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

ABSTRACT: The existence of doublets is a key confounder in single-cell RNA sequencing (scRNA-seq) data analysis. Computational techniques 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 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 accommodate new doublet-detection methods in the fast-growing scRNA-seq field. For details on the use and execution of this protocol, please refer to Xi and Li (2020).

SUBMITTER: Xi NM

PROVIDER: S-EPMC8339294 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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

Xi Nan Miles NM Li Jingyi Jessica JJ

STAR protocols 20210728 3

The existence of doublets is a key confounder in single-cell RNA sequencing (scRNA-seq) data analysis. Computational techniques 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 provides a unified interface to perform and visualize downstream analysis after doublet detection. Here, we present a protocol of using DoubletCollection to benchmark ...[more]

PMID: 34382023

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