Doublet identification in single-cell sequencing data using scDblFinder
Pierre‐Luc Germain, Aaron T. L. Lun, Carlos Garcia Meixide, Will Macnair, Mark D. Robinson
Abstract
<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>