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  1. Home
  3. Scpubr: A User-friendly R-package For Generating Publication-ready Visualizations Of Single-cell Transcriptome Analyses.
  1. Home
  3. Scpubr: A User-friendly R-package For Generating Publication-ready Visualizations Of Single-cell Transcriptome Analyses.

Related Experiment Video

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SCpubr: a user-friendly R-package for generating publication-ready visualizations of single-cell transcriptome

Enrique Blanco-Carmona1,2, Marcel Kool1,2,3,4

  • 1Division of Pediatric Neurooncology, Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany.

Bioinformatics Advances
|June 22, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

SCpubr is a new R package simplifying the creation of publication-ready figures from single-cell RNA sequencing data. It helps researchers generate high-quality visualizations efficiently for complex analyses.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
  • Standard analysis workflows generate diverse outputs requiring specialized visualizations.
  • Existing tools often lack sufficient customization for publication-quality figures.

Purpose of the Study:

  • To introduce SCpubr, an R package designed for generating publication-ready visualizations for scRNA-seq data.
  • To streamline the process of creating figures from complex single-cell analyses.
  • To assist researchers, particularly experimental biologists, in producing journal-standard figures.

Main Methods:

  • Development of the SCpubr R package.
  • Implementation of concise functions for common scRNA-seq visualization needs.
  • Integration with existing R-based analysis workflows (e.g., Seurat, ggplot2).

    • Main Results:

    • SCpubr provides easy-to-use functions for generating high-quality visualizations.
    • The package simplifies the conversion of analytical outputs into publication-ready figures.
    • Enhanced visualization capabilities for single-cell transcriptome analyses.

    Conclusions:

    • SCpubr effectively addresses the need for efficient and high-quality figure generation in scRNA-seq research.
    • The package empowers researchers to create publication-standard visualizations with less effort.
    • SCpubr enhances the accessibility and reproducibility of scRNA-seq data visualization.