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Carnation

Deeply explore your bulk RNA-Seq data with interactive visualizations

Carnation is an interactive Shiny dashboard that transforms complex bulk RNA-Seq data into beautiful, insightful visualizations. Designed for both computational and experimental biologists, Carnation makes exploring differential expression analysis, functional enrichment, and pattern analysis intuitive and exciting.

✨ Key Features

  • Interactive Visualizations: Explore your data through multiple perspectives
    • 📊 PCA plots - Visualize sample relationships with gene loadings overlay
    • 🔥 Heatmaps - Examine expression patterns across samples and conditions
    • 📈 MA plots - Identify differentially expressed genes
    • 🧩 UpSet plots - Discover overlapping gene sets across comparisons
    • 📉 Gene plots - Create customizable expression visualizations
  • Functional Enrichment Analysis: Understand the biological significance of your results
    • 🌐 Network plots - Visualize gene-term relationships
    • 📊 Summary overviews - Compare enrichment across conditions
    • 🔍 Fuzzy search - Find relevant pathways across genes or descriptions
  • Pattern Analysis: Identify co-regulated gene clusters across conditions
  • Gene Tracking: Keep your genes of interest at your fingertips with the “Gene scratchpad”
  • Flexible Deployment: Run locally for personal analysis or on a server to share with collaborators
  • User Management: Optional authentication system for controlled access in multi-user environments

🚀 Installation

The easiest way to get started with Carnation is through conda, which handles all dependencies automatically:

# Create environment outside the carnation directory
cd .. && conda env create -p env --file carnation/requirements-pinned.yaml
conda activate ./env
R

Then install the package with the remotes package. Note, here we set upgrade='never' to make sure the conda-installed package versions remain unchanged.

remotes::install_github('NICHD-BSPC/carnation', upgrade='never')

remotes

Alternatively, you can install directly with remotes:

install.packages('remotes')
setRepositories(ind=c(1,2,3,4,5))  # Get both CRAN and Bioconductor packages
remotes::install_github('NICHD-BSPC/carnation')

🏁 Getting Started

Data Organization

Organize your data in a directory structure that Carnation can easily navigate:

/carnation/data/
  ├─ project1
  │  ├─ main.rds
  │  ├─ subset.rds
  │  └─ main-nooutlier.rds
  │
  └─ project2
     └─ default.rds

First Run

Load the library and install required Python dependencies:

library(carnation)
install_carnation()  # Installs plotly and kaleido for PDF export
run_carnation()

For remote servers with SSH port forwarding:

run_carnation(options=list(port=12345, launch.browser=FALSE))

Then access Carnation at http://127.0.0.1:12345

🔍 Exploring Your Data

Once your data is loaded, Carnation offers multiple ways to explore:

  • DE Analysis: Analyze differential expression through multiple visualizations
    • Summary: Get a quick overview of your differential expression results
    • Metadata: Explore sample metadata and experimental design
    • PCA Plot: Visualize sample relationships with gene loadings overlay
    • MA Plot: Identify differentially expressed genes with statistical significance
    • Gene Plot: Create customizable expression visualizations for genes of interest
    • UpSet Plot: Discover overlapping gene sets across multiple comparisons
    • Heatmap: Examine expression patterns across samples and conditions
  • Functional Enrichment: Understand the biological significance of your results
    • Table: Interactive tables with powerful search capabilities
    • Plots: Seven different visualizations including network plots and dendrograms
    • Compare Results: Directly compare enrichment results between conditions
  • Pattern Analysis: Identify co-regulated gene clusters across conditions
    • Plot: Visualize expression patterns of gene clusters
    • Cluster Membership: Explore which genes belong to which clusters
  • Gene Scratchpad: Track genes of interest across all visualizations

📚 Documentation

Each module includes comprehensive help documentation accessible through the help buttons throughout the interface. The documentation provides detailed explanations of plot options, statistical methods, and interpretation guidelines.

🤝 Contributing

We welcome contributions to Carnation! Please feel free to submit issues or pull requests to the GitHub repository.

📄 License

Carnation is available under the MIT license.

💻 Server Mode

Carnation supports multi-user environments with authentication:

# Create user database
credentials <- data.frame(
  user = c('shinymanager'),
  password = c('12345'),
  admin = c(TRUE),
  stringsAsFactors = FALSE
)

# Initialize the database
shinymanager::create_db(
  credentials_data = credentials,
  sqlite_path = 'credentials.sqlite',
  passphrase = 'admin_passphrase'
)

# Run with authentication
run_carnation(credentials='credentials.sqlite', passphrase='admin_passphrase')