# scSNViz: visualization and analysis of cell-specific expressed SNVs

## TL;DR

scSNViz is an R package that helps researchers visualize and analyze single-cell expressed genetic variations, enabling insights into cellular heterogeneity and allelic regulation.

## Contribution

scSNViz introduces a novel tool for visualizing and analyzing expressed SNVs in single-cell RNA sequencing data with integration into existing frameworks.

## Key findings

- scSNViz supports variant allele fraction estimation and clustering of SNV expression profiles.
- The tool enables 2D and 3D visualization of SNVs and user-defined SNV groups.
- It integrates with Seurat, Slingshot, scType, and CopyKat for multi-omic analyses.

## Abstract

Accurately characterizing expressed genetic variation at the single-cell level is essential for understanding transcriptional heterogeneity, allelic regulation, and mutational dynamics within complex tissues. However, few tools enable comprehensive visualization and quantitative analysis of expressed variants across individual cells.

scSNViz is an R package for the exploration, quantification, and visualization of expressed single-nucleotide variants (SNVs) from cell-barcoded single-cell RNA sequencing (scRNA-seq) data. The software supports estimation of variant allele fractions, clustering of SNV expression profiles, and 2D and 3D visualization of individual SNVs or user-defined SNV groups. Beyond visualization, scSNViz facilitates investigation of cell-, cluster-, or lineage-specific variant expression patterns, as well as allelic dynamics including imprinting, random allele inactivation, and transcriptional bursting. It interoperates seamlessly with established single-cell frameworks—Seurat for clustering, Slingshot for trajectory inference, scType for cell-type annotation, and CopyKat for copy-number profiling—enabling integrative multi-omic analyses of expressed variation.

scSNViz is implemented in R and freely available at https://github.com/HorvathLab/scSNViz (DOI: 10.5281/zenodo.17307516). The package includes comprehensive documentation and example workflows designed for users with limited bioinformatics experience.