EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data.
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Peer-reviewed
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Abstract
Droplet-based single-cell RNA sequencing protocols have dramatically increased the throughput of single-cell transcriptomics studies. A key computational challenge when processing these data is to distinguish libraries for real cells from empty droplets. Here, we describe a new statistical method for calling cells from droplet-based data, based on detecting significant deviations from the expression profile of the ambient solution. Using simulations, we demonstrate that EmptyDrops has greater power than existing approaches while controlling the false discovery rate among detected cells. Our method also retains distinct cell types that would have been discarded by existing methods in several real data sets.
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Keywords
Cell detection, Droplet-based protocols, Empty droplets, Single-cell transcriptomics, Biomarkers, High-Throughput Nucleotide Sequencing, Humans, Microfluidic Analytical Techniques, Monocytes, Neurons, Sequence Analysis, RNA, Single-Cell Analysis
Journal Title
Genome Biol
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Journal ISSN
1474-7596
1474-760X
1474-760X
Volume Title
20
Publisher
Springer Science and Business Media LLC
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Sponsorship
Cancer Research UK (22231)