Abstract
SUMMARYMethods 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.
Highlights
Single-cell genomics provides a powerful means to derive and characterize novel cell populations and transitional states (Olsson et al, 2016; Velten et al, 2017; Villani et al, 2017; Yanez et al, 2017)
Multiplets with two captured cells, can be grouped into two main classes: (1) those that occur between transcriptionally distinct cell types and (2) those that occur within the same cell type, with multiplets of more than two cells being exceedingly rare (0.36%, assuming a doublet rate of 8%)
Overview To detect heterotypic doublet captures and distinguish them from gradual cellular transitions, we developed a multi-step analysis strategy that identifies an initial set of putative doublets based on deconvolution analysis, rescues erroneously predicted doublet clusters that have unique gene expression (STAR Methods; Figure 1A)
Summary
Single-cell genomics provides a powerful means to derive and characterize novel cell populations and transitional states (Olsson et al, 2016; Velten et al, 2017; Villani et al, 2017; Yanez et al, 2017). Multiplets with two captured cells, can be grouped into two main classes: (1) those that occur between transcriptionally distinct cell types (heterotypic) and (2) those that occur within the same cell type (homotypic), with multiplets of more than two cells being exceedingly rare (0.36%, assuming a doublet rate of 8%). Experimental methods, such as Cell Hashing, aim to address the challenge of doublet identification by labeling cells with different oligonucleotide bar codes to remove artifacts, but they are costly, increase the likelihood of cell death, and cannot be applied to previously generated datasets (Stoeckius et al, 2018)
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