cellchat-cell-communication
Infer and visualize intercellular communication from scRNA-seq with CellChat (R). Build CellChat from Seurat/counts → subset CellChatDB ligand-receptor pairs → over-expressed genes per group → communication probabilities → pathway signaling → network centrality (senders/receivers/influencers) → chord/heatmap/bubble plots → cross-condition compare. Human, mouse. Use liana for pure-Python.
npx skills add BioTender-max/awesome-bio-agent-skills --skill cellchat-cell-communication --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Instructs the agent to build and analyze a CellChat object from scRNA-seq data, set the appropriate ligand-receptor database (human or mouse), subset to desired signaling categories, identify over-expressed genes and interactions, compute communication probabilities between cell groups, aggregate to pathways, analyze network centrality (senders, receivers, influencers), and generate visualizations (chord diagrams, heatmaps, bubble plots). It also covers cross-condition comparison by merging two CellChat objects and performing differential analyses.
How it works
- Create a CellChat object from a Seurat object or a normalized counts matrix, supplying a meta data frame mapping labels to cell groups.
- Load CellChatDB for the species and optionally subset to a signaling category (e.g., Secreted Signaling); subsetData to keep only relevant genes.
- Identify over-expressed genes per cell group and map to ligand-receptor pairs in the database; inspect inferred interactions.
- Compute pairwise communication probabilities between ordered cell-group pairs using computeCommunProb with specified type (default "triMean"), applying population weighting and bootstrapping; filter by min.cells.
- Compute pathway-level communication with computeCommunProbPathway and aggregateNet; view significant pathways.
- Analyze network centrality via netAnalysis_computeCentrality and visualize with netAnalysis_signalingRole_heatmap; identify dominant patterns with identifyCommunicationPatterns.
- Visualize results using netVisual_circle, netVisual_heatmap, netVisual_aggregate (pathway-specific), and netVisual_bubble; export figures as needed.
- For two conditions, mergeCellChat and compareInteractions across groupings, generate differential visuals for gained/lost connections, and rankNet for pathway shifts.
When to use it
- When characterizing dominant senders/receivers of signals across cell types.
- When comparing signaling networks between two conditions (e.g., healthy vs diseased).
- When prioritizing signaling pathways for perturbation experiments.
- When preferring a pure-Python workflow via liana for consensus rankings across ligand-receptor databases.
What it can touch
- Data inputs: normalized scRNA-seq count matrices and metadata for cell groups.
- Requires: CellChat (R), Seurat (R), and optionally liana for Python-based workflows.
Caveats
- Dependencies include R packages like CellChat, Seurat, NMF, ggplot2, igraph, dplyr, patchwork, circlize, RColorBrewer; ensure compatibility with the target environment.
- The workflow expects human or mouse CellChatDB data and may require species-specific setup.
- Results depend on statistical defaults (e.g., bootstrapping iterations, min.cells) that can affect sensitivity.
# CellChat — Cell-Cell Communication Analysis ## Overview CellChat is an R package that infers and visualizes intercellular signaling networks from single-cell RNA-seq data. Starting from a normalized expression matrix and cluster labels, CellChat identifies ligand-receptor interactions supported by CellChatDB — a manually curated database of over 2,000 validated ligand-receptor pairs in human and mouse. Communication probability is modeled using the law of mass action, combining expression levels of ligands, receptors, and cofactors. CellChat aggregates pair-level probabilities into pathway-level signaling networks and quantifies each cell group's role as a signal sender, receiver, mediator, or influencer. The result is a rich, interpretable picture of which cell types talk to which, through which signaling pathways, and how these patterns change between conditions. ## When to Use - Characterizing which cell types are the dominant senders or receivers of paracrine and autocrine signals in a tissue atlas or disease sample - Identifying specific ligand-receptor pairs mediating communication between a cell population of interest (e.g., tumor cells → T cells, fibroblasts → epithelial
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Step 1: Create CellChat Object
- Step 2: Set CellChatDB and Subset Interactions
- Step 3: Identify Over-Expressed Genes and Interactions
- Step 4: Infer Cell-Cell Communication Probabilities
- Step 5: Compute Pathway-Level Communication
- Step 6: Analyze Network Centrality — Senders, Receivers, Influencers
- Step 7: Visualize — Chord Diagrams, Heatmaps, Bubble Plots
- Step 8: Compare Two CellChat Objects Across Conditions
- Key Parameters
What does the cellchat-cell-communication skill do?
Infer and visualize intercellular communication from scRNA-seq with CellChat (R). Build CellChat from Seurat/counts → subset CellChatDB ligand-receptor pairs → over-expressed genes per group → communication probabilities → pathway signaling → network centrality (senders/receivers/influencers) → chord/heatmap/bubble plots → cross-condition compare. Human, mouse. Use liana for pure-Python.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill cellchat-cell-communication --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.