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

被引：2
|
作者：
Dong, Xiaoru [1 ]
Bacher, Rhonda [1 ]
机构：
[1] Univ Florida, Dept Biostat, Gainesville, FL 32611 USA
来源：
PATTERNS | 2022年 / 3卷 / 03期
关键词：
D O I：
10.1016/j.patter.2022.100465
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
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页数：2
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