301. DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data.
- Author
-
DePasquale EAK, Schnell DJ, Van Camp PJ, Valiente-Alandí Í, Blaxall BC, Grimes HL, Singh H, and Salomonis N
- Subjects
- Algorithms, Animals, Cluster Analysis, Databases, Genetic, HEK293 Cells, Humans, Mice, NIH 3T3 Cells, Sensitivity and Specificity, Signal-To-Noise Ratio, Software, Transcriptome genetics, RNA-Seq methods, Single-Cell Analysis methods
- 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., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)
- Published
- 2019
- Full Text
- View/download PDF