Cryo-em images are intrinsically low dimensional
This work addresses the challenge of interpreting latent spaces in cryo-EM for researchers in structural biology, providing insights that could enhance inference strategies, though it is incremental as it builds on existing CryoSBI methods.
The study tackled the problem of understanding the geometric structure of latent representations in cryo-electron microscopy by applying manifold learning to CryoSBI data, revealing that high-dimensional data inherently occupy low-dimensional, smooth manifolds with simulated data covering experimental data.
Simulation-based inference provides a powerful framework for cryo-electron microscopy, employing neural networks in methods like CryoSBI to infer biomolecular conformations via learned latent representations. This latent space represents a rich opportunity, encoding valuable information about the physical system and the inference process. Harnessing this potential hinges on understanding the underlying geometric structure of these representations. We investigate this structure by applying manifold learning techniques to CryoSBI representations of hemagglutinin (simulated and experimental). We reveal that these high-dimensional data inherently populate low-dimensional, smooth manifolds, with simulated data effectively covering the experimental counterpart. By characterizing the manifold's geometry using Diffusion Maps and identifying its principal axes of variation via coordinate interpretation methods, we establish a direct link between the latent structure and key physical parameters. Discovering this intrinsic low-dimensionality and interpretable geometric organization not only validates the CryoSBI approach but enables us to learn more from the data structure and provides opportunities for improving future inference strategies by exploiting this revealed manifold geometry.