| name | bioconductor-intercellar |
| description | 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 comm |
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
