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Updated: Jun 23, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
Published on: January 10, 2019
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.
Motivation:
Single-cell RNA sequencing (scRNA-seq) is now a core technology for resolving cellular heterogeneity in complex samples, and standard analysis workflows produce a wide range of outputs, each requiring tailored visualization. To support this, a wide range of analysis tools have been developed, many of which offer built-in visualizations but leave further customization to the user. Researchers who run standard single-cell workflows in R, often experimental biologists with a working knowledge of Seurat and ggplot2, still spend considerable effort converting analytical outputs into figures that meet journal standards.
Results:
We present SCpubr, an R package that provides concise function calls for generating high-quality, publication-ready visualizations commonly used in single-cell transcriptome analyses.
Availability And Implementation:
SCpubr is available on CRAN (https://cran.r-project.org/package=SCpubr), with source code accessible on GitHub (https://github.com/enblacar/SCpubr).
Supplementary Information:
Supplementary figures are available at Bioinformatics Advances online. Extensive documentation and tutorials are available via the GitHub Pages site (https://enblacar.github.io/SCpubr-book/). The complete analysis code used to generate all figures in this publication, along with the full R session information and instructions for obtaining the raw input data, is available in GitHub (https://github.com/enblacar/SCpubr-manuscript).
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