MarkerCount: A stable, count-based cell type identifier for single-cell RNA-seq experiments.
Comput Struct Biotechnol J
; 20: 3120-3132, 2022.
Article
em En
| MEDLINE
| ID: mdl-35782735
ABSTRACT
Cell type identification is a key step toward downstream analysis of single cell RNA-seq experiments. Although the primary objective is to identify known cell populations, good identifiers should also recognize unknown clusters which may represent a previously unidentified subpopulation of a known cell type or tumor cells of an unknown phenotype. Herein, we present MarkerCount, which utilizes the number of expressed markers, regardless of their expression level. MarkerCount works in both reference- and marker-based mode, where the latter utilizes existing lists of markers, while the former uses a pre-annotated dataset to find markers to be used for cell type identification. In both modes, MarkerCount first utilizes the "marker count" to identify cell populations and, after rejecting uncertain cells, reassigns cell type and/or makes corrections in cluster-basis. The performance of MarkerCount was evaluated and compared with existing identifiers, both marker- and reference-based, that can be customized using publicly available datasets and marker databases. The results show that MarkerCount performs better in the identification of known populations as well as of unknown ones, when compared to other reference- and marker-based cell type identifiers for most of the datasets analyzed.
Texto completo:
1
Base de dados:
MEDLINE
Idioma:
En
Revista:
Comput Struct Biotechnol J
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Coréia do Sul