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

Title:
ScRDAVis: An R shiny application for single-cell transcriptome data analysis and visualization.
Authors:
Jagadesan S; Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, Nebraska, United States of America., Guda C; Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, Nebraska, United States of America.; Center for Biomedical Informatics Research and Innovation, University of Nebraska Medical Center, Omaha, Nebraska, United States of America.
Source:
PLoS computational biology [PLoS Comput Biol] 2025 Nov 13; Vol. 21 (11), pp. e1013721. Date of Electronic Publication: 2025 Nov 13 (Print Publication: 2025).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
Imprint Name(s):
Original Publication: San Francisco, CA : Public Library of Science, [2005]-
MeSH Terms:
Single-Cell Analysis*/methods , Gene Expression Profiling*/methods , Transcriptome*/genetics , Software*, Computational Biology/methods ; Sequence Analysis, RNA/methods ; Humans ; Gene Regulatory Networks ; Animals
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Grant Information:
P20 GM103427 United States GM NIGMS NIH HHS; P30 CA036727 United States CA NCI NIH HHS
Entry Date(s):
Date Created: 20251113 Date Completed: 20251118 Latest Revision: 20251122
Update Code:
20260130
PubMed Central ID:
PMC12626302
DOI:
10.1371/journal.pcbi.1013721
PMID:
41231930
Database:
MEDLINE

Weitere Informationen

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.
(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.)

The authors have declared that no competing interests exist.