Latent space visualization

For latent spaces with dimension greater than 2, the distribution of latent encodings were visualized with standard dimensionality reduction techniques such as PCA and UMAP³⁵. PCA projections of latent space particle distributions were computed using the implementation provided by scikit-learn⁵⁰. Two-dimensional UMAP³⁵ embeddings were computed using version 0.4.1 of the Python implementation (https://github.com/lmcinnes/umap) with the default settings of k=15 for the k-nearest neighbors graph and a minimum distance parameter of 0.1. Automated tools to analyze and visualize the latent space given the outputs of model training are provided in the cryoDRGN software.