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CellCommunicationEngine: CellChat-Style LR Interaction Scoring, Pathway Activity, and Intercellular Signaling Network Analysis
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Cell-cell communication through ligand-receptor interactions coordinates tissue homeostasis, immune responses, and development. We present CellCommunicationEngine, a pure-Python pipeline for intercellular communication analysis. The engine implements CellChat-style interaction strength scoring (expression product of ligand in sender × receptor in receiver), signaling pathway activity aggregation, differential communication analysis, NicheNet-style ligand activity scoring, and communication network visualization. Applied to 8 cell types × 200 LR pairs, the pipeline identifies total interaction strength=126.01, top sender=NK_cell, top receiver=Endothelial, top pathway=WNT, and top LR pair strength=436.30.
Introduction
Cell-cell communication through ligand-receptor (LR) interactions is fundamental to multicellular biology. CellChat models communication probability as the product of ligand and receptor expression. NicheNet predicts which ligands most influence target gene expression in receiver cells.
Methods
Interaction Strength
P(L→R) = mean_expr(L in sender) × mean_expr(R in receiver) × evidence_weight.
Pathway Activity
Pathway score = sum of interaction strengths for all LR pairs in pathway.
Ligand Activity
NicheNet: Pearson correlation between ligand-regulated gene set and observed DE genes in receiver.
Results
Total interaction=126.01. Top sender=NK_cell. Top pathway=WNT. Top LR=436.30.
Code Availability
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