Computational workflow for investigating highly variable genes in single-cell RNA-seq across multiple time points and cell types.
STAR Protoc
; 4(3): 102387, 2023 Sep 15.
Article
en En
| MEDLINE
| ID: mdl-37379219
ABSTRACT
Here, we present a computational approach for investigating highly variable genes (HVGs) associated with biological pathways of interest, across multiple time points and cell types in single-cell RNA-sequencing (scRNA-seq) data. Using public dengue virus and COVID-19 datasets, we describe steps for using the framework to characterize the dynamic expression levels of HVGs related to common and cell-type-specific biological pathways over multiple immune cell types. For complete details on the use and execution of this protocol, please refer to Arora et al.1.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Perfilación de la Expresión Génica
/
Análisis de Expresión Génica de una Sola Célula
Idioma:
En
Revista:
STAR Protoc
Año:
2023
Tipo del documento:
Article
País de afiliación:
Tailandia