptomics data. 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

Spatial Preprocessing

QC, filtering, normalization, and feature selection for spatial data.

Required Imports

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

Calculate QC Metrics

# Calculate standard QC metrics
sc.pp.calculate_qc_metrics(adata, inplace=True)

# View QC columns
print(adata.obs[['total_counts', 'n_genes_by_counts']].describe())
print(adata.var[['total_counts', 'n_cells_by_counts']].describe())

Calculate Mitochondrial Content

# Mark mitochondrial genes
adata.var['mt'] = adata.var_names.str.startswith('MT-')

# Calculate percent mitochondrial
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)
print(f"Mean MT%: {adata.obs['pct_counts_mt'].mean():.1f}")

Visualize QC Metrics on Tissue

# Plot QC metrics spatially
sq.pl.spatial_scatter(adata, color=['total_counts', 'n_genes_by_counts', 'pct_counts_mt'], ncols=3)

# Or with Scanpy
sc.pl.spatial(adata, color=['total_counts', 'n_genes_by_counts'], spot_size=1.5)

QC Metric Distributions

fig, axes = plt.subplots(1, 3, figsize=(12, 4))
axes[0].hist(adata.obs['total_counts'], bins=50)
axes[0].set_xlabel('Total counts')
axes[1].hist(adata.obs['n_genes_by_counts'], bins=50)
axes[1].set_xlabel('Genes detected')
axes[2].hist(adata.obs['pct_counts_mt'], bins=50)
axes[2].set_xlabel('MT %')
plt.tight_layout()

Filter Spots

# Filter based on QC metrics
print(f'Before filtering: {adata.n_obs} spots')

# Minimum counts and genes
sc.pp.filter_cells(adata, min_counts=500)
sc.pp.filter_cells(adata, min_genes=200)

# Maximum mitochondrial content
adata = adata[adata.obs['pct_counts_mt'] < 20].copy()

print(f'After filtering: {adata.n_obs} spots')

Filter Genes

# Remove genes detected in few spots
print(f'Before filtering: {adata.n_vars} genes')
sc.pp.filter_genes(adata, min_cells=10)
print(f'After filtering: {adata.n_vars} genes')

Normalization

# Store raw counts
adata.layers['counts'] = adata.X.copy()

# Normalize to median total counts
sc.pp.normalize_total(adata, target_sum=1e4)

# Log transform
sc.pp.log1p(adata)

SCTransform-like Normalization

# Pearson residuals normalization (similar to SCTransform)
# Requires raw counts
adata_raw = adata.copy()
adata_raw.X = adata_raw.layers['counts']

sc.experimental.pp.normalize_pearson_residuals(adata_raw)
adata.layers['pearson'] = adata_raw.X.copy()

Highly Variable Genes

# Find HVGs
sc.pp.highly_variable_genes(adata, n_top_genes=2000, flavor='seurat_v3', layer='counts')

# View HVG stats
print(f"Found {adata.var['highly_variable'].sum()} HVGs")
sc.pl.highly_variable_genes(adata)

Spatially Variable Genes

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

# Find spatially variable genes using Moran's I
sq.gr.spatial_autocorr(adata, mode='moran', genes=adata.var_names[:1000])

# Get top spatially variable genes
svg = adata.uns['moranI'].sort_values('I', ascending=False)
print('Top spatially variable genes:')
print(svg.head(20))

Combine HVG and SVG

# Get union of highly variable and spatially variable genes
hvg = set(adata.var_names[adata.var['highly_variable']])
svg_top = set(adata.uns['moranI'].head(500).index)
selected_genes = hvg | svg_top

print(f'HVG: {len(hvg)}, SVG: {len(svg_top)}, Union: {len(selected_genes)}')

# Subset to selected genes for downstream
adata_subset = adata[:, list(selected_genes)].copy()

Scale Data

# Scale for PCA (use log-normalized data)
sc.pp.scale(adata, max_value=10)

PCA

# Run PCA
sc.tl.pca(adata, n_comps=50)

# Variance explained
sc.pl.pca_variance_ratio(adata, n_pcs=50)

Complete Preprocessing Pipeline

import squidpy as sq
import scanpy as sc

# Load data
adata = sq.read.visium('spaceranger_output/')

# QC
adata.var['mt'] = adata.var_names.str.startswith('MT-')
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)

# Filter
sc.pp.filter_cells(adata, min_counts=1000)
sc.pp.filter_cells(adata, min_genes=500)
adata = adata[adata.obs['pct_counts_mt'] < 20].copy()
sc.pp.filter_genes(adata, min_cells=10)

# Normalize
adata.layers['counts'] = adata.X.copy()
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)

# HVGs
sc.pp.highly_variable_genes(adata, n_top_genes=2000, flavor='seurat_v3', layer='counts')

# Scale and PCA
sc.pp.scale(adata, max_value=10)
sc.tl.pca(adata, n_comps=50)

print(f'Preprocessed: {adata.n_obs} spots, {adata.n_vars} genes')
adata.write_h5ad('preprocessed.h5ad')

Related Skills

• spatial-data-io - Load spatial data
• spatial-neighbors - Build spatial graphs
• single-cell/preprocessing - Non-spatial preprocessing