Claude-skill-registry cellcellcommunication

Infer ligand-receptor interactions and cell-cell communication networks from single-cell RNA-seq data using the LIANA+ framework. Identifies potential signaling events between cell types based on gene expression patterns and curated ligand-receptor interaction databases.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/cellcellcommunication" ~/.claude/skills/majiayu000-claude-skill-registry-cellcellcommunication && rm -rf "$T"

manifest: skills/data/cellcellcommunication/SKILL.md

CellCellCommunication Process Configuration

Purpose

When to Use

To identify ligand-receptor interactions between cell types
For systematic analysis of cell-cell communication networks
To understand cell crosstalk and signaling pathways
To compare communication patterns across biological conditions
To identify key signaling mediators in tissue microenvironments

Configuration Structure

Process Enablement

[CellCellCommunication]
cache = true

Input Specification

[CellCellCommunication.in]
sobjfile = ["path/to/seurat_object.rds"]  # Seurat (.rds, .h5seurat) or AnnData (.h5ad)

Environment Variables

[CellCellCommunication.envs]
# Method selection
method = "cellchat"  # Default inference method

# Cell type grouping
groupby = "ident"  # Column name for cell type labels (default: Seurat ident)

# Species selection
species = "human"  # "human" or "mouse"

# Filtering parameters
expr_prop = 0.1  # Minimum expression proportion (0.0-1.0)
min_cells = 5     # Minimum cells per cell type

# Statistical parameters
n_perms = 1000  # Permutations for permutation testing
seed = 1337      # Random seed for reproducibility

# Computational resources
ncores = 1  # Number of parallel cores

# Advanced options
subset = ""      # Expression to subset cells (e.g., "adata.obs.group == 'control'")
split_by = ""    # Column to run analysis separately and combine
assay = "RNA"   # Assay to use for Seurat objects

Available Inference Methods

LIANA+ provides multiple methods for cell-cell communication inference:

Method	Description	Magnitude Score	Specificity Score
`CellChat`	Mass-action-based communication probability	lr_means	cellchat_pvals
`CellPhoneDB`	Permutation-based significance	lr_means	cellphone_pvals
`Connectome`	Interaction-specific scoring	-	-
`log2FC`	Log-fold change based	-	-
`NATMI`	Network analysis	-	-
`SingleCellSignalR`	Database-driven scoring	-	-
`Rank_Aggregate`	Aggregates multiple methods	-	-
`Geometric_Mean`	Geometric mean scoring	-	-

Default method:

cellchat

(recommended for most analyses)

LIANA+ Resources

Species-Specific Resources

# Human (default)
species = "human"  # Uses 'consensus' resource (CellPhoneDB, CellChat, ICELLNET, etc.)

# Mouse
species = "mouse"  # Uses 'mouseconsensus' resource

Available Resources (override with

resource_name

)

```
consensus
```
(human default): Combines multiple curated resources
```
cellchatdb
```
: CellChat database interactions
```
cellphonedb
```
: CellPhoneDB interactions
```
mouseconsensus
```
(mouse default): Mouse-specific consensus

icellnet

connectomedb2020

ramilowski2015

lrdb

, and more

Configuration Examples

Minimal Configuration

[CellCellCommunication]
[CellCellCommunication.in]
sobjfile = ["path/to/seurat_object.rds"]

[CellCellCommunication.envs]
# Use defaults: method=cellchat, species=human, groupby=ident

Human PBMC Analysis

[CellCellCommunication]
[CellCellCommunication.in]
sobjfile = ["path/to/pbmc_seurat.rds"]

[CellCellCommunication.envs]
method = "cellchat"
species = "human"
groupby = "cell_type"  # Use annotated cell types
expr_prop = 0.1
min_cells = 10

Mouse Tissue Analysis

[CellCellCommunication]
[CellCellCommunication.in]
sobjfile = ["path/to/mouse_seurat.rds"]

[CellCellCommunication.envs]
species = "mouse"
method = "cellchat"
groupby = "seurat_clusters"
expr_prop = 0.15
min_cells = 8

Multi-Condition Comparison

[CellCellCommunication]
[CellCellCommunication.in]
sobjfile = ["path/to/combined_seurat.rds"]

[CellCellCommunication.envs]
split_by = "condition"  # Run separately per condition, then combine
method = "cellchat"
groupby = "cell_type"

Custom Cell Subset

[CellCellCommunication]
[CellCellCommunication.in]
sobjfile = ["path/to/seurat_object.rds"]

[CellCellCommunication.envs]
subset = "adata.obs.tissue == 'tumor'"
subset_using = "python"
method = "cellchat"

Common Patterns

Pattern 1: Full Interaction Network (CellChat)

[CellCellCommunication]
[CellCellCommunication.in]
sobjfile = ["intermediate/seuratclustering/SeuratClustering/sample.seurat.qs"]

[CellCellCommunication.envs]
method = "cellchat"
groupby = "ident"
expr_prop = 0.1
ncores = 4

Pattern 2: Disease vs Healthy Comparison

[CellCellCommunication]
[CellCellCommunication.in]
sobjfile = ["path/to/disease_vs_healthy.rds"]

[CellCellCommunication.envs]
split_by = "disease_status"
method = "cellchat"
groupby = "cell_type"
expr_prop = 0.1
min_cells = 10

Pattern 3: High-Stringency Analysis

[CellCellCommunication]
[CellCellCommunication.in]
sobjfile = ["path/to/seurat_object.rds"]

[CellCellCommunication.envs]
method = "cellchat"
expr_prop = 0.2  # Higher expression threshold
min_cells = 20    # More cells required

Dependencies

Upstream: SeuratClustering (required), CellTypeAnnotation (recommended for meaningful labels)
Downstream: CellCellCommunicationPlots (visualization: network, circos, heatmap, box plots)

Validation Rules

Species matching: Set
```
species = "human"
```
or
```
species = "mouse"
```
to match your organism
Cell type grouping:
```
groupby
```
column must exist in metadata; use CellTypeAnnotation or SeuratClustering results
Expression thresholds:
```
expr_prop
```
between 0.0-1.0; recommended 0.1 for human, 0.15 for mouse
Cell type resolution:
```
min_cells
```
minimum cells per type; recommended 5-10 cells per type

Troubleshooting

No Interactions Found

Solutions: Lower

expr_prop

(e.g., 0.1→0.05), reduce

min_cells

, check

groupby

column, verify

species

parameter

Species Mismatch Error

Solutions: Verify

species

matches organism, ensure gene symbols in correct format (human: uppercase, mouse: title case)

Slow Execution

Solutions: Increase

ncores

, reduce

n_perms

for permutation methods, use faster

cellchat

method

Memory Issues

Solutions: Reduce

ncores

, use

subset

to analyze specific cell types, merge rare cell types

Unexpected Cell Type Pairings