Review of single-cell RNA-seq data clustering for cell-type identification and characterization.
RNA
; 29(5): 517-530, 2023 05.
Article
em En
| MEDLINE
| ID: mdl-36737104
ABSTRACT
In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control, normalization, and dimension reduction. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on simulated and multiple single-cell transcriptomic data sets.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Análise da Expressão Gênica de Célula Única
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Revista:
RNA
Assunto da revista:
BIOLOGIA MOLECULAR
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China