Agent skill

bio-spatial-transcriptomics-spatial-preprocessing

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.

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Install this agent skill to your Project

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

SKILL.md

Version Compatibility

Reference examples tested with: matplotlib 3.8+, numpy 1.26+, scanpy 1.10+, squidpy 1.3+

Before using code patterns, verify installed versions match. If versions differ:

  • Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Spatial Preprocessing

"Preprocess my spatial transcriptomics data" → Calculate spatial QC metrics (genes/spot, mitochondrial fraction), filter spots by expression and tissue coverage, normalize, and select variable genes.

  • Python: scanpy.pp.calculate_qc_metrics() → filter_cells() → normalize_total() on spatial AnnData

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

Goal: Compute per-spot and per-gene quality control statistics.

Approach: Use Scanpy's calculate_qc_metrics to generate total counts, gene counts, and other summary statistics.

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

Goal: Quantify mitochondrial gene expression as a quality indicator.

Approach: Flag MT-prefixed genes, then compute percentage of counts from mitochondrial genes per spot.

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

Goal: Display QC metrics overlaid on tissue coordinates to identify spatial patterns in data quality.

Approach: Use Squidpy or Scanpy spatial plots with QC metric columns as color variables.

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

Goal: Remove low-quality spots based on count, gene, and mitochondrial thresholds.

Approach: Apply sequential filters for minimum counts, minimum genes, and maximum mitochondrial percentage.

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

Goal: Remove lowly expressed genes detected in very few spots.

Approach: Apply a minimum cell count threshold to drop genes with negligible spatial coverage.

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

Goal: Normalize count data to remove library size effects and prepare for downstream analysis.

Approach: Store raw counts as a layer, normalize to median total counts, then log-transform.

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

Goal: Apply variance-stabilizing normalization analogous to Seurat's SCTransform.

Approach: Compute Pearson residuals from raw counts using Scanpy's experimental module.

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

Goal: Identify genes with high expression variability for feature selection.

Approach: Use Scanpy's HVG detection with the Seurat v3 flavor on raw count data.

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

Goal: Identify genes whose expression varies significantly across tissue space.

Approach: Build a spatial neighbor graph, then compute Moran's I autocorrelation to rank genes by spatial variability.

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

Goal: Create a unified gene set that captures both expression variability and spatial patterning.

Approach: Take the union of highly variable genes and top spatially variable genes for downstream analysis.

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

Goal: Execute a full spatial preprocessing workflow from raw data to PCA-ready AnnData.

Approach: Chain QC, filtering, normalization, HVG selection, scaling, and PCA into a single 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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