Protocol for Executing and Benchmarking Eight Computational Doublet-Detection Methods in Single-Cell RNA Sequencing Data Analysis

TL;DR

This paper introduces DoubletCollection, an R package that integrates eight doublet-detection methods for scRNA-seq data, providing a standardized protocol for benchmarking and downstream analysis visualization.

Contribution

The paper presents a unified framework and protocol for benchmarking multiple doublet-detection methods in scRNA-seq analysis, facilitating method comparison and integration.

Findings

DoubletCollection supports eight doublet-detection methods.

The protocol enables automated benchmarking of doublet detection methods.

It allows easy incorporation of new methods in the rapidly evolving field.

Abstract

The existence of doublets is a key confounder in single-cell RNA sequencing (scRNA-seq) data analysis. Computational methods have been developed for detecting doublets from scRNA-seq data. We developed an R package DoubletCollection to integrate the installation and execution of eight doublet-detection methods. DoubletCollection also provides a unified interface to perform and visualize downstream analysis after doublet detection. Here, we present a protocol of using DoubletCollection to benchmark doublet-detection methods. This protocol can automatically accommodate new doublet-detection methods in the fast-growing scRNA-seq field.