Nonlinear embedding and integration of omics data: a fast and tuning-free approach.

ABSTRACT: The rapid progress of single-cell technology has facilitated cost-effective acquisition of diverse omics data, allowing biologists to unravel the complexities of cell populations, disease states, and more. Additionally, single-cell multi-omics technologies have opened new avenues for studying biological interactions. However, the high dimensionality and sparsity of omics data present significant analytical challenges. Dimension reduction (DR) techniques are hence essential for analyzing such complex data, yet many existing methods have inherent limitations. Linear methods like principal component analysis (PCA) struggle to capture intricate associations within data. In response, nonlinear techniques have emerged, but they may face scalability issues, be restricted to single-omics data, or

SUBMITTER: Liu S 

PROVIDER: S-EPMC12009717 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature