bio-workflows-spatial-pipeline

name	bio-workflows-spatial-pipeline
description	End-to-end spatial transcriptomics workflow for Visium/Xenium data. Covers data loading, preprocessing, spatial analysis, domain detection, and visualization with Squidpy. Use when analyzing spatial transcriptomics data.
tool_type	python
primary_tool	Squidpy
workflow	true
depends_on	["spatial-transcriptomics/spatial-data-io","spatial-transcriptomics/spatial-preprocessing","spatial-transcriptomics/spatial-neighbors","spatial-transcriptomics/spatial-statistics","spatial-transcriptomics/spatial-domains","spatial-transcriptomics/spatial-visualization"]
qc_checkpoints	[{"after_loading":"Spots/cells detected, image aligned"},{"after_qc":"Low-quality spots filtered, genes detected"},{"after_clustering":"Spatial domains correspond to tissue regions"}]

Spatial Transcriptomics Pipeline

Complete workflow for analyzing Visium, Xenium, or other spatial transcriptomics data.

Workflow Overview

Spatial data (Space Ranger output)
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[1. Load Data] ---------> Read Visium/Xenium
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[2. QC & Preprocessing] -> Filter, normalize
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[3. Clustering] --------> Standard scRNA-seq clustering
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[4. Spatial Analysis] --> Neighbors, statistics
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[5. Domain Detection] --> Spatial domains
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[6. Visualization] -----> Spatial plots
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    v
Annotated spatial data

Primary Path: Squidpy + Scanpy

Step 1: Load Data

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

# Load Visium data (Space Ranger output)
adata = sq.read.visium('spaceranger_output/')

# Or load from specific files
adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')
adata.uns['spatial'] = ...  # Add spatial info

# For Xenium
adata = sq.read.xenium('xenium_output/')

print(f'Loaded: {adata.n_obs} spots/cells, {adata.n_vars} genes')

Step 2: Quality Control

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

# Visualize QC
fig, axes = plt.subplots(1, 3, figsize=(15, 4))
sc.pl.spatial(adata, color='total_counts', ax=axes[0], show=False)
sc.pl.spatial(adata, color=, ax=axes[], show=)
sc.pl.spatial(adata, color=, ax=axes[], show=)
plt.savefig()


sc.pp.filter_cells(adata, min_counts=)
sc.pp.filter_cells(adata, min_genes=)
sc.pp.filter_genes(adata, min_cells=)
adata = adata[adata.obs.pct_counts_mt < , :]

()