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Computational evaluation demonstrates accurate doublet detection in single-cell sequencing datasets, indicating improved quality control for genomic analyses.
<ns3:p>Doublets are prevalent in single-cell sequencing data and can lead to artifactual findings. A number of strategies have therefore been proposed to detect them. Building on the strengths of existing</ns3:p> <ns3:p> approaches, we developed <ns3:italic>scDblFinder</ns3:italic> , a fast, flexible and accurate Bioconductor-based doublet detection method. Here we present the method, justify its design choices, demonstrate its performance on both single-cell RNA and accessibility (ATAC) sequencing data, and provide some observations on doublet formation, detection, and enrichment analysis. Even in complex datasets, <ns3:italic>scDblFinder</ns3:italic> can accurately identify most heterotypic doublets, and was already found by an independent benchmark to outcompete alternatives. </ns3:p>
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Germain et al. (2022) studied this question.
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