Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
Nat Methods. 2021 Nov;18(11):1317-1321. doi: 10.1038/s41592-021-01286-1. Epub 2021 Nov 1.
The scaling of single-cell data exploratory analysis with the rapidly growing diversity and quantity of single-cell omics datasets demands more interpretable and robust data representation that is generalizable across datasets. Here, we have developed a 'linearly interpretable' framework that combines the interpretability and transferability of linear methods with the representational power of non-linear methods. Within this framework we introduce a data representation and visualization method, GraphDR, and a structure discovery method, StructDR, that unifies cluster, trajectory and surface estimation and enables their confidence set inference.
单细胞数据探索性分析的扩展要求更具可解释性和稳健性的数据表示，并且能够跨数据集进行推广。在这里，我们开发了一种“线性可解释”框架，该框架结合了线性方法的可解释性和可转移性以及非线性方法的表示能力。在这个框架内，我们引入了一种数据表示和可视化方法 GraphDR，以及一种结构发现方法 StructDR，它统一了聚类、轨迹和曲面估计，并能够对其置信集进行推断。
