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)