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scSNViz: visualization and analysis of cell-specific expressed SNVs.
Siera Martinez1, Tushar Sharma1, Luke Johnson1
1McCormick Genomics and Proteomics Center, Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, United States.
Bioinformatics (Oxford, England)
|January 14, 2026
Summary
scSNViz is a new R package that visualizes and quantifies expressed genetic variants in single-cell RNA sequencing data. This tool aids in understanding cellular heterogeneity and allelic regulation.
Area of Science:
- Genomics
- Computational Biology
- Bioinformatics
Background:
- Characterizing expressed genetic variation at the single-cell level is crucial for understanding cellular heterogeneity, allelic regulation, and mutational dynamics.
- Existing tools lack comprehensive visualization and quantitative analysis capabilities for expressed variants across individual cells.
Purpose of the Study:
- To introduce scSNViz, an R package designed for the exploration, quantification, and visualization of expressed single-nucleotide variants (SNVs) from single-cell RNA sequencing (scRNA-seq) data.
- To enable detailed investigation of variant expression patterns and allelic dynamics at the single-cell level.
Main Methods:
- Developed scSNViz as an R package utilizing cell-barcoded scRNA-seq data.
- Implemented functionalities for estimating variant allele fractions and clustering SNV expression profiles.
- Enabled 2D and 3D visualization of SNVs and SNV groups.
- Ensured interoperability with established single-cell analysis frameworks like Seurat, Slingshot, scType, and CopyKat.
Main Results:
- scSNViz provides tools for quantifying variant allele fractions and clustering SNV expression profiles.
- The package offers 2D and 3D visualization of individual or grouped SNVs.
- Facilitates analysis of cell-, cluster-, or lineage-specific variant expression and allelic dynamics (imprinting, random allele inactivation, transcriptional bursting).
- Enables integrative multi-omic analyses by interoperating with other single-cell analysis tools.
Conclusions:
- scSNViz is a versatile R package that enhances the analysis of expressed genetic variation in scRNA-seq data.
- It supports comprehensive visualization, quantification, and investigation of allelic dynamics, facilitating deeper insights into cellular heterogeneity and regulation.
- The package is freely available with documentation and examples for users of varying bioinformatics expertise.
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