Predicting Future States with Spatial Point Processes in Single Molecule
  Resolution Spatial Transcriptomics
Parisa Boodaghi Malidarreh Biraaj Rout Mohammad Sadegh Nasr Priyanshi Borad Jillur Rahman Saurav Jai Prakash Veerla Kelli Fenelon Theodora Koromila Jacob M. Luber
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.