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Updated: Oct 24, 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

Protocol for executing and benchmarking eight computational doublet-detection methods in single-cell RNA sequencing

Nan Miles Xi^1,2, Jingyi Jessica Li^2,3,4,5

¹Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL 60660, USA.

|August 12, 2021

Summary

This summary is machine-generated.

Doublets are a major issue in single-cell RNA sequencing (scRNA-seq) data. We created DoubletCollection, an R package, to benchmark doublet detection methods and streamline analysis.

Keywords:

Bioinformatics Molecular Biology RNAseq Sequencing

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Doublets, or cells with two distinct transcriptomes, are a significant confounder in single-cell RNA sequencing (scRNA-seq) data.
Accurate doublet detection is crucial for reliable downstream analysis and interpretation of scRNA-seq data.

Purpose of the Study:

To introduce DoubletCollection, an R package designed to integrate and benchmark multiple doublet detection methods for scRNA-seq data.
To provide a standardized protocol for evaluating the performance of various doublet detection algorithms.

Main Methods:

Development of the DoubletCollection R package, offering a unified interface for installing and running eight different doublet detection algorithms.
Implementation of a benchmarking protocol utilizing DoubletCollection to assess the efficacy of various doublet detection strategies.
Integration of downstream analysis and visualization tools within the package.

Main Results:

DoubletCollection successfully integrates eight distinct doublet detection methods.
The package provides a standardized framework for benchmarking these methods on scRNA-seq datasets.
It facilitates unified downstream analysis and visualization post-doublet detection.

Conclusions:

DoubletCollection offers a comprehensive solution for evaluating and applying doublet detection methods in scRNA-seq analysis.
The package is designed to be extensible, accommodating new doublet detection algorithms as they emerge.
This work provides a valuable resource for researchers seeking to improve the accuracy and reliability of their scRNA-seq data analysis.