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

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


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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Animals
  • Cluster Analysis
  • Databases, Genetic
  • HEK293 Cells
  • Humans
  • Mice
  • NIH 3T3 Cells
  • RNA-Seq / methods*
  • Sensitivity and Specificity
  • Signal-To-Noise Ratio
  • Single-Cell Analysis / methods*
  • Software
  • Transcriptome / genetics