2025/09/08 by O. Lauzirika, Marek Pernica, David Herreros +10 · 1 voice
Materials Science · Biochemistry, Genetics and Molecular Biology · #Electron and X-Ray Spectroscopy Techniques #Advanced Electron Microscopy Techniques and Applications #Molecular Biology Techniques and Applications
paper · pdf · doi:10.1107/s2059798325007831
openalex publication_date 2025/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
Heterogeneity in cryoEM is essential for capturing the structural variability of macromolecules, reflecting their functional states and biological significance. However, estimating heterogeneity remains challenging due to particle misclassification and algorithmic biases, which can lead to reconstructions that blend distinct conformations or fail to resolve subtle differences. Furthermore, the low signal-to-noise ratio inherent in cryo-EM data makes it nearly impossible to detect minute structural changes, as noise often obscures subtle variations in macromolecular projections. In this paper, we investigate the use of p-values associated with the null hypothesis that the observed classification differs from a random partition of the input data set, thereby providing a statistical framework for determining the number of distinguishable classes present in a given data set.