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

By Samuel L. Wolock, Romain Lopez, Allon M. Klein. bioRxiv, 2018

2,265 citations recorded by the source corpus.

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 Scrublet (Single-Cell Remover of Doublets), 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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/948c5d95d2fe6cf9beb941732147028415d5a576

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Citations in the corpus

Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, background): “Three recent doublet detection tools offer more elegant and potentially better solutions (DoubletDecon: preprint: DePasquale et al , 2018; Scrublet: Wolock et al , 2019; Doublet Finder: McGinnis et al , 2018).”
Single cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species (2019, methodology): “After filtering scRNA-Seq data to exclude putative cell doublets (Wolock et al., 2018) and stressed or dead cells, a total of 40,362 cell transcriptomes were retained for analysis across the seven patients.”
Multiple early factors anticipate post-acute COVID-19 sequelae (2022, positive): “Doublets were simultaneously identiﬁed in sample demultiplexing or using scrublet (Wolock et al., 2019) and removed prior to the aforementioned ﬁltering.”
Comprehensive analysis of single cell ATAC-seq data with SnapATAC (2021, methodology): “To examine this, we applied Scrublet to a single-cell ATAC-seq dataset of mixed human and mouse cells where the “ ground-truth ” doublets can be identi ﬁ ed based on the alignment ratio to human and mouse genome.”
DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors (2018, methodology): “This decrease in performance is common to other computational doublet detection strategies that utilize transcriptomic information alone (Wolock et al., 2018, this issue of Cell Systems) and illustrates a key methodological limitation that should be carefully considered by all prospective users.”
Individual brain organoids reproducibly form cell diversity of the human cerebral cortex (2019, positive): “1 39 was used to assess the effect of droplets that may have contained more than one cell, with an expected doublet rate of 0.1 and a score threshold of 0.39, chosen based on histograms of simulated doublet scores (Extended Data Fig.”