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

ABSTRACT: Nonlinear dimensionality reduction (NLDR) methods such as t-Distributed Stochastic Neighbour Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) have been widely used for biological data exploration, especially in single-cell analysis. However, the existing methods have drawbacks in preserving data's geometric and topological structures. A high-dimensional data analysis method, called Panoramic manifold projection (Panoramap), was developed as an enhanced deep learning framework for structure-preserving NLDR. Panoramap enhances deep neural networks by using cross-layer geometry-preserving constraints. The constraints constitute the loss for deep manifold learning and serve as geometric regularizers for NLDR network training. Therefore, Panoramap has better performanc

SUBMITTER: Wang Y 

PROVIDER: S-EPMC9316349 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature