SCARR-Vis

SCARR-Vis is an R/Shiny application for interactive assessment and correction of ambient RNA contamination in single-cell and single nucleus RNA-seq data. The interface follows a typical workflow: upload 10x matrices, choose a decontamination method, inspect pre- and post-correction QC, and explore clustering and gene expression.

Features

Estimation Method and Parameters

1. SoupX: models background 'soup' RNA and adjusts counts.
2. DecontX: from celda infers cell-specific contamination fractions.
3. scCDC: identifies contamination-causing genes (GCGs) and optionally corrects them.
4. FastCAR: profiles ambient RNA using empty droplets and estimates per-cell contamination.

Use SCARR-Vis Online

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

Launch SCARR-Vis Locally

Prerequisites

Ensure the following software is installed:

• R (>= 4.6.1): Download R
• RStudio (>= 2026.07.1): Download RStudio
• Bioconductor (>= 3.23)
• Shiny (>= 1.14.0)

Note: SCARR-Vis 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 packagesinstall_cran_packages<-function(packages) {
for (pkginpackages) {
if (!require(pkg, character.only=TRUE)) {
install.packages(pkg, dependencies=TRUE)
}
}
}
# Function to check and install Bioconductor packagesinstall_bioc_packages<-function(packages) {
for (pkginpackages) {
if (!require(pkg, character.only=TRUE)) {
BiocManager::install(pkg, update=FALSE, dependencies=TRUE)
}
}
}
# Function to check and install GitHub packagesinstall_github_packages<-function(packages) {
for (pkgin names(packages)) {
if (!require(pkg, character.only=TRUE)) {
devtools::install_github(packages[[pkg]], upgrade="never", dependencies=TRUE)
}
}
}
# List of CRAN packagescran_packages<- c("shiny", "shinythemes", "shinyWidgets", "shinycssloaders", "shinydashboard", "shinyjs", "DT", "ggplot2", "devtools", "Matrix", "reshape2", "zip", "tools", "grid", "filelock", "Seurat", BiocManager)
# List of Bioconductor packagesbioc_packages<- c("biomaRt", "rhdf5", "celda", "decontX", "SingleCellExperiment", "SummarizedExperiment")
# List of GitHub packagesgithub_packages<-list(
"patchwork"="thomasp85/patchwork",
"SoupX"="constantAmateur/SoupX",
"scCDC"="ZJU-UoE-CCW-LAB/scCDC",
"FastCAR"="https://git.web.rug.nl/P278949/FastCAR"
)
# Install all packages
install_cran_packages(cran_packages)
install_bioc_packages(bioc_packages)
install_github_packages(github_packages)

Start the App

To launch SCARR-Vis, 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('SCARR-Vis', 'GudaLab')

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

Running the app

RNA-DAVis is a standard three-file Shiny app (global.R, ui.R, server.R).

In RStudio

1. Open the RNA-DAVis folder.
2. Open ui.R or server.R or global.R.
3. Click the ▶ Run App button in the top-right of the editor.

From the R console

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

Replace /path/to/the/SCARR-Vis 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/SCARR-Vis

Example Datasets

To ensure seamless analysis and reproducibility, SCARR-Vis includes one reference dataset GSM7681687. 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.

These datasets are ideal for:

• Demonstrating SCARR-Vis functionalities.
• Familiarizing users with the tool's analysis workflow.
• Testing the application.

Tested Platforms

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