Clustering single-cell multi-omics data via multi-subspace contrastive learning with structural smoothness.

ABSTRACT: The integration of single-cell multi-omics data can uncover the underlying regulatory basis of diverse cell types and states. However, single-cell data inherently suffer from high levels of noise, sparsity, and intercellular heterogeneity, which pose significant challenges to the accuracy and robustness of clustering algorithms. Most existing multi-omics clustering approaches primarily focus on the integration of omics individuality and commonality across modalities, but they ignore the diverse feature extraction of the low-dimensional representation before the fusion of single-cell multi-omics data, and the feature smoothing consistency of the diverse features after the fusion of single-cell multi-omics data. In order to address above issues, we propose a novel multi-subspace contrastive

SUBMITTER: Ding Y 

PROVIDER: S-EPMC12834668 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature