SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data.
The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downs...
Guardado en:
Autores principales: | Jing Qi, Yang Zhou, Zicen Zhao, Shuilin Jin |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Public Library of Science (PLoS)
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/2ab4286ecc6f4833a97d4daf1b183d78 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Embracing the dropouts in single-cell RNA-seq analysis
por: Peng Qiu
Publicado: (2020) -
An accurate and robust imputation method scImpute for single-cell RNA-seq data
por: Wei Vivian Li, et al.
Publicado: (2018) -
ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion
por: Xiutao Pan, et al.
Publicado: (2021) -
Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast
por: Thoma Itoh, et al.
Publicado: (2021) -
Detection and removal of barcode swapping in single-cell RNA-seq data
por: Jonathan A. Griffiths, et al.
Publicado: (2018)