Unsupervised classification of single-particle X-ray diffraction snapshots by spectral clustering.

Single-particle experiments using X-ray Free Electron Lasers produce more than 10(5) snapshots per hour, consisting of an admixture of blank shots (no particle intercepted), and exposures of one or more particles. Experimental data sets also often contain unintentional contamination with different species. We present an unsupervised method able to sort experimental snapshots without recourse to templates, specific noise models, or user-directed learning. The results show 90% agreement with manual classification.

[1]  S. Marchesini,et al.  Single mimivirus particles intercepted and imaged with an X-ray laser , 2011, Nature.

[2]  Georg Weidenspointner,et al.  Femtosecond X-ray protein nanocrystallography , 2011, Nature.

[3]  D. Ratner,et al.  First lasing and operation of an ångstrom-wavelength free-electron laser , 2010 .

[4]  A. H. Walenta,et al.  Large-format, high-speed, X-ray pnCCDs combined with electron and ion imaging spectrometers in a multipurpose chamber for experiments at 4th generation light sources , 2010 .

[5]  J. Bozek AMO instrumentation for the LCLS X-ray FEL , 2009 .

[6]  Ronald R. Coifman,et al.  Graph Laplacian Tomography From Unknown Random Projections , 2008, IEEE Transactions on Image Processing.

[7]  A. Ourmazd,et al.  Crystallography without crystals. I. The common-line method for assembling a three-dimensional diffraction volume from single-particle scattering. , 2008, Acta crystallographica. Section A, Foundations of crystallography.

[8]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[9]  W. H. Benner,et al.  Single particle X-ray diffractive imaging. , 2007, Nano letters.

[10]  J. Kirz,et al.  An assessment of the resolution limitation due to radiation-damage in x-ray diffraction microscopy. , 2005, Journal of electron spectroscopy and related phenomena.

[11]  Pietro Perona,et al.  Self-Tuning Spectral Clustering , 2004, NIPS.

[12]  Bernhard Schölkopf,et al.  A kernel view of the dimensionality reduction of manifolds , 2004, ICML.

[13]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[14]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[15]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Andrew B. Kahng,et al.  New spectral methods for ratio cut partitioning and clustering , 1991, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[17]  B. Mohar THE LAPLACIAN SPECTRUM OF GRAPHS y , 1991 .