ABSTRACT
Technologies and analytical methods for single-cell RNA sequencing (scRNA-Seq) have greatly advanced in recent years. While droplet- and well-based methods have significantly increased the isolation of cells for scRNA-Seq analysis, these technologies readily produce technical artifacts, such as doublet-cell and multiplet-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. Traditional approaches for detecting doublets, such as assessing the number of sequencing reads, fall short when different cell types with differing levels of transcriptional activity or library amplification efficiency are sequenced. We introduce DoubletDecon (https://github.com/EDePasquale/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 and cell-hashing cell singlets and doublets from scRNA-Seq datasets of varying cellular complexity. DoubletDecon is able to account for cell-cycle effects, and is compatible with diverse species and unsupervised population detection algorithms (e.g., ICGS, Seurat). We believe this approach has the potential to become a standard quality control step for the accurate delineation of cell states.
