EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data.
Genome Biol
; 20(1): 63, 2019 03 22.
Article
em En
| MEDLINE
| ID: mdl-30902100
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Monócitos
/
Análise de Sequência de RNA
/
Técnicas Analíticas Microfluídicas
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Análise de Célula Única
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Sequenciamento de Nucleotídeos em Larga Escala
/
Neurônios
Limite:
Humans
Idioma:
En
Revista:
Genome Biol
Assunto da revista:
BIOLOGIA MOLECULAR
/
GENETICA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Reino Unido