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Updated: Sep 5, 2025

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Doublet identification in single-cell sequencing data using scDblFinder.

Pierre-Luc Germain^1,2,3, Aaron Lun⁴, Carlos Garcia Meixide²

¹DMLS Lab of Statistical Bioinformatics, University of Zürich, Zürich, 805, Switzerland.

|July 12, 2022

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Doublets are common in single-cell sequencing, causing errors. We created scDblFinder, a fast and accurate tool to detect these doublets in both RNA and ATAC sequencing data.

Area of Science:

Computational Biology and Bioinformatics
Genomics and Molecular Biology

Background:

Doublets, arising from two cells captured as one, are a significant source of artifacts in single-cell sequencing data.
Existing doublet detection methods have limitations in speed, flexibility, or accuracy, necessitating improved approaches.

Purpose of the Study:

To introduce scDblFinder, a novel, efficient, and precise Bioconductor-based method for doublet detection.
To evaluate the performance of scDblFinder on diverse single-cell sequencing datasets, including RNA and ATAC-seq.
To provide insights into doublet formation, detection strategies, and enrichment analysis.

Main Methods:

Development of scDblFinder, a new algorithm integrated into the Bioconductor framework.
Benchmarking scDblFinder against existing doublet detection tools using both simulated and real-world single-cell RNA and ATAC-seq data.

Keywords:

doublets filtering multiplets single-cell sequencing

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Last Updated: Sep 5, 2025

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

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

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Analysis of doublet formation patterns and their impact on downstream analyses.

Main Results:

scDblFinder demonstrates high accuracy in identifying doublets, including challenging heterotypic doublets, across complex datasets.
The method is computationally efficient and flexible, suitable for large-scale single-cell experiments.
Independent benchmarks confirm that scDblFinder outperforms alternative doublet detection strategies.

Conclusions:

scDblFinder offers a robust and superior solution for doublet detection in single-cell genomics.
The tool facilitates more reliable interpretation of single-cell RNA and ATAC-seq data by mitigating doublet-induced artifacts.
This method aids researchers in improving the quality and accuracy of their single-cell analyses.