bio-spatial-transcriptomics-spatial-proteomics

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

## Combat batch correction if multiple FOVs

sm.pp.combat(adata, batch_key='fov')

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')