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

MPG-Autoren
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Erk,  Benjamin
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Foucar,  Lutz
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Hartmann,  Elisabeth
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Kassemeyer,  Stephan
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Lomb,  Lukas
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Nass,  Karol
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Rolles,  Daniel
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Rudek,  Benedikt
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Schlichting,  Ilme
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Shoeman,  Robert L.
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Steinbrener,  Jan
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Stier,  Gunter
Department of Biomolecular Mechanisms, Max Planck Institute for Medical Research, Max Planck Society;

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Zitation

Yoon, C. H., Schwander, P., Abergel, C., Andersson, I., Andreasson, J., Aquila, A., et al. (2011). Unsupervised classification of single-particle X-ray diffraction snapshots by spectral clustering. Optics Express, 19(17), 16542-16549. doi:10.1364/OE.19.016542.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0024-20D5-3
Zusammenfassung
Single−particle experiments using X−ray Free Electron Lasers produce more than 105 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