ScRDAVis: An R shiny application for single-cell transcriptome data analysis and visualization
Sankarasubramanian Jagadesan, Chittibabu Guda
Abstract
Single-cell RNA sequencing (scRNA-seq) technology has revolutionized biological research by enabling a through exploration of cellular heterogeneity. However, the complexity of data processing pipelines and the need for programming expertise create barriers for many biologists to explore scRNA-seq data.
Introduction
Single-cell RNA sequencing has revolutionized our ability to analyze gene expression at a single cell resolution and opened up new opportunities to study biology using a different lens and gain insights into the cellular heterogeneity, developmental trajectories, and the healthy and disease states of single cells [1,2].
Materials and method
ScRDAVis is developed in R and utilizes the Shiny framework for interactive functionality. The tool requires R (version 4.5.1 or later), RStudio (version 2025.05.1 or later), Bioconductor (version 3.21 or later), and Shiny (version 1.11.1 or later) for its optimal performance. ScRDAVis can be accessed via our web server or installed on a local desktop from GitHub. To launch the R Shiny graphical interface on a desktop, users can run the following command in R: shiny::runGitHub(“ScRDAVis”, “gudalab”).
Results
ScRDAVis comprises nine comprehensive modules that facilitate various aspects of scRNA-seq analysis: 1. Single or Multiple Samples Analysis, 2. Subclustering, 3. Correlation Network Analysis, 4. Gene Ontology (GO) Analysis, 5. Pathway Analysis, 6. GSEA Analysis, 7. Cell-Cell Communication Analysis, 8. Trajectory and Pseudotime Analysis, 9. Co-Expression and TF Analysis.
Discussion
ScRDAVis is a comprehensive and user-friendly toolkit designed to streamline scRNA-seq analysis workflows specifically designed for biologists with little or no programming knowledge. It incorporates a suite of nine seamlessly connected modules that enable researchers to address key challenges with the current tools used for analyzing single-cell transcriptomic datasets.
Acknowledgments
Authors would like to thank the Bioinformatics and Systems Biology Core (BSBC) facility at UNMC for providing the computational infrastructure and support.
Citation: Jagadesan S, Guda C (2025) ScRDAVis: An R shiny application for single-cell transcriptome data analysis and visualization. PLoS Comput Biol 21(11): e1013721. https://doi.org/10.1371/journal.pcbi.1013721
Editor: Jae Kyoung Kim, Korea Advanced Institute of Science and Technology, KOREA, REPUBLIC OF
Received: May 8, 2025; Accepted: November 7, 2025; Published: November 13, 2025
Copyright: © 2025 Jagadesan, Guda. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The standalone application code can be downloaded at https://github.com/GudaLab/ScRDAVis. The online version of ScRDAVis is accessible at https://www.gudalab-rtools.net/ScRDAVis.
Funding: This work was supported by the National Institutes of Health (2P20GM103427, 5P30CA036727) to CG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
