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

conda (recommended)

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')