A semi-automatic cell type annotation method for single-cell RNA sequencing dataset
Genomics & Informatics
; : e26-2020.
Article
em En
| WPRIM
| ID: wpr-898412
Biblioteca responsável:
WPRO
ABSTRACT
Single-cell RNA sequencing (scRNA-seq) has been widely applied to provide insights into the cell-by-cell expression difference in a given bulk sample. Accordingly, numerous analysis methods have been developed. As it involves simultaneous analyses of many cell and genes, efficiency of the methods is crucial. The conventional cell type annotation method is laborious and subjective. Here we propose a semi-automatic method that calculates a normalized score for each cell type based on user-supplied cell type–specific marker gene list. The method was applied to a publicly available scRNA-seq data of mouse cardiac non-myocyte cell pool. Annotating the 35 t-stochastic neighbor embedding clusters into 12 cell types was straightforward, and its accuracy was evaluated by constructing co-expression network for each cell type. Gene Ontology analysis was congruent with the annotated cell type and the corollary regulatory network analysis showed upstream transcription factors that have well supported literature evidences. The source code is available as an R script upon request.
Texto completo:
1
Base de dados:
WPRIM
Idioma:
En
Revista:
Genomics & Informatics
Ano de publicação:
2020
Tipo de documento:
Article
País de publicação:
COREA DEL SUR
/
COREIA DO SUL
/
KR
/
SOUTH KOREA