Evaluation of the performance of classification algorithms for XFEL single-particle imaging data
Category
Published on
Type
journal-article
Author
Yingchen Shi and Ke Yin and Xuecheng Tai and Hasan DeMirci and Ahmad Hosseinizadeh and Brenda G. Hogue and Haoyuan Li and Abbas Ourmazd and Peter Schwander and Ivan A. Vartanyants and Chun Hong Yoon and Andrew Aquila and Haiguang Liu
Citation
Shi, Y. et al., 2019. Evaluation of the performance of classification algorithms for XFEL single-particle imaging data. IUCrJ, 6(2), pp.331–340. Available at: http://dx.doi.org/10.1107/s2052252519001854.
Abstract
Using X-ray free-electron lasers (XFELs), it is possible to determine three-dimensional structures of nanoscale particles using single-particle imaging methods. Classification algorithms are needed to sort out the single-particle diffraction patterns from the large amount of XFEL experimental data. However, different methods often yield inconsistent results. This study compared the performance of three classification algorithms: convolutional neural network, graph cut and diffusion map manifold embedding methods. The identified single-particle diffraction data of the PR772 virus particles were assembled in the three-dimensional Fourier space for real-space model reconstruction. The comparison showed that these three classification methods lead to different datasets and subsequently result in different electron density maps of the reconstructed models. Interestingly, the common dataset selected by these three methods improved the quality of the merged diffraction volume, as well as the resolutions of the reconstructed maps.
DOI
Funding
NSF-STC Biology with X-ray Lasers (NSF-1231306)
