Extended Data Fig. 2: Random selection of tilts per epoch allows flexible and robust model training for datasets with non-uniform numbers of tilt-images per particle.

(a) Graphical summary of a dataset with non-uniform numbers of tilt images per particle. Here, the minimum number of tilt images for any particle is 3. (b) Corresponding tomoDRGN network architecture for random sampling and ordering of 3 tilt images per particle. (c) Mean per-class volumetric correlation coefficient for identical tomoDRGN models trained on 41 sequentially sampled tilts (top) or 41 randomly sampled tilts (bottom). At 5 epoch intervals, 25 random volumes were generated from each class for correlation coefficient calculation to ground truth ribosome assembly intermediate volumes (classes B-E). Error bars denote standard error of the mean CC. (d) Nine tomoDRGN models with identical architectures were trained with the indicated number of tilts sampled per particle (total available tilts = 41). PCA (left) and UMAP (right) dimensionality reduction of each final epoch’s latent embeddings. Once trained, up to 10 randomly sampled and permuted tilt images for one representative particle from each volume class were embedded using the corresponding pretrained tomoDRGN model and are superimposed as colored points. Note increased dispersion of colored points as number of tilts sampled during training decreased. (e) For each ribosomal large subunit class (B-E), 25 particles were randomly selected and up to 10 subsets of their tilt images were randomly sampled and permuted as in (d). In the heatmap, row indices refer to models trained in (d) using different numbers of sampled tilts (1-41), and columns denote epochs of training with that model. For each particle, each tilt subset was evaluated with the corresponding tomoDRGN model and the ratio of standard deviations of each particle’s 10 latent embeddings to all particles’ latent embeddings was calculated. The mean ratio across all particles, which measures the dispersion of encoder embeddings, is plotted per ribosomal LSU class. Here, lower dispersion indicates better performance. (f) Particles and tilt subsets were selected as in (e). At each indicated epoch of training, the corresponding tomoDRGN model was used to generate volumes for each particle’s tilt subsets. For each such volume, the correlation coefficient was calculated between that volume and the corresponding ground truth volume. The mean across all particles at each epoch for each model is shown as a heatmap per ribosomal LSU class. Here, higher CC indicates improved performance.

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