Data & ReproGIS & Remote SensingFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-imaging-mass-cytometry-spatial-analysis

Maintainer FreedomIntelligence · Last updated April 1, 2026

Spatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying cell-cell interaction patterns in imaging mass cytometry data.

OpenClawNanoClawAnalysisReproductionbio-imaging-mass-cytometry-spatial-analysis🧬 bioinformatics (gptomics bio-* suite)bioinformatics — immunoinformatics & flow cytometryspatial

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-imaging-mass-cytometry-spatial-analysis

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

Python: squidpy.gr.spatial_neighbors(), squidpy.gr.nhood_enrichment().
Analyze spatial cell interactions in my IMC data" → Build spatial neighborhood graphs, test for cell-cell interaction enrichment, and identify spatial domains from multiplexed imaging data. Python: squidpy.gr.spatial_neighbors(), squidpy.gr.nhood_enrichment().
adata = ad.read_h5ad('imc_phenotyped.h5ad').

Source Doc

Excerpt From SKILL.md

Build Spatial Graph

import squidpy as sq
import anndata as ad

## Build spatial neighbor graph

sq.gr.spatial_neighbors(adata, coord_type='generic', delaunay=True)

## Or by distance

sq.gr.spatial_neighbors(adata, coord_type='generic', radius=50)  # 50 pixels

print(f'Built graph with {adata.obsp["spatial_connectivities"].nnz} edges')

Use cases

Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying cell-cell interaction patterns in imaging mass cytometry data.