scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data.
Commun Biol
; 6(1): 1268, 2023 12 14.
Article
em En
| MEDLINE
| ID: mdl-38097699
ABSTRACT
Recent developments in single-cell technology have enabled the exploration of cellular heterogeneity at an unprecedented level, providing invaluable insights into various fields, including medicine and disease research. Cell type annotation is an essential step in its omics research. The mainstream approach is to utilize well-annotated single-cell data to supervised learning for cell type annotation of new singlecell data. However, existing methods lack good generalization and robustness in cell annotation tasks, partially due to difficulties in dealing with technical differences between datasets, as well as not considering the heterogeneous associations of genes in regulatory mechanism levels. Here, we propose the scPML model, which utilizes various gene signaling pathway data to partition the genetic features of cells, thus characterizing different interaction maps between cells. Extensive experiments demonstrate that scPML performs better in cell type annotation and detection of unknown cell types from different species, platforms, and tissues.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Análise da Expressão Gênica de Célula Única
/
Medicina
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article