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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.
Sankarasubramanian et al. (Thu,) studied this question.