Predicting Future States with Spatial Point Processes in Single Molecule Resolution Spatial Transcriptomics
This provides a tool for spatially resolved developmental biology, analogous to RNA Velocity, but is incremental as it applies existing methods to new data.
The authors tackled the problem of predicting future gene expression distributions in Drosophila embryogenesis using spatial point processes and XGBoost, achieving an average predictive accuracy for active cell distribution.
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.