ScMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosystems and molecular interactions in various biomedical research. Hence, identifying cell types from large-scale scRNA-seq data using existing annotations is challenging and requires stable and interpretable methods. However, the current cell type identification methods have limited performance, mainly due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, a multi-view graph convolutional network model (scMGCN) that integrates multiple graph structures from raw scRNA-seq data and applies graph convolutional networks with attention mechanisms to learn cell embeddings and predict cell labe

SUBMITTER: Sun H 

PROVIDER: S-EPMC10889820 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature