Data denoising with transfer learning in single-cell transcriptomics.
Nat Methods
; 16(9): 875-878, 2019 09.
Article
en En
| MEDLINE
| ID: mdl-31471617
ABSTRACT
Single-cell RNA sequencing (scRNA-seq) data are noisy and sparse. Here, we show that transfer learning across datasets remarkably improves data quality. By coupling a deep autoencoder with a Bayesian model, SAVER-X extracts transferable gene-gene relationships across data from different labs, varying conditions and divergent species, to denoise new target datasets.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neoplasias de la Mama
/
Leucocitos Mononucleares
/
Linfocitos T
/
Análisis de Secuencia de ARN
/
Biología Computacional
/
Análisis de la Célula Individual
/
Transcriptoma
Tipo de estudio:
Prognostic_studies
Límite:
Animals
/
Female
/
Humans
Idioma:
En
Revista:
Nat Methods
Asunto de la revista:
TECNICAS E PROCEDIMENTOS DE LABORATORIO
Año:
2019
Tipo del documento:
Article
País de afiliación:
Estados Unidos