ScRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning.

ABSTRACT: Cell type annotation is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data. A large number of deep learning (DL)-based methods have been proposed to annotate cell types of scRNA-seq data and have achieved impressive results. However, there are several limitations to these methods. First, they do not fully exploit cell-to-cell differential features. Second, they are developed based on shallow features and lack of flexibility in integrating high-order features in the data. Finally, the low-dimensional gene features may lead to overfitting in neural networks. To overcome those limitations, we propose a novel DL-based model, cell type annotation of single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning (scRGCL), based on resid

SUBMITTER: Yuan L 

PROVIDER: S-EPMC11663015 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature