Supervised Classification Methods for Flash X-ray single particle diffraction Imaging
This addresses data processing bottlenecks for researchers in structural biology using XFELs, but it is incremental as it applies supervised classification to an existing experimental setup.
The paper tackles the problem of classifying high-quality single-molecule diffraction patterns in Flash X-ray single-particle diffraction Imaging (FXI) to handle data contamination and heterogeneity, resulting in methods that match templates within a few milliseconds and can be parallelized to match XFEL repetition rates.
Current Flash X-ray single-particle diffraction Imaging (FXI) experiments, which operate on modern X-ray Free Electron Lasers (XFELs), can record millions of interpretable diffraction patterns from individual biomolecules per day. Due to the stochastic nature of the XFELs, those patterns will to a varying degree include scatterings from contaminated samples. Also, the heterogeneity of the sample biomolecules is unavoidable and complicates data processing. Reducing the data volumes and selecting high-quality single-molecule patterns are therefore critical steps in the experimental set-up. In this paper, we present two supervised template-based learning methods for classifying FXI patterns. Our Eigen-Image and Log-Likelihood classifier can find the best-matched template for a single-molecule pattern within a few milliseconds. It is also straightforward to parallelize them so as to fully match the XFEL repetition rate, thereby enabling processing at site.