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

Abstract

Summary: 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. : Multiplets are a source of confounding gene expression in single-cell RNA sequencing (scRNA-seq) that result from the simultaneous capture of multiple cells in a droplet. DePasquale et al. introduce DoubletDecon to identify putative doublets and to consider unique gene expression inherent to transitional states and progenitors to “rescue” singlet captures from inaccurate classification. Keywords: single-cell RNA-seq, multiplet, doublet, deconvolution, RNA-seq, bioinformatics, artifact detection