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scViewer: An Interactive Single-Cell Gene Expression Visualization Tool.

Abhijeet R Patil1, Gaurav Kumar1, Huanyu Zhou1

  • 1Global Statistical and Data Sciences, Teva Pharmaceuticals, West Chester, PA 19380, USA.

Cells
|June 10, 2023
PubMed
Summary
This summary is machine-generated.

scViewer is a new R/Shiny application for visualizing single-cell RNA sequencing (scRNA-seq) data. It offers user-friendly exploration of gene expression, differential expression, and co-expression for efficient data interpretation.

Keywords:
R Shinybioinformaticsco-expressiondifferential expression analysisgene expressionscRNA-seqsingle-cell RNA sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) generates high-dimensional, complex data essential for understanding cellular diversity.
  • Existing analysis tools often lack intuitive visualization capabilities for differential and co-expression analysis.
  • Efficient interpretation of scRNA-seq data is crucial for biological discovery.

Purpose of the Study:

  • To introduce scViewer, an interactive R/Shiny application for visualizing scRNA-seq data.
  • To provide researchers with a user-friendly tool for exploring gene expression, differential expression, and co-expression.
  • To facilitate the interpretation of complex scRNA-seq datasets for multi-condition comparisons.

Main Methods:

  • Developed scViewer as an interactive graphical user interface (GUI) R/Shiny application.
  • Utilized processed Seurat RDS objects as input for data analysis.
  • Implemented statistical approaches including negative binomial mixed modeling for differential expression analysis.

Main Results:

  • scViewer facilitates visualization of cell-type-specific gene expression.
  • The application enables co-expression analysis of gene pairs.
  • It performs differential expression analysis considering cell-level and subject-level variations.
  • Publication-ready plots are generated for detailed data interpretation.

Conclusions:

  • scViewer provides efficient and user-friendly visualization of scRNA-seq data.
  • The tool supports on-the-fly gene-level differential and co-expression analysis.
  • scViewer enhances collaboration between bioinformaticians and wet lab scientists.
  • It serves as a valuable resource for interpreting complex single-cell gene expression data.