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

Xiaoru Dong; Rhonda Bacher

doi:10.1016/j.patter.2022.100465

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

Patterns (N Y). 2022 Mar 11;3(3):100465. doi: 10.1016/j.patter.2022.100465.

Authors

Xiaoru Dong¹, Rhonda Bacher¹

Affiliation

¹ Department of Biostatistics, University of Florida, Gainesville, FL, USA.

Abstract

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.

Publication types

News