Figure 4.

Overview of machine learning strategy. (A) Data pre-processing for insulin and glucagon vesicles from β and α cells is conducted. A final vesicle feature matrix, including group labels (denoting which cell a vesicle is from) for vesicles, is used as input for machine learning. (B) Train/test split for grouping vesicles from α and β cells. The process of model building is described, with Leave One Group Out cross-validation used to estimate the performance of predicting vesicle identity from unseen cells. Model building and testing are repeated over 56 combinations to understand variability in performance.