ArticleIUCrJ2022
Unsupervised learning approaches to characterizing heterogeneous samples using X-ray single-particle imaging.
Article in IUCrJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- High-throughput in situ single particle X-ray imaging of dehydrating viral capsids.Light, science & applications · 2026Article
- Direct Observation of the Exciton-Polaron in Single CsPbBrACS nano · 2025Article
- Scalable 3D reconstruction for X-ray single particle imaging with online machine learning.Nature communications · 2025Article
- Advancing time-resolved structural biology: latest strategies in cryo-EM and X-ray crystallography.Nature methods · 2025Review
- Application of self-supervised approaches to the classification of X-ray diffraction spectra during phase transitions.Scientific reports · 2023Article
- Finding the semantic similarity in single-particle diffraction images using self-supervised contrastive projection learning.npj computational materials · 2023Article
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Authors and funding
42 authors.
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Abstract
One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand-maximize-compress (
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