利用空间点过程在单分子分辨率空间转录组学中预测未来状态
Predicting Future States with Spatial Point Processes in Single Molecule Resolution Spatial Transcriptomics
AI总结：
本文提出基于XGBoost和空间点过程（Ripley's K函数）的流程，在单分子分辨率空间转录组学中预测果蝇胚胎Sog-D活跃细胞在AP和DV轴的未来分布，实现类似RNA Velocity的发育生物学预测。
摘要：
在本文中，我们引入了一种基于XGBoost的流程，用于预测果蝇胚胎发生过程中Sog-D基因表达的细胞（活跃细胞）在前后轴（AP）和背腹轴（DV）上的未来分布。该方法为细胞和生物体如何在超分辨率全胚胎空间转录组学成像中，于亚细胞、单分子分辨率下控制基因表达提供了见解。使用XGBoost模型基于前一阶段预测下一阶段的活跃分布。为实现这一目标，我们利用时间分辨的空间点过程，将Ripley's K函数与胚胎发生各阶段的细胞状态结合使用，并发现了活跃细胞分布的平均预测准确率。该工具类似于用于空间分辨发育生物学的RNA Velocity，从单个数据点出发，我们可以利用空间点过程的特征预测未来空间分辨的基因表达。
英文摘要：
In this paper, we introduce a pipeline based on XGboost to predict the future distribution of cells that are expressed by the Sog-D gene (active cells) in both the Anterior to posterior (AP) and the Dorsal to Ventral (DV) axis of the Drosophila in embryogenesis process. This method provides insights about how cells and living organisms control gene expression in super resolution whole embryo spatial transcriptomics imaging at sub cellular, single molecule resolution. An XGboost model was used to predict the next stage active distribution based on the previous one. To achieve this goal, we leveraged temporally resolved, spatial point processes by including Ripley's K-function in conjunction with the cell's state in each stage of embryogenesis, and found average predictive accuracy of active cell distribution. This tool is analogous to RNA Velocity for spatially resolved developmental biology, from one data point we can predict future spatially resolved gene expression using features from the spatial point processes.
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。
