Evaluation of the performance of classification algorithms for XFEL single-particle imaging data

Yingchen Shi, Ke Yin, Xue-Cheng TAI, Hasan DeMirci, Ahmad Hosseinizadeh, Brenda G. Hogue, Haoyuan Li, Abbas Ourmazd, Peter Schwander, Ivan A. Vartanyants, Chun Hong Yoon, Andrew Aquila*, Haiguang Liu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

19 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)331-340
Number of pages10
Publication statusPublished - Mar 2019

Scopus Subject Areas

  • Chemistry(all)
  • Biochemistry
  • Materials Science(all)
  • Condensed Matter Physics

User-Defined Keywords

  • classification algorithms
  • electron-density map reconstruction
  • single-particle imaging
  • X-ray free-electron lasers (XFELs)


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