bio-spatial-transcriptomics-spatial-proteomics

To analyze CODEX spatial proteomics data, load per-cell marker intensities and coordinates into AnnData, normalize the markers, and apply gating or clustering to identify cell phenotypes.

Yes, you can integrate spatial proteomics with transcriptomics when matched data exist, enabling cross-modality comparison and visualization to reveal comprehensive spatial relationships.

You quantify cell-cell interactions by building spatial neighborhood graphs from cell coordinates, allowing you to analyze spatial domains and cellular relationships across multiple FOVs.

Yes, the workflow supports IMC and MIBI multiplexed imaging datasets, processing per-cell marker intensities and coordinates to generate spatial neighborhoods and phenotypes.

The spatial proteomics workflow uses Python-based libraries such as scimap and squidpy to perform data loading, preprocessing, phenotyping, spatial analysis, and visualization.

The best way to map spatial neighborhoods is to load multiplexed imaging data into AnnData, phenotype the cells, and build spatial neighborhood graphs to quantify cell-cell interactions.