bio-spatial-transcriptomics-spatial-proteomics
Analyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization. Use when working with multiplexed imaging data, analyzing protein spatial patterns, or integrating spatial proteomics with transcriptomics.
Best use case
bio-spatial-transcriptomics-spatial-proteomics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Analyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization. Use when working with multiplexed imaging data, analyzing protein spatial patterns, or integrating spatial proteomics with transcriptomics.
Teams using bio-spatial-transcriptomics-spatial-proteomics should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/bio-spatial-transcriptomics-spatial-proteomics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bio-spatial-transcriptomics-spatial-proteomics Compares
| Feature / Agent | bio-spatial-transcriptomics-spatial-proteomics | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Analyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization. Use when working with multiplexed imaging data, analyzing protein spatial patterns, or integrating spatial proteomics with transcriptomics.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
## Version Compatibility
Reference examples tested with: anndata 0.10+, 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 Proteomics Analysis
**"Analyze my CODEX/IMC spatial proteomics data"** → Process multiplexed imaging data including cell segmentation, protein phenotyping, spatial neighborhood analysis, and protein colocalization scoring.
- Python: `scimap.tl.phenotype_cells()`, `squidpy.gr.nhood_enrichment()`
## Data Loading
**Goal:** Process multiplexed spatial proteomics data (CODEX/IMC/MIBI) through cell phenotyping, spatial neighborhood analysis, and protein colocalization scoring.
**Approach:** Load the cell-by-marker intensity matrix with spatial coordinates into AnnData, normalize and rescale marker intensities, phenotype cells by marker expression gating, then analyze spatial neighborhoods and cell-cell interactions using scimap and squidpy.
```python
import scimap as sm
import anndata as ad
# Load CODEX/IMC data (cell x marker matrix with spatial coordinates)
adata = ad.read_h5ad('spatial_proteomics.h5ad')
# Required: spatial coordinates in adata.obsm['spatial']
# Required: protein intensities in adata.X
```
## Preprocessing
```python
# Log transform intensities
sm.pp.log1p(adata)
# Rescale markers (0-1 per marker)
sm.pp.rescale(adata)
# Combat batch correction if multiple FOVs
sm.pp.combat(adata, batch_key='fov')
```
## Phenotyping Cells
```python
# Manual gating approach
phenotype_markers = {
'T_cell': ['CD3', 'CD45'],
'B_cell': ['CD20', 'CD45'],
'Macrophage': ['CD68', 'CD163'],
'Tumor': ['panCK', 'Ki67']
}
sm.tl.phenotype_cells(adata, phenotype=phenotype_markers,
gate=0.5, label='phenotype')
# Clustering-based phenotyping
sm.tl.cluster(adata, method='leiden', resolution=1.0)
```
## Spatial Analysis
```python
# Build spatial neighbors graph
sm.tl.spatial_distance(adata, x_coordinate='X', y_coordinate='Y')
# Neighborhood enrichment
sm.tl.spatial_interaction(adata, phenotype='phenotype',
method='knn', knn=10)
# Spatial clustering (communities of cells)
sm.tl.spatial_cluster(adata, phenotype='phenotype')
```
## Visualization
```python
# Spatial scatter plot
sm.pl.spatial_scatterPlot(adata, colorBy='phenotype',
x='X', y='Y', s=5)
# Heatmap of spatial interactions
sm.pl.spatial_interaction(adata)
# Marker expression overlay
sm.pl.image_viewer(adata, markers=['CD3', 'CD20', 'panCK'])
```
## Integration with Transcriptomics
```python
import squidpy as sq
# If matched spatial transcriptomics available
# Transfer labels or integrate modalities
sq.gr.spatial_neighbors(adata_protein)
sq.gr.spatial_neighbors(adata_rna)
# Compare spatial patterns across modalities
```
## Platform-Specific Notes
| Platform | Markers | Resolution | Notes |
|----------|---------|------------|-------|
| CODEX | 40-60 | Subcellular | Cyclic staining |
| IMC | 40+ | 1 um | Metal-tagged antibodies |
| MIBI | 40+ | 260 nm | Mass spectrometry |
## Related Skills
- spatial-transcriptomics/spatial-neighbors - Spatial graph construction
- spatial-transcriptomics/spatial-domains - Domain identification
- imaging-mass-cytometry/phenotyping - IMC-specific analysisRelated Skills
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Map scRNA-seq atlases onto spatial transcriptomics slides using omicverse's Single2Spatial workflow for deep-forest training, spot-level assessment, and marker visualisation.
bio-spatial-transcriptomics-spatial-visualization
Visualize spatial transcriptomics data using Squidpy and Scanpy. Create tissue plots with gene expression, clusters, and annotations overlaid on histology images. Use when visualizing spatial expression patterns.
bio-spatial-transcriptomics-spatial-statistics
Compute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when computing spatial autocorrelation or co-occurrence statistics.
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.
bio-spatial-transcriptomics-spatial-neighbors
Build spatial neighbor graphs for spatial transcriptomics data using Squidpy. Compute k-nearest neighbors, Delaunay triangulation, and radius-based connectivity for downstream spatial analyses. Use when building spatial neighborhood graphs.
bio-spatial-transcriptomics-spatial-multiomics
Analyze high-resolution spatial platforms like Slide-seq, Stereo-seq, and Visium HD. Use when working with subcellular resolution or high-density spatial data.
bio-spatial-transcriptomics-spatial-domains
Identify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use when identifying tissue domains or spatial regions.
bio-spatial-transcriptomics-spatial-deconvolution
Estimate cell type composition in spatial transcriptomics spots using reference-based deconvolution. Use cell2location, RCTD, SPOTlight, or Tangram to infer cell type proportions from scRNA-seq references. Use when estimating cell type composition in spatial spots.