EmptyDrops: Distinguishing Cells from Empty Droplets in Droplet-based Single-cell RNA Sequencing Data

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2019 Mar 24

PMID 30902100

Citations 431

Authors

Aaron T L Lun

Samantha Riesenfeld

Tallulah Andrews

The Phuong Dao

Tomas Gomes

John C Marioni

Affiliations

Soon will be listed here.

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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