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Updated: Jan 17, 2026

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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
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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.

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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.