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Cascade

Unleash the power of your single-cell data

Cascade is an interactive Shiny dashboard designed for exploring and analyzing single-cell RNA sequencing (scRNA-Seq) and spatial transcriptomics data. It provides a user-friendly interface for researchers to visualize and interpret complex single-cell genomics data.

General workflow

  • Load a pre-processed single-cell or spatial dataset and (optional) marker gene tables
  • Explore cell clusters in reduced dimensional space
  • Visualize expression patterns of genes of interest
  • Compare expression across different conditions or cell types
  • Filter and subset data for focused analysis

Key Features

  1. Data Support
  • Compatible with Seurat, SingleCellExperiment (.Rds files) and AnnData objects (.h5ad files) from scanpy
  • Handles both single-cell RNA-Seq and spatial transcriptomics data (including Visium and Xenium)
  1. User Interface
  • Clean, modern interface with a tabbed layout for different analysis views
  • Interactive tour functionality to guide new users
  • Optional user authentication system
  • Consistent help documentation for all modules
  1. Main Analysis Modules:
  • Summary: Provides an overview of the dataset with basic statistics
  • Cell Embeddings:
    • Visualizes cells in reduced dimensional space (UMAP, t-SNE, etc.)
    • Interactive plots with split view and selection capabilities
    • Spatial visualization for spatial transcriptomics data
  • Metadata Viewer:
    • Displays and explores cell metadata and clustering information
    • Summarizes cluster characteristics
    • Visualizes metadata distributions with various plot types
  • Cluster Tree:
    • Visualizes hierarchical relationships between cell clusters
    • Three visualization modes:
      • Single: Hierarchical tree for a single clustering
      • Compare resolutions (Tree): Tree diagram showing relationships between clusters at different resolutions
      • Compare resolutions (Overlay): Overlay of cluster relationships on dimension reduction plots
  • Cell Markers:
    • Three types of marker tables:
      • Cluster Markers: Genes that define each cluster (e.g. from FindAllMarkers (Seurat) or rank_genes_groups (scanpy).
      • Conserved Markers: Genes conserved across groups (from Seurat::FindConservedMarkers)
      • DE Markers: Differentially expressed genes between conditions (from Seurat::FindMarkers or pseudo-bulk analysis from DESeq2)
    • Interactive tables with filtering and selection capabilities
    • Integration with gene scratchpad for cross-module analysis
  • Marker Plots:
    • Multiple visualization options for gene expression:
      • Violin plots
      • Dot plots
      • Feature plots (on UMAP or spatial coordinates)
      • Co-expression plots (on UMAP or spatial coordinates)
      • Scatter plots
    • Download functionality for all plots
  • Settings:
    • Configure data directories
    • Manage user access (if authentication is enabled)
  1. Interactive Features:
  • Point selection in UMAP/spatial cell-embeddings or marker plots
  • Gene selection from marker tables
  • Filtering capabilities for cells based on metadata, gene expression or lasso selection
  • Customizable plot parameters (colors, sizes, opacity, etc.)
  • Download options for plots and data
  • Gene scratchpad for tracking genes of interest across modules

Installation

CascadeR can be installed using BiocManager::install. First, start R (version: 4.6) and then run:

# first check to see if BiocManager is available
if(!requireNamespace('BiocManager', quietly=TRUE)){
  install.packages('BiocManager')
}

BiocManager::install('cascadeR')

To install the ‘devel’ version:

BiocManager::install('cascadeR', version='devel')

conda

An alternative way to get started with cascadeR is through conda, which handles all dependencies automatically:

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

Then install the package with the remotes package. Here we set upgrade=‘never’ to make sure the conda-installed package versions remain unchanged.

remotes::install_github('NICHD-BSPC/cascadeR@r4.5', upgrade='never')

Note:

Conda packages for R >= 4.6.0 may not be available yet causing installation using the default github branch to fail. To avoid this, use branch r4.3 which pins R to a lower version.

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

Getting Started

Data Organization

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

/cascade/data/
  ├─ project1
  │  ├─ seurat
  │  │  ├─ clustered.Rds
  │  │  └─ allmarkers.tsv
  │  │
  │  └─ scanpy
  │     ├─ clustered.h5ad
  │     └─ allmarkers.tsv
  │
  └─ project2
     └─ seurat5
        └─ clustered.Rds

Launch Cascade

First, load the library and install required Python dependencies:

library(cascadeR)
install_cascade()  # Installs plotly and kaleido for interactive plots
run_cascade()      # Launch the app!

The first time you run Cascade, you’ll be prompted to choose a data directory. Point it to your data location (e.g., /cascade/data), and you’re ready to explore!

Remote Access

Running Cascade on a remote server? No problem:

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

Then access the app at http://127.0.0.1:12345 through your SSH tunnel.

Documentation

Each module includes comprehensive help documentation accessible through the help buttons throughout the interface.

Contributing

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

License

Cascade is available under the MIT license.