Data-driven assessment of dimension reduction quality for single-cell omics data

• Published in: Patterns (New York, N.Y.) Vol. 3; no. 3; p. 100465
• Main Authors: Dong, Xiaoru, Bacher, Rhonda
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 11.03.2022; Elsevier
• Subjects: Preview
• ISSN: 2666-3899; 2666-3899
• DOI: 10.1016/j.patter.2022.100465

Dimension reduction (DR) techniques have become synonymous with single-cell omics data due to their ability to generate attractive visualizations and enable analyses of high-dimensional data. In this issue of Patterns, Johnsona et al. develop a statistical approach to assist in selecting high-quality reduced representations to improve analyses and biological interpretations. Dimension reduction (DR) techniques have become synonymous with single-cell omics data due to their ability to generate attractive visualizations and enable analyses of high-dimensional data. In this issue of Patterns, Johnsona et al. develop a statistical approach to assist in selecting high-quality reduced representations to improve analyses and biological interpretations.