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Cell Rep. 2019 Nov 5;29(6):1718-1727.e8. doi: 10.1016/j.celrep.2019.09.082.

DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data.

Author information

1
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Biomedical Informatics, University of Cincinnati, Cincinnati, OH 45221, USA.
2
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Heart Institute and Center for Translational Fibrosis Research, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.
3
Heart Institute and Center for Translational Fibrosis Research, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.
4
Heart Institute and Center for Translational Fibrosis Research, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati, Cincinnati, OH 45221, USA.
5
Department of Pediatrics, University of Cincinnati, Cincinnati, OH 45221, USA; Division of Immunobiology and Center for Systems Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Division of Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.
6
Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA 15260, USA; Department of Immunology, University of Pittsburgh, Pittsburgh, PA 15260, USA; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15620, USA.
7
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Biomedical Informatics, University of Cincinnati, Cincinnati, OH 45221, USA; Department of Pediatrics, University of Cincinnati, Cincinnati, OH 45221, USA. Electronic address: nathan.salomonis@cchmc.org.

Abstract

Methods for single-cell RNA sequencing (scRNA-seq) have greatly advanced in recent years. While droplet- and well-based methods have increased the capture frequency of cells for scRNA-seq, these technologies readily produce technical artifacts, such as doublet cell captures. Doublets occurring between distinct cell types can appear as hybrid scRNA-seq profiles, but do not have distinct transcriptomes from individual cell states. We introduce DoubletDecon, an approach that detects doublets with a combination of deconvolution analyses and the identification of unique cell-state gene expression. We demonstrate the ability of DoubletDecon to identify synthetic, mixed-species, genetic, and cell-hashing cell doublets from scRNA-seq datasets of varying cellular complexity with a high sensitivity relative to alternative approaches. Importantly, this algorithm prevents the prediction of valid mixed-lineage and transitional cell states as doublets by considering their unique gene expression. DoubletDecon has an easy-to-use graphical user interface and is compatible with diverse species and unsupervised population detection algorithms.

KEYWORDS:

RNA-seq; artifact detection; bioinformatics; deconvolution; doublet; multiplet; single-cell RNA-seq

PMID:
31693907
DOI:
10.1016/j.celrep.2019.09.082
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