Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering.

ABSTRACT: Clustering plays a crucial role in analyzing scRNA-seq data and has been widely used in studying cellular distribution over the past few years. However, the high dimensionality and complexity of scRNA-seq data pose significant challenges to achieving accurate clustering from a singular perspective. To address these challenges, we propose a novel approach, called multi-level multi-view network based on structural consistency contrastive learning (scMMN), for scRNA-seq data clustering. Firstly, the proposed method constructs shallow views through the $k$-nearest neighbor ($k$NN) and diffusion mapping (DM) algorithms, and then deep views are generated by utilizing the graph Laplacian filters. These deep multi-view data serve as the input for representation learning. To improve the clustering

SUBMITTER: Shu Z 

PROVIDER: S-EPMC11532661 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature