scViewer: An Interactive Single-Cell Gene Expression Visualization Tool

• Published in: Cells (Basel, Switzerland) Vol. 12; no. 11; p. 1489
• Main Authors: Patil, Abhijeet R., Kumar, Gaurav, Zhou, Huanyu, Warren, Liling
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.05.2023; MDPI
• Subjects: Alzheimer's disease; bioinformatics; Cells; co-expression; Communication; Databases, Factual; Datasets; Gene Expression; Metadata; Methods; Neurodegenerative diseases; R Shiny; RNA sequencing; scRNA-seq; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; single-cell RNA sequencing; Software; User interface; Visualization; United States; single-cell RNA sequencing; scRNA-seq; bioinformatics; co-expression; R Shiny; differential expression analysis; gene expression
• ISSN: 2073-4409; 2073-4409
• DOI: 10.3390/cells12111489

Single-cell RNA sequencing (scRNA-seq) is an attractive technology for researchers to gain valuable insights into the cellular processes and cell type diversity present in all tissues. The data generated by the scRNA-seq experiment are high-dimensional and complex in nature. Several tools are now available to analyze the raw scRNA-seq data from public databases; however, simple and easy-to-explore single-cell gene expression visualization tools focusing on differential expression and co-expression are lacking. Here, we present scViewer, an interactive graphical user interface (GUI) R/Shiny application designed to facilitate the visualization of scRNA-seq gene expression data. With the processed Seurat RDS object as input, scViewer utilizes several statistical approaches to provide detailed information on the loaded scRNA-seq experiment and generates publication-ready plots. The major functionalities of scViewer include exploring cell-type-specific gene expression, co-expression analysis of two genes, and differential expression analysis with different biological conditions considering both cell-level and subject-level variations using negative binomial mixed modeling. We utilized a publicly available dataset (brain cells from a study of Alzheimer’s disease to demonstrate the utility of our tool. scViewer can be downloaded from GitHub as a Shiny app with local installation. Overall, scViewer is a user-friendly application that will allow researchers to visualize and interpret the scRNA-seq data efficiently for multi-condition comparison by performing gene-level differential expression and co-expression analysis on the fly. Considering the functionalities of this Shiny app, scViewer can be a great resource for collaboration between bioinformaticians and wet lab scientists for faster data visualizations.