Supervised classification methods for flash X-ray single particle diffraction imaging.

Abstract

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 practical limitations with the FXI technology, 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 setup. 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 match the XFEL repetition rate fully, thereby enabling processing at site. The methods perform in a stable way on various kinds of synthetic data. As a practical example we tested our methods on a real mimivirus dataset, obtaining a convincing classification accuracy of 0.9.