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ScRDAVis: An R shiny application for single-cell transcriptome data analysis and visualization
Sankarasubramanian Jagadesan1, Chittibabu Guda1,2
1Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, Nebraska, United States of America.
Plos Computational Biology
|November 13, 2025
Summary
ScRDAVis is a new R Shiny application that simplifies single-cell RNA sequencing (scRNA-seq) data analysis for biologists. This user-friendly tool offers advanced features without requiring programming knowledge, democratizing scRNA-seq data exploration.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Single-cell RNA sequencing (scRNA-seq) provides deep insights into cellular heterogeneity.
- Data processing complexity and programming requirements create barriers for biologists using scRNA-seq data.
Purpose of the Study:
- To develop an accessible, interactive, browser-based R Shiny application for scRNA-seq data analysis.
- To empower biologists with no programming expertise to perform comprehensive scRNA-seq analyses.
Main Methods:
- Developed ScRDAVis, an R Shiny application integrating Seurat, CellChat, Monocle3, clusterProfiler, and hdWGCNA.
- Implemented user-friendly interface for single-sample, multiple-sample, and group-based analyses.
- Included functionalities for marker discovery, cell type annotation, subclustering, cell-cell communication, trajectory inference, pathway enrichment, WGCNA, and TF regulatory network analysis.
Main Results:
- ScRDAVis offers a GUI-based platform for scRNA-seq analysis, including novel hdWGCNA integration.
- The application supports advanced functional studies and publication-ready visualizations.
- Provides data download options in various formats for further research.
Conclusions:
- ScRDAVis democratizes scRNA-seq data analysis by providing an intuitive graphical user interface.
- Enables researchers to extract meaningful biological insights from complex scRNA-seq datasets.
- Facilitates advanced analyses like co-expression and TF regulatory network analysis without coding.
