Heterogeneotasunaren analisi azkar eta automatizatua krio-mikroskopia elektronikoan

Abstract

Understanding structural heterogeneity is a key challenge in Cryo-Electron Microscopy (Cryo-EM). We propose a novel method that leverages the natural similarity between neighboring projection directions to perform a localized Principal Component Analysis (PCA) in directional subsets of the data. These local analyses are then globally synchronized using Orthogonal Group Synchronization, a novel field in applied mathematics. Our approach is designed to pose very few execution parametes, reducing the need for extensive user tuning. The parameters involved have clear physical and statistical interpretations, enhancing their usability and transparency. In addition, it is also computationally efficient, achieving significantly faster execution times compared to conventional heterogeneity analysis methods. We validate our approach on both simulated and experimental datasets exhibiting compositional and conformational heterogeneity, demonstrating its effectiveness in capturing structural variability.