A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering.

ABSTRACT:

Background

Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell resolution. Cell clustering serves as a foundation for scRNA-seq data analysis and provides new insights into the heterogeneity of cells within complex tissues. However, the inherent features of scRNA-seq data, such as heterogeneity, sparsity, and high dimensionality, pose significant technical challenges for effective cell clustering.

Results

Here, we present a novel deep clustering method, scCAGN, based on an adversarial autoencoder (AAE) and a cross-attention graph convolutional network (GCN), to address the above challenges in scRNA-seq data analysis. Specifically, to enhance data reconstruction, scCAGN utilizes a

SUBMITTER: Tang B 

PROVIDER: S-EPMC12359927 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature