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Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data.

ABSTRACT: Single-cell RNA-sequencing has become a widely used, powerful approach for studying cell populations. However, these methods often generate multiplet artifacts, where two or more cells receive the same barcode, resulting in a hybrid transcriptome. In most experiments, multiplets account for several percent of transcriptomes and can confound downstream data analysis. Here, we present Single-Cell Remover of Doublets (Scrublet), a framework for predicting the impact of multiplets in a given analysis and identifying problematic multiplets. Scrublet avoids the need for expert knowledge or cell clustering by simulating multiplets from the data and building a nearest neighbor classifier. To demonstrate the utility of this approach, we test Scrublet on several datasets that include independent knowledge of cell multiplets. Scrublet is freely available for download at github.com/AllonKleinLab/scrublet.

SUBMITTER: Wolock SL

PROVIDER: S-EPMC6625319 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data.

Wolock Samuel L SL Lopez Romain R Klein Allon M AM

Cell systems 20190403 4

Single-cell RNA-sequencing has become a widely used, powerful approach for studying cell populations. However, these methods often generate multiplet artifacts, where two or more cells receive the same barcode, resulting in a hybrid transcriptome. In most experiments, multiplets account for several percent of transcriptomes and can confound downstream data analysis. Here, we present Single-Cell Remover of Doublets (Scrublet), a framework for predicting the impact of multiplets in a given analysi ...[more]

PMID: 30954476

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