Graduation Semester and Year

Spring 2024



Document Type


Degree Name

Master of Science in Computer Science


Computer Science and Engineering

First Advisor

Jacob Luber

Second Advisor

Ming Li

Third Advisor

Rémi A. Chou


In this thesis, we present an innovative framework centered around the application of Random Forest Regression to forecast the prospective distribution of cells expressing the Sog-D gene (active cells) during the embryogenesis process in Drosophila. Our methodology specifically targets the Anterior-to-posterior (AP) and Dorsal-to-Ventral (DV) axes, unraveling the intricacies of gene expression control in living organisms at super-resolution, single-molecule resolution through whole embryo spatial transcriptomics imaging. The Random Forest Regression model serves as a pivotal tool in predicting the succeeding stage’s active cell distribution, capitalizing on the insights obtained from the preceding stage. We integrate temporally resolved, spatial point processes into our analysis, incorporating Ripley’s K-function alongside the cell’s state at each embryogenesis stage. Our approach yields an average predictive accuracy for active cell distribution, providing a valuable tool akin to RNA Velocity for spatially resolved developmental biology. This framework empowers researchers to extrapolate future spatially resolved gene expression from a singular data point, leveraging features derived from spatial point processes. Through this thesis, we contribute to advancing the understanding of developmental biology, offering a robust methodology for predicting gene expression dynamics at sub-cellular resolutions.


Random Forest, Regression, Dorpsophila, Sog-D, Ripley’s K-function, Transcriptomics, Embryogenesis



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