Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization.

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• Nonlinear dimensionality reduction (NLDR) methods like t-SNE and UMAP are crucial for exploring biological data, particularly single-cell data.
• Existing NLDR methods often struggle to preserve the intricate geometric and topological structures inherent in high-dimensional biological datasets.

Purpose of the Study:

• To introduce Panoramic manifold projection (Panoramap), an advanced deep learning framework designed for structure-preserving NLDR.
• To evaluate Panoramap's efficacy in enhancing the visualization and interpretation of single-cell data.

Main Methods:

• Panoramap utilizes deep neural networks enhanced with cross-layer geometry-preserving constraints.
• These constraints act as geometric regularizers during network training, optimizing the deep manifold learning process.

Main Results:

• Panoramap demonstrates superior performance in preserving the global structure of high-dimensional data compared to existing methods.
• Application to single-cell datasets revealed Panoramap's strength in delineating cell type lineage/hierarchy and identifying rare cell populations.

Conclusions:

• Panoramap offers improved, biologically plausible visualizations and interpretations of single-cell data.
• The method facilitates trajectory inference and holds potential for early tumor diagnosis, applicable to various high-dimensional data analysis fields.