---
name: bio-spatial-transcriptomics-spatial-multiomics
description: Analyze high-resolution spatial platforms like Slide-seq, Stereo-seq, and Visium HD. Use when working with subcellular resolution or high-density spatial data.
tool_type: python
primary_tool: squidpy
---

## Version Compatibility

Reference examples tested with: Cellpose 3.0+, matplotlib 3.8+, numpy 1.26+, scanpy 1.10+, scipy 1.12+, spatialdata 0.1+, 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 Multi-omics Analysis

**"Analyze my high-resolution spatial data"** → Process subcellular-resolution spatial platforms (Xenium, MERFISH, Slide-seq, Stereo-seq) including cell segmentation, binning strategies, and multi-modal integration.
- Python: `spatialdata` + `squidpy` for unified multi-platform analysis

## Platform Comparison

| Platform | Resolution | Spots/Beads | Coverage |
|----------|------------|-------------|----------|
| Visium | 55 µm | ~5,000 | Tissue-wide |
| Visium HD | 2 µm | ~11M | Subcellular |
| Slide-seq | 10 µm | ~100,000 | High-density |
| Stereo-seq | 0.5 µm | >200M | Subcellular |
| MERFISH | Single-molecule | N/A | Targeted genes |

## Squidpy for High-Resolution Data

**Goal:** Run standard spatial analyses (autocorrelation, neighborhood enrichment, ligand-receptor) on high-resolution spatial data.

**Approach:** Adjust neighbor graph density for high-resolution platforms, then apply standard Squidpy workflows.

```python
import squidpy as sq
import scanpy as sc

# Load spatial data
adata = sc.read_h5ad('spatial_multiomics.h5ad')

# Spatial neighbors (for high-resolution, adjust n_neighs based on density)
sq.gr.spatial_neighbors(adata, coord_type='generic', n_neighs=10, spatial_key='spatial')

# Spatial autocorrelation (Moran's I)
sq.gr.spatial_autocorr(adata, mode='moran', genes=adata.var_names[:100])

# Neighborhood enrichment analysis
sq.gr.nhood_enrichment(adata, cluster_key='cell_type')
sq.pl.nhood_enrichment(adata, cluster_key='cell_type')

# Ligand-receptor analysis
sq.gr.ligrec(adata, n_perms=100, cluster_key='cell_type')
```

## SpatialData Framework

**Goal:** Load and query multi-modal spatial data using the SpatialData unified representation.

**Approach:** Use spatialdata-io readers per platform, then access images, points, shapes, and tables through a single object with spatial queries.

```python
import spatialdata as sd
from spatialdata_io import read_visium, read_xenium

# Read Visium data
sdata = read_visium('visium_output/')

# Read Xenium data (10x Genomics subcellular)
sdata = read_xenium('xenium_output/')

# Read from Zarr
sdata = sd.read_zarr('experiment.zarr')

# Access different elements
images = sdata.images['morphology']
points = sdata.points['transcripts']
shapes = sdata.shapes['cell_boundaries']
table = sdata.tables['adata']

# Query by region
from spatialdata import bounding_box_query
roi = bounding_box_query(sdata, min_coordinate=[0, 0], max_coordinate=[1000, 1000], axes=['x', 'y'])
```

## Slide-seq/Stereo-seq Processing

```python
# For high-density data, bin spots into hexagonal grids
import numpy as np

# Create hexagonal bins
def hexbin_data(adata, gridsize=50):
    coords = adata.obsm['spatial']
    from matplotlib.pyplot import hexbin
    hb = hexbin(coords[:, 0], coords[:, 1], C=None, gridsize=gridsize, reduce_C_function=np.sum)
    return hb

# Squidpy visualization with hex binning
sq.pl.spatial_scatter(adata, shape='hex', size=50, color='cluster')

# Grid-based spatial neighbors for regular patterns
sq.gr.spatial_neighbors(adata, coord_type='grid', n_rings=1)
```

## Subcellular Analysis (MERFISH/Xenium)

**Goal:** Perform transcript-level and subcellular compartment analysis for single-molecule platforms.

**Approach:** Segment cells with Cellpose, then assign individual transcripts to cells based on mask coordinates.

```python
# Transcript-level analysis
# Assign transcripts to compartments
sq.gr.co_occurrence(adata, cluster_key='compartment', spatial_key='spatial')

# Cell segmentation integration
from cellpose import models
model = models.Cellpose(model_type='cyto2')
masks, flows, styles, diams = model.eval(image, diameter=30, channels=[0, 0])

# Map transcripts to cells
def assign_transcripts_to_cells(transcripts_df, masks):
    x, y = transcripts_df['x'].values.astype(int), transcripts_df['y'].values.astype(int)
    transcripts_df['cell_id'] = masks[y, x]
    return transcripts_df[transcripts_df['cell_id'] > 0]
```

## Multi-Modal Integration

**Goal:** Combine spatial gene expression with histological image features for integrated analysis.

**Approach:** Process and segment tissue images, extract image features, then correlate with gene expression.

```python
# Combine spatial transcriptomics with histology
sq.im.process(adata, layer='image', method='smooth', sigma=2)
sq.im.segment(adata, layer='image', method='watershed', thresh=0.1)

# Extract image features
sq.im.calculate_image_features(
    adata, layer='image', features=['texture', 'summary'],
    key_added='img_features', n_jobs=4
)

# Correlate image features with gene expression
from scipy.stats import pearsonr
for gene in ['marker1', 'marker2']:
    r, p = pearsonr(adata.obs['img_feature'], adata[:, gene].X.flatten())
    print(f'{gene}: r={r:.3f}, p={p:.3e}')
```

## Visium HD Specific

```python
# Visium HD produces bin files at multiple resolutions
# Load 8µm binned data (recommended starting point)
adata = sc.read_h5ad('visium_hd_8um.h5ad')

# Downsample to 16µm if needed for initial analysis
# Original 2µm data available for detailed analysis
```

## Quality Metrics

| Metric | Visium | High-Resolution |
|--------|--------|-----------------|
| Genes/spot | >2000 | >500 |
| UMI/spot | >5000 | >1000 |
| Spatial coverage | >80% | >50% |

## Related Skills

- spatial-transcriptomics/spatial-preprocessing - Standard spatial analysis
- single-cell/preprocessing - scRNA-seq concepts
- spatial-transcriptomics/image-analysis - Morphology processing
- single-cell/cell-annotation - Cell type assignment


---
Source attribution: GPTomics
Original project: https://github.com/GPTomics/bioSkills
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 FridrichMethod

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

---

NOTE ON BUNDLED CONTENT

This repository aggregates skills from multiple upstream projects (see the
README for the source list). Each `skills/<name>/` directory may carry its
own license inherited from its upstream source. The MIT license above
applies to this repository's curation and the scripts/workflows in `/`,
`/scripts/`, and `/.github/` — not to the upstream skill content itself.
Consult the upstream repository linked in the README and any LICENSE
file inside a skill directory before reusing skill content commercially.



# Mega AI Hub resource metadata

Creator / publisher: GPTomics
Original project: https://github.com/GPTomics/bioSkills
Attribution basis: Actual original-project package file and versioned publisher credits

Fields of work: Data & Analytics, Education & Research
Roles: Business Intelligence Analyst, Data Scientist, Researcher
Specialties: API, Testing
Tools: Spatialdata, Squidpy
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: c658b63d4df4e24087ac581a2aee6530bcf9aa0b6cdf269f712b887b1187bc3c
Metadata reviewed: 2026-10-27
This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.