One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data.
Genome Biol
; 23(1): 102, 2022 04 20.
Article
em En
| MEDLINE
| ID: mdl-35443717
ABSTRACT
Integrative analysis of large-scale single-cell RNA sequencing (scRNA-seq) datasets can aggregate complementary biological information from different datasets. However, most existing methods fail to efficiently integrate multiple large-scale scRNA-seq datasets. We propose OCAT, One Cell At a Time, a machine learning method that sparsely encodes single-cell gene expression to integrate data from multiple sources without highly variable gene selection or explicit batch effect correction. We demonstrate that OCAT efficiently integrates multiple scRNA-seq datasets and achieves the state-of-the-art performance in cell type clustering, especially in challenging scenarios of non-overlapping cell types. In addition, OCAT can efficaciously facilitate a variety of downstream analyses.
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Base de dados:
MEDLINE
Assunto principal:
Análise de Célula Única
/
Aprendizado de Máquina
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article