Detecting novel cell type in single-cell chromatin accessibility data via open-set domain adaptation.
Brief Bioinform
; 25(5)2024 Jul 25.
Article
em En
| MEDLINE
| ID: mdl-39073828
ABSTRACT
Recent advances in single-cell technologies enable the rapid growth of multi-omics data. Cell type annotation is one common task in analyzing single-cell data. It is a challenge that some cell types in the testing set are not present in the training set (i.e. unknown cell types). Most scATAC-seq cell type annotation methods generally assign each cell in the testing set to one known type in the training set but neglect unknown cell types. Here, we present OVAAnno, an automatic cell types annotation method which utilizes open-set domain adaptation to detect unknown cell types in scATAC-seq data. Comprehensive experiments show that OVAAnno successfully identifies known and unknown cell types. Further experiments demonstrate that OVAAnno also performs well on scRNA-seq data. Our codes are available online at https//github.com/lisaber/OVAAnno/tree/master.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Cromatina
/
Análise de Célula Única
Limite:
Animals
/
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article