Complex hierarchical structures analysis in single-cell data with Poincare deep manifold transformation.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) offers remarkable insights into cellular development and differentiation by capturing the gene expression profiles of individual cells. The role of dimensionality reduction and visualization in the interpretation of scRNA-seq data has gained widely acceptance. However, current methods face several challenges, including incomplete structure-preserving strategies and high distortion in embeddings, which fail to effectively model complex cell trajectories with multiple branches. To address these issues, we propose the Poincaré deep manifold transformation (PoincaréDMT) method, which maps high-dimensional scRNA-seq data to a hyperbolic Poincaré disk. This approach preserves global structure from a graph Laplacian matrix while achieving local structure cor

SUBMITTER: Xu Y 

PROVIDER: S-EPMC11757945 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature