Title: Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching,
Abstract: Cellular Electron CryoTomography (CECT) is a 3D imaging technique that
captures information about the structure and spatial organization of
macromolecular complexes within single cells, in near-native state and at
sub-molecular resolution. Although template matching is often used to locate
macromolecules in a CECT image, it is insufficient as it only measures the
relative structural similarity. Therefore, it is preferable to assess the
statistical credibility of the decision through hypothesis testing, requiring
many templates derived from a diverse population of macromolecular structures.
Due to the very limited number of known structures, we need a generative model
to efficiently and reliably sample pseudo-structures from the complex
distribution of macromolecular structures. To address this challenge, we
propose a novel image-derived approach for performing hypothesis testing for
template matching by constructing generative models using the generative
adversarial network. Finally, we conducted hypothesis testing experiments for
template matching on both simulated and experimental subtomograms, allowing us
to conclude the identity of subtomograms with high statistical credibility and
significantly reducing false positives.