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EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Lun, Aaron TL 
Riesenfeld, Samantha 
Andrews, Tallulah 
Dao, The Phuong 
Gomes, Tomas 

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.

Description

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

Conference Name

Journal ISSN

1474-7596
1474-760X

Volume Title

20

Publisher

Springer Science and Business Media LLC