Agent skill

spatial-preprocessing

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Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/spatial-transcriptomics-analysis/bioSkills/spatial-preprocessing

SKILL.md

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

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

Calculate QC Metrics

python
# 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

python
# 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

python
# 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

python
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

python
# 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

python
# 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

python
# 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

python
# 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

python
# 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

python
# 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

python
# 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

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

PCA

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

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

Complete Preprocessing Pipeline

python
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

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