Format

Send to

Choose Destination
Genome Biol. 2019 Mar 22;20(1):63. doi: 10.1186/s13059-019-1662-y.

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

Author information

1
Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, UK. aaron.lun@cruk.cam.ac.uk.
2
Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
3
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
4
Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, USA.
5
Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, UK. marioni@ebi.ac.uk.
6
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK. marioni@ebi.ac.uk.
7
EMBL European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK. marioni@ebi.ac.uk.

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.

KEYWORDS:

Cell detection; Droplet-based protocols; Empty droplets; Single-cell transcriptomics

PMID:
30902100
PMCID:
PMC6431044
DOI:
10.1186/s13059-019-1662-y
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center