Start a fresh conversation. Replace the highlighted placeholders.
Use bioconductor-intercellar to launch a reproducible interactive local analysis of [precomputed ligand-receptor interaction table] and save its figures and tables to [existing local output folder].
Required inputs
[precomputed ligand-receptor interaction table]
[existing local output folder]
User-approved starter prompt based on a pinned source version; check current skill requirements before running. Task evidence ↗
SKILL.md
name: bioconductor-intercellardescription: InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell commwhen_to_use: Use when: InterCellar User Guide; Single-cell RNA-seq analysis (InterCellar). Not for: Requires R ≥ 4.1 and Bioconductor ≥ 3.16user-invocable: false
InterCellar
Workflows
Standard Workflow
Launch the interactive Shiny application to analyze cell-cell communication from precomputed ligand-receptor interactions.
Input: Precomputed ligand-receptor interaction tables (e.g., from CellPhoneDB, CellChat, ICELLNET, or SingleCellSignalR). Output: Interactive Shiny interface in a web browser for filtering, annotation, and visualization.
When to Use
To interactively analyze and visualize cell-cell communication (CCI) from single-cell RNA-seq data using precomputed ligand-receptor interactions.
To perform functional annotation of interaction pairs using Gene Ontology or pathway databases (via graphite or biomaRt).
To compare cell-cell communication across multiple conditions (up to 3) using cluster-based, gene-based, or function-based approaches.
When NOT to Use
For performing the initial prediction of ligand-receptor interactions from raw scRNA-seq counts; use tools like CellChat, CellPhoneDB, or SingleCellSignalR instead.
For non-interactive, high-throughput command-line pipeline execution of cell-cell communication analysis; use CellChat or scran directly in R.
Data Requirements
Precomputed cell-cell interaction (CCI) results from supported tools (CellPhoneDB, CellChat, ICELLNET, SingleCellSignalR) or custom tables containing interaction pairs and scores.
Output folder path on the local drive where InterCellar can save figures and tables.
Key Parameters
reproducible (TRUE): Flag in run_app() to ensure results are reproducible across R sessions.
Best Practices
Run the app with reproducible = TRUE to ensure analysis consistency across sessions.
Specify an existing local folder for saving output tables and figures before uploading data.
Ensure cluster names are consistent across compared conditions when performing multiple-condition analysis.
Common Pitfalls
App does not open automatically: If the browser does not launch, manually copy and navigate to the local address (e.g., http://127.0.0.1:6134) shown in the R console.
Inconsistent cluster names in multiple conditions: Comparing conditions with different cluster compositions can lead to misleading radar or bar plots; ensure cluster names are identical or highly similar.
Global filtering effects: Applying strict p-value or interaction score thresholds in the Cluster-verse will globally subset the dataset and affect downstream Gene-verse and Function-verse analyses.
Alternatives
CellChat for R-based mechanistic modeling and comparison of cell-cell communication.
SingleCellSignalR for simple ligand-receptor interaction predictions and network construction.
scater / scran for general single-cell RNA-seq preprocessing and clustering.
Citations
Efremova et al. 2020, Nature Protocols (CellPhoneDBv2)
Jin et al. 2021, Nature Communications (CellChat)
Chua et al. 2020, Nature Biotechnology (COVID-19 dataset)