| 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 |
Spatial Proteomics Analysis
Data Loading
import scimap as sm
import anndata as ad
adata = ad.read_h5ad('spatial_proteomics.h5ad')
Preprocessing
sm.pp.log1p(adata)
sm.pp.rescale(adata)
sm.pp.combat(adata, batch_key='fov')
Phenotyping Cells
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')
sm.tl.cluster(adata, method='leiden', resolution=1.0)
Spatial Analysis
sm.tl.spatial_distance(adata, x_coordinate='X', y_coordinate='Y')
sm.tl.spatial_interaction(adata, phenotype='phenotype',
method='knn', knn=10)
sm.tl.spatial_cluster(adata, phenotype='phenotype')
Visualization
sm.pl.spatial_scatterPlot(adata, colorBy='phenotype',
x='X', y='Y', s=5)
sm.pl.spatial_interaction(adata)
sm.pl.image_viewer(adata, markers=['CD3', 'CD20', 'panCK'])
Integration with Transcriptomics
import squidpy as sq
sq.gr.spatial_neighbors(adata_protein)
sq.gr.spatial_neighbors(adata_rna)
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