Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA.
Nat Commun
; 14(1): 400, 2023 01 25.
Article
en En
| MEDLINE
| ID: mdl-36697410
ABSTRACT
Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias Pancreáticas
/
Carcinoma Ductal Pancreático
Tipo de estudio:
Diagnostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Año:
2023
Tipo del documento:
Article