DOT：一种灵活的多目标优化框架，用于在单细胞和空间组学之间转移特征
DOT: A flexible multi-objective optimization framework for transferring features across single-cell and spatial omics
AI总结：
DOT框架通过多目标优化在单细胞和空间组学数据间转移特征，兼顾细胞类型、状态及基因表达，提升组织映射的分辨率和基因覆盖。
摘要：
单细胞RNA测序（scRNA-seq）和空间解析成像/测序技术已革新生物医学研究。一方面，scRNA-seq提供单细胞转录组信息，但缺乏空间上下文；另一方面，空间解析测量在分辨率和基因覆盖间存在权衡。结合scRNA-seq与不同空间解析技术可提供更完整的组织图谱。本文提出DOT，一种新型多目标优化框架，用于在这些数据模态间转移细胞特征。DOT灵活，可用于推断分类特征（如细胞类型或状态）或连续特征（如基因表达）。我们的优化模型结合了组织组成、技术影响和先验知识整合等实际因素，从而灵活结合scRNA-seq与低/高分辨率空间数据。基于Frank-Wolfe算法的快速实现，在高/低分辨率空间数据中定位细胞特征和估计未测基因表达方面达到最先进的性能。DOT免费提供，无需大计算资源即可部署；典型案例研究可在笔记本电脑上运行，便于使用。
英文摘要：
Single-cell RNA sequencing (scRNA-seq) and spatially-resolved imaging/sequencing technologies have revolutionized biomedical research. On one hand, scRNA-seq provides information about a large portion of the transcriptome for individual cells, but lacks the spatial context. On the other hand, spatially-resolved measurements come with a trade-off between resolution and gene coverage. Combining scRNA-seq with different spatially-resolved technologies can thus provide a more complete map of tissues with enhanced cellular resolution and gene coverage. Here, we propose DOT, a novel multi-objective optimization framework for transferring cellular features across these data modalities. DOT is flexible and can be used to infer categorical (cell type or cell state) or continuous features (gene expression) in different types of spatial omics. Our optimization model combines practical aspects related to tissue composition, technical effects, and integration of prior knowledge, thereby providing flexibility to combine scRNA-seq and both low- and high-resolution spatial data. Our fast implementation based on the Frank-Wolfe algorithm achieves state-of-the-art or improved performance in localizing cell features in high- and low-resolution spatial data and estimating the expression of unmeasured genes in low-coverage spatial data across different tissues. DOT is freely available and can be deployed efficiently without large computational resources; typical cases-studies can be run on a laptop, facilitating its use.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
