In this paper, we introduce a pipeline based on Random Forest Regression 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. A Random Forest Regression 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.

利用基于随机森林回归的流程，本研究预测果蝇胚胎发育过程中Sog-D基因表达的细胞未来分布，以揭示细胞和有机体在超分辨全胚空间转录组成像中如何控制基因表达；通过结合细胞在胚胎发育的每个阶段的状态和Ripley's K-function，我们得到了平均活跃细胞分布的预测准确性，该工具类似于空间分辨的发育生物学RNA速度，通过一个数据点可以利用空间点过程的特征预测未来的空间基因表达。

在单分子分辨率的空间转录组学中使用空间点过程预测未来状态