ScRDAVis

ScRDAVis is a browser-based, user-friendly R Shiny application designed for researchers without programming expertise to analyze and visualize single-cell RNA (scRNA) data. It supports single and multiple sample analyses as well as group comparisons, offering a range of functionalities for comprehensive data exploration.

Features

ScRDAVis includes nine functional modules:

1. Single or Multiple Samples Analysis
   • Stats
   • Sample Groups and QC Filtering
   • Normalization and PCA Analysis
   • Clustering
   • Remove Doublets
   • Marker Identification
   • Cell Type Prediction
   • Cluster-Based Plots
   • Condition-Based Analysis
2. Subclustering
3. Correlation Network Analysis
4. Genome Ontology (GO) Terms
5. Pathway Analysis
6. GSEA Analysis
7. Cell-Cell Communication
8. Trajectory and Pseudotime Analysis
9. Co-Expression and TF Analysis
   • Co-Expression Network Analysis
   • Transcription Factor Regulatory Network Analysis

Use ScRDAVis Online

ScRDAVis is deployed online and accessible at:
https://www.gudalab-rtools.net/ScRDAVis

Launch ScRDAVis Locally

Prerequisites

Ensure the following software is installed:

• R (>= 4.4.3): Download R
• RStudio (>= 2024.12.0): Download RStudio
• Bioconductor (>= 3.20)
• Shiny (>= 1.10.0)

Note: ScRDAVis has been tested with these versions. Using older versions of R may cause errors during package installation. Updating to the latest R version is recommended.

Installation

Run the following commands in an R session to install required packages:

if (!require("BiocManager")) install.packages("BiocManager", update = FALSE)
if (!require("devtools")) install.packages("devtools", update = FALSE)

# Function to check and install CRAN packages
install_cran_packages <- function(packages) {
  for (pkg in packages) {
    if (!require(pkg, character.only = TRUE)) {
      install.packages(pkg, dependencies = TRUE)
    }
  }
}

# Function to check and install Bioconductor packages
install_bioc_packages <- function(packages) {
  for (pkg in packages) {
    if (!require(pkg, character.only = TRUE)) {
      BiocManager::install(pkg, update = FALSE, dependencies = TRUE)
    }
  }
}

# Function to check and install GitHub packages
install_github_packages <- function(packages) {
  for (pkg in names(packages)) {
    if (!require(pkg, character.only = TRUE)) {
      devtools::install_github(packages[[pkg]], upgrade = "never", dependencies = TRUE)
    }
  }
}

# List of CRAN packages
cran_packages <- c("shiny", "DT", "shinythemes", "shinyjs", "shinyFiles", "shinyWidgets", "shinycssloaders", "ggplot2", "data.table", "ggpubr", "shinydashboard", "dplyr", "tibble", "HGNChelper", "openai", "metap", "harmony", "ggrepel", "R.utils", "circlize", "hdf5r", "ggupset", "gridExtra", "ggalluvial", "NMF", "ggraph", "igraph", "cowplot", "pdftools", "xgboost", "msigdbr")

if (!require("msigdbdf"))install.packages("msigdbdf", repos = "https://igordot.r-universe.dev")

# List of Bioconductor packages
bioc_packages <- c("Seurat", "SeuratObject", "sctransform", "celldex", "SingleR", "scRNAseq", "glmGamPoi", "scran", "EnhancedVolcano", "ComplexHeatmap", "clusterProfiler", "org.Hs.eg.db", "org.Mm.eg.db", "org.Mmu.eg.db", "org.Rn.eg.db", "org.Ss.eg.db", "ReactomePA", "fgsea", "enrichplot", "multtest", "WGCNA", "hdWGCNA", "motifmatchr", "TFBSTools", "GenomicRanges", "JASPAR2020", "JASPAR2024", "EnsDb.Hsapiens.v86", "BSgenome.Hsapiens.UCSC.hg38", "BSgenome.Mmusculus.UCSC.mm10")

# List of GitHub packages
github_packages <- list(
  "DoubletFinder" = "chris-mcginnis-ucsf/DoubletFinder",
  "GPTCelltype" = "Winnie09/GPTCelltype",
  "openxlsx" = "ycphs/openxlsx",
  "presto" = "immunogenomics/presto",
  "monocle3" = "cole-trapnell-lab/monocle3",
  "SeuratWrappers" = "satijalab/seurat-wrappers",
  "SeuratDisk" = "mojaveazure/seurat-disk",
  "patchwork" = "thomasp85/patchwork",
  "CellChat" = "jinworks/CellChat",
  "genesorteR" = "mahmoudibrahim/genesorteR",
  "enrichR" = "wjawaid/enrichR",
  "hdWGCNA" = "smorabit/hdWGCNA"
)

# Install all packages
install_cran_packages(cran_packages)
install_bioc_packages(bioc_packages)
install_github_packages(github_packages)

Start the App

To launch ScRDAVis, follow one of these methods:

Option 1: Run Directly from GitHub

1. Open an R session in RStudio.
2. Execute the following lines of code:

library(shiny)
shiny::runGitHub('ScRDAVis', 'GudaLab')

Option 2: Download the source code from GitHub and run:

library(shiny)
runApp('/path/to/the/ScRDAVis-master', launch.browser = TRUE)

Replace /path/to/the/ScRDAVis-master with the actual path to the downloaded folder

Usage

A detailed user manual is available under the "Manual" tab at: https://www.gudalab-rtools.net/ScRDAVis

Example Datasets

To ensure seamless analysis and reproducibility, ScRDAVis includes one reference dataset for each input format. These datasets, sourced from NCBI, have been pre-tested with the tool and allow users to explore its functionalities and understand the analysis workflow effectively.

Available Datasets:

• H5 File: GSE271107
• Matrix Files: GSE277476
• Seurat Object: GSE250488
• Matrix Count File: GSE155953

These datasets are ideal for:

• Demonstrating ScRDAVis functionalities.
• Familiarizing users with the tool's analysis workflow.
• Testing the application in different input formats.

Users can download these datasets directly from the provided links and use them to explore ScRDAVis.

Tested Platforms

This application was tested on: Linux (Red Hat and Ubuntu) and Windows (10 and 11)