Diffusive topology preserving manifold distances for single-cell data analysis

• 成果类型: Article
• 署名作者: Wei, Jiangyong; Zhang, Bin; Wang, Qiu; Zhou, Tianshou; Tai, Tianhai; Chen, Luonan
• 署名单位: Sun Yat Sen University; Monash University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; Center for Excellence in Molecular Cell Science, CAS
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2404860121
• 发表日期: 2025-01-28
• 页码: e2404860121
• 关键词: manifold distance; data topology; diffusion model; Dimension Reduction; single-cell analysis

摘要

Manifold learning techniques have emerged as crucial tools for uncovering latent patterns in high-dimensional single-cell data. However, most existing dimensionality reduction methods primarily rely on 2D visualization, which can distort true data relationships and fail to extract reliable biological information. Here, we present DTNE (diffusive topology neighbor embedding), a dimensionality reduction framework that faithfully approximates manifold distance to enhance cellular relationships and dynamics. DTNE constructs a manifold distance matrix using a modified personalized PageRank algorithm, thereby preserving topological structure while enabling diverse single-cell analyses. This approach facilitates distribution-based cellular relationship analysis, pseudotime inference, and clustering within a unified framework. Extensive benchmarking against mainstream algorithms on diverse datasets demonstrates DTNE's superior performance in maintaining geodesic distances and revealing significant biological patterns. Our results establish DTNE as a powerful tool for high-dimensional data analysis in uncovering meaningful biological insights.