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

ATL Lun, S Riesenfeld, T Andrews, TP Dao, T Gomes… - Genome biology, 2019 - Springer
Genome biology, 2019Springer
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 …
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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