将空间分辨多组学数据与COSMOS进行协同整合。

Zhou Yuansheng, Xiao Xue, Dong Lei, Tang Chen, Xiao Guanghua, Xu Lin

Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.

Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.

Nat Commun. 2025 Jan 2;16(1):27. doi: 10.1038/s41467-024-55204-y.

Recent advancements in biological technologies have enabled the measurement of spatially resolved multi-omics data, yet computational algorithms for this purpose are scarce. Existing tools target either single omics or lack spatial integration. We generate a graph neural network algorithm named COSMOS to address this gap and demonstrated the superior performance of COSMOS in domain segmentation, visualization, and spatiotemporal map for spatially resolved multi-omics data integration tasks.

生物技术的最新进展使得对空间分辨的多组学数据进行测量成为可能，然而，用于此目的的计算算法却很稀缺。现有的工具要么针对单一组学，要么缺乏空间整合。我们生成了一种名为COSMOS的图神经网络算法来填补这一空白，并证明了COSMOS在空间分辨的多组学数据整合任务的域分割、可视化和时空映射方面具有卓越的性能。