ScSNViz: a user-friendly toolset for visualization and analysis of Cell-Specific Expressed SNVs

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Understanding genetic variation at the single-cell level is essential for studying cellular heterogeneity, clonal evolution, and gene expression.
• Existing tools for visualizing and analyzing cell-level genetic variants are limited, creating a need for new solutions.

Purpose of the Study:

• To introduce scSNViz, a comprehensive R-based toolset for the visualization and analysis of cell-specific expressed Single Nucleotide Variants (sceSNVs).
• To enable 3D visualization of sceSNVs within dimensionally reduced scRNA-seq data.
• To facilitate the analysis of sceSNV distribution and expression across individual cells.

Main Methods:

• Developed scSNViz as an R-based toolset.
• Integrated compatibility with popular scRNA-seq tools (Seurat) and cell-type classifiers (SingleR, scType).
• Incorporated trajectory inference using Slingshot.
• Implemented estimation, summary, and graphical representation of sceSNV statistical metrics.

Main Results:

• scSNViz provides 3D visualization of sceSNVs in scRNA-seq data.
• The tool supports analysis of individual and multiple sceSNVs.
• It offers compatibility with existing single-cell analysis pipelines.
• ScSNViz facilitates the estimation and visualization of sceSNV metrics.

Conclusions:

• scSNViz is a user-friendly R-based toolset that addresses the scarcity of tools for analyzing cell-level genetic variants.
• It enhances the understanding of cellular heterogeneity and gene expression regulation through effective visualization and analysis of sceSNVs.
• The tool is freely available and requires no specialized bioinformatics skills.