scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data.
Genome Biol
; 23(1): 82, 2022 03 21.
Article
em En
| MEDLINE
| ID: mdl-35313930
ABSTRACT
The increasing number of scRNA-seq data emphasizes the need for integrative analysis to interpret similarities and differences between single-cell samples. Although different batch effect removal methods have been developed, none are suitable for heterogeneous single-cell samples coming from multiple biological conditions. We propose a method, scINSIGHT, to learn coordinated gene expression patterns that are common among, or specific to, different biological conditions, and identify cellular identities and processes across single-cell samples. We compare scINSIGHT with state-of-the-art methods using simulated and real data, which demonstrate its improved performance. Our results show the applicability of scINSIGHT in diverse biomedical and clinical problems.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Análise de Célula Única
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article