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

Erica A K DePasquale1, Daniel J Schnell2, Pieter-Jan Van Camp1, Íñigo Valiente-Alandí3, Burns C Blaxall4, H Leighton Grimes5, Harinder Singh6, Nathan Salomonis7

  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.

Presented By Erica DePasquale

Biotechnology, GenomicsNicholas Liao2019-11 (Nov)