bgpt-paper-search
BGPT is a remote MCP server that searches a curated database of scientific papers built from raw experimental data extracted from full-text…
Maintainer FreedomIntelligence · Last updated April 1, 2026
Analyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Infer intercellular signaling, identify communication pathways, and visualize interaction networks. Use when analyzing cell-cell communication in spatial context.
Original source
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-spatial-transcriptomics-spatial-communication
Skill Snapshot
Source Doc
Goal: Identify significant ligand-receptor interactions between spatially proximal cell types.
Approach: Build a spatial neighbor graph, then run permutation-based ligand-receptor analysis using Squidpy's built-in database.
"Find cell-cell communication in my spatial data" -> Test ligand-receptor co-expression between neighboring cell types with permutation-based significance.
## Build spatial neighbors if not already done
sq.gr.spatial_neighbors(adata, coord_type='generic', n_neighs=6)
## Run ligand-receptor analysis
sq.gr.ligrec(
adata,
cluster_key='cell_type', # Column with cell type annotations
n_perms=100, # Permutations for significance testing
threshold=0.01, # P-value threshold
copy=False,
)
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