Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, United States of America.
PLoS Comput Biol. 2024 Apr 29;20(4):e1012084. doi: 10.1371/journal.pcbi.1012084. eCollection 2024 Apr.
We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance based on the ability of set genes to reconstruct values for all measured genes. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios. As demonstrated using real and simulated scRNA-seq data, RESET provides superior performance at a lower computational cost relative to other single sample approaches.
我们开发了一种新的、分析上新颖的单样本基因集测试方法,称为重建集测试(RESET)。RESET 根据集基因重建所有测量基因值的能力来量化基因集的重要性。RESET 使用一种计算效率高的随机化降秩重建算法(可通过 CRAN 上的 RESET R 包获得)来实现,该算法可有效地检测自包含和竞争情况下的丰度差异和相关性差异模式。使用真实和模拟的 scRNA-seq 数据证明,与其他单样本方法相比,RESET 以更低的计算成本提供了卓越的性能。