Intercellar

InterCellar

Workflows

Standard Workflow

Launch the interactive Shiny application to analyze cell-cell communication from precomputed ligand-receptor interactions.

library(InterCellar)
InterCellar::run_app(reproducible = TRUE)

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)

References

• Homepage: https://bioconductor.org/packages/intercellar
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/intercellar/inst/doc/vignette.html