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Related Experiment Video

Updated: May 22, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

scUmaper: An automated framework for doublet removal and cell-type annotation in single-cell transcriptomics.

Xushun Guo1, Mudan Zhang2, Zhuo Xie1

  • 1Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Iscience
|May 21, 2026
PubMed
Summary

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We developed scUmaper, an R/Seurat workflow for single-cell RNA sequencing (scRNA-seq) analysis. It improves doublet detection and cell-type annotation, making scRNA-seq more reproducible.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) offers high resolution for cellular heterogeneity.
  • scRNA-seq data is susceptible to technical artifacts like heterotypic doublets.
  • Current preprocessing and annotation methods often demand significant user expertise.

Purpose of the Study:

  • To introduce scUmaper, an R/Seurat-native workflow.
  • To integrate quality control, biologically grounded doublet filtering, and marker-library-based cell-type annotation.
  • To enhance the reproducibility and accessibility of scRNA-seq data analysis.

Main Methods:

  • scUmaper utilizes lineage-marker incompatibility rules for doublet detection.
  • It employs global clustering followed by within-lineage re-clustering.
Keywords:
Biological sciencesComputer science

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Last Updated: May 22, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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11:34

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  • The workflow integrates quality control and cell-type annotation using marker libraries.

    • Main Results:

    • scUmaper identified and removed high-confidence heterotypic doublets missed by simulation-based methods.
    • Achieved annotation agreement comparable to or exceeding common R-based baselines.
    • Demonstrated stable performance under simulated ambient RNA contamination and reduced sequencing depth.

    Conclusions:

    • scUmaper provides an interpretable and extensible framework for scRNA-seq analysis.
    • The workflow effectively addresses challenges in doublet filtering and cell-type annotation.
    • scUmaper lowers barriers for reproducible single-cell RNA sequencing analysis.