bio-spatial-transcriptomics-spatial-proteomics

name	bio-spatial-transcriptomics-spatial-proteomics
description	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.
tool_type	python
primary_tool	scimap

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.

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

# 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

# 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

# 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

# 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

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 analysis