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ScRDAVis: An R shiny application for single-cell transcriptome data analysis and visualization.

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Název: ScRDAVis: An R shiny application for single-cell transcriptome data analysis and visualization.
Autoři: Jagadesan, Sankarasubramanian, Guda, Chittibabu
Zdroj: PLoS Computational Biology; 11/13/2025, Vol. 21 Issue 11, p1-19, 19p
Témata: VISUALIZATION, RNA sequencing, USER interfaces, DATA visualization, GENE regulatory networks, DATA analysis, BIOLOGICAL research methodology
Abstrakt: 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. To address this, we developed Single-cell RNA Data Analysis and Visualization (ScRDAVis), an interactive, browser-based R Shiny application tailored for biologists with no programming expertise. ScRDAVis integrates widely used analysis packages, such as Seurat, CellChat, Monocle3, clusterProfiler and hdWGCNA to provide a user-friendly interface for single-cell data analysis. The application supports single-sample, multiple-sample and group-based analyses, along with features such as marker discovery, cell type annotation, subclustering analysis, and advanced functional studies. Key functionalities include cell-cell communication analysis, trajectory and pseudotime inference, pathway enrichment analysis, weighted gene co-expression network analysis (WGCNA), and transcription factor (TF) regulatory network analysis. ScRDAVis stands out as the first GUI-based platform offering hdWGCNA for co-expression network and TF regulatory network analysis using scRNA-seq data. ScRDAVis provides publication-ready visualizations with data download options in different formats empowering researchers to extract meaningful biological insights and democratizing the analytical capabilities required to comprehensively analyze scRNA-seq studies. ScRDAVis can be freely downloaded from GitHub at https://github.com/GudaLab/ScRDAVis or accessed from any browser at https://www.gudalab-rtools.net/ScRDAVis. Author summary: Single-cell RNA sequencing (scRNA-seq) has transformed our ability to study cellular heterogeneity, but its analysis requires integrating many computational steps that are often distributed across multiple tools. To address this challenge, we developed ScRDAVis that can be accessed via a web browser or installed locally on a desktop. This versatile tool unifies nine analytical modules, covering preprocessing, clustering, marker identification, functional enrichment, cell–cell communication, trajectory inference, and regulatory network analysis. We designed ScRDAVis to be very user-friendly, specifically, for those without prior programming expertise. This tool generates interactive visualizations at each step that can be downloaded in seven different formats. Similarly, we provide contextual help menus to document parameter descriptions, default values, and recommended ranges, ensuring reproducibility and transparency. We demonstrate the utility of ScRDAVis with a case study using the publicly available dataset GSE266873. This example illustrates how the integrated modules work together in a cohesive workflow to generate meaningful biological insights. By combining breadth, accessibility, and transparency, ScRDAVis enables a broad community of researchers to explore and effectively interpret single-cell transcriptomics data. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt: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. To address this, we developed Single-cell RNA Data Analysis and Visualization (ScRDAVis), an interactive, browser-based R Shiny application tailored for biologists with no programming expertise. ScRDAVis integrates widely used analysis packages, such as Seurat, CellChat, Monocle3, clusterProfiler and hdWGCNA to provide a user-friendly interface for single-cell data analysis. The application supports single-sample, multiple-sample and group-based analyses, along with features such as marker discovery, cell type annotation, subclustering analysis, and advanced functional studies. Key functionalities include cell-cell communication analysis, trajectory and pseudotime inference, pathway enrichment analysis, weighted gene co-expression network analysis (WGCNA), and transcription factor (TF) regulatory network analysis. ScRDAVis stands out as the first GUI-based platform offering hdWGCNA for co-expression network and TF regulatory network analysis using scRNA-seq data. ScRDAVis provides publication-ready visualizations with data download options in different formats empowering researchers to extract meaningful biological insights and democratizing the analytical capabilities required to comprehensively analyze scRNA-seq studies. ScRDAVis can be freely downloaded from GitHub at https://github.com/GudaLab/ScRDAVis or accessed from any browser at https://www.gudalab-rtools.net/ScRDAVis. Author summary: Single-cell RNA sequencing (scRNA-seq) has transformed our ability to study cellular heterogeneity, but its analysis requires integrating many computational steps that are often distributed across multiple tools. To address this challenge, we developed ScRDAVis that can be accessed via a web browser or installed locally on a desktop. This versatile tool unifies nine analytical modules, covering preprocessing, clustering, marker identification, functional enrichment, cell–cell communication, trajectory inference, and regulatory network analysis. We designed ScRDAVis to be very user-friendly, specifically, for those without prior programming expertise. This tool generates interactive visualizations at each step that can be downloaded in seven different formats. Similarly, we provide contextual help menus to document parameter descriptions, default values, and recommended ranges, ensuring reproducibility and transparency. We demonstrate the utility of ScRDAVis with a case study using the publicly available dataset GSE266873. This example illustrates how the integrated modules work together in a cohesive workflow to generate meaningful biological insights. By combining breadth, accessibility, and transparency, ScRDAVis enables a broad community of researchers to explore and effectively interpret single-cell transcriptomics data. [ABSTRACT FROM AUTHOR]
ISSN:1553734X
DOI:10.1371/journal.pcbi.1013721