Nonlinear Embedding and Integration of Omics Data: A Fast and Tuning-Free Approach

Music City Center 

The rapid progress of single-cell technology is enabling biologists to unravel the intricacies of cell populations, disease states, and developmental lineages. The high-dimensional, noisy, and sparse nature of single-cell omics data poses significant analytical challenges. Here, we introduce DCOL (Dissimilarity based on Conditional Ordered List) correlation, a functional dependency measure for quantifying nonlinear relationships between variables. Based on this measure, we propose DCOL-PCA and DCOL-CCA, for dimension reduction and integration of single- and multi-omics data. In simulation studies, our methods outperformed eight other DR methods and four joint dimension reduction (jDR) methods, showcasing stable performance across various settings. It proved highly effective in extracting essential factors even in the most challenging scenarios. We also validated these methods on real datasets, with our method demonstrating its ability to detect intricate signals within and between omics data and generate lower-dimensional embeddings that preserve the essential information and latent structures in the data.

Nonlinear Dimensionality Reduction

Single-cell Analysis

Multi-Omics Integration 

Section on Statistics in Genomics and Genetics