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

Yoon, Chun Hong; Schwander, Peter; Abergel, Chantal; Andersson, Inger; Andreasson, Jakob; Aquila, Andrew; Bajt, Saša; Barthelmeß, Miriam; Barty, Anton; Bogan, Michael J.; Bostedt, Christoph; Bozek, John D.; Chapman, Henry N.; Claverie, Jean‐Michel; Coppola, N.; DePonte, Daniel P.; Ekeberg, Tomas; Epp, Sascha W.; Erk, Benjamin; Fleckenstein, H.; Foucar, L.; Graafsma, H.; Gumprecht, Lars; Hajdu, János; Hampton, Christina Y.; Hartmann, Andreas; Hartmann, Elisabeth; Hartmann, R.; Hauser, Günter; Hirsemann, H.; Holl, P.; Kassemeyer, Stephan; Kimmel, Nils; Кискинова, М.; Liang, Mao; Loh, N. Duane; Lomb, Lukas; Maia, Filipe R. N. C.; Martin, Andrew V.; Nass, Karol; Pedersoli, Emanuele; Reich, Christian; Rolles, Daniel; Rudek, Benedikt; Rudenko, Artem; Schlichting, Ilme; Schulz, Joachim; Seibert, M. Marvin; Seltzer, Virginie; Shoeman, Robert L.; Sierra, Raymond G.; Soltau, H.; Starodub, D.; Steinbrener, Jan; Stier, Gunter; Strüder, L.; Svenda, Martin; Ullrich, J.; Weidenspointner, G.; White, Thomas A.; Wunderer, C.; Ourmazd, A.

doi:10.1364/oe.19.016542

Cited by 96 publications

(65 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This effect explains the rather narrow size distribution observed e.g. in size selected polystyrene latex spheres (Loh unpublished) and mimivirus, which was identified as a member of a single class by classification algorithms [13] and showed the expected size of the strongly scattering capsid [4,14]. We find this to be true for most samples except for small biological objects, like enterobacteria phage T4.…”

Section: Resultssupporting

confidence: 51%

See 1 more Smart Citation

Femtosecond free-electron laser x-ray diffraction data sets for algorithm development

Kassemeyer¹,

Steinbrener²,

Lomb³

et al. 2012

Opt. Express

Self Cite

View full text Add to dashboard Cite

Abstract:We describe femtosecond X-ray diffraction data sets of viruses and nanoparticles collected at the Linac Coherent Light Source. The data establish the first large benchmark data sets for coherent diffraction methods freely available to the public, to bolster the development of algorithms that are essential for developing this novel approach as a useful imaging technique. Applications are 2D reconstructions, orientation classification and finally 3D imaging by assembling 2D patterns into a 3D diffraction volume. ©2012 Optical Society of America References and links1. R. Neutze, R. Wouts, D. van der Spoel, E. Weckert, and J. Hajdu, "Potential for biomolecular imaging with femtosecond X-ray pulses," Nature 406(6797), 752-757 (2000

show abstract

Section: Resultssupporting

confidence: 51%

“…Details will be published elsewhere. In addition, diffraction patterns have been classified in an unsupervised manner [13]. Lists of individual frames belonging to single classes of samples have also been included in the CXIDB deposition.…”

Section: Resultsmentioning

confidence: 99%

Femtosecond free-electron laser x-ray diffraction data sets for algorithm development

Kassemeyer¹,

Steinbrener²,

Lomb³

et al. 2012

Opt. Express

Self Cite

View full text Add to dashboard Cite

show abstract

“…Subsequent frame analysis by particle efficiently rejects outliers from the homogenous particle class desired for single particle imaging, thus reducing data volumes by another order of magnitude. Data vetoed by these hit finding criteria can be used as the input to more computationally intensive sorting algorithms [20] that are not yet fast enough for processing data in near-real time.…”

Section: Discussionmentioning

confidence: 99%

Automated identification and classification of single particle serial femtosecond X-ray diffraction data

Andreasson¹,

Martin²,

Liang³

et al. 2014

Opt. Express

Self Cite

View full text Add to dashboard Cite

Abstract:The first hard X-ray laser, the Linac Coherent Light Source (LCLS), produces 120 shots per second. Particles injected into the X-ray beam are hit randomly and in unknown orientations by the extremely intense X-ray pulses, where the femtosecond-duration X-ray pulses diffract from the sample before the particle structure is significantly changed even though the sample is ultimately destroyed by the deposited X-ray energy. Single particle X-ray diffraction experiments generate data at the FEL repetition rate, resulting in more than 400,000 detector readouts in an hour, the data stream during an experiment contains blank frames mixed with hits on single particles, clusters and contaminants. The diffraction signal is generally weak and it is superimposed on a low but continually fluctuating background signal, originating from photon noise in the beam line and electronic noise from the detector. Meanwhile, explosion of the sample creates fragments with a characteristic signature. Here, we describe methods based on rapid image analysis combined with ion Time-of-Flight (ToF) spectroscopy of the fragments to achieve an efficient, automated and unsupervised sorting of diffraction data. The studies described here form a basis for the development of real-time frame rejection methods, e.g. for the European XFEL, which is expected to produce 100 million pulses per hour. ©2014 Optical Society of America

show abstract

“…Classification work includes manifold mapping (6), spectral clustering (7), principal component analysis, and support vector machines (8). Orientation methods include common curve approaches (9)(10)(11)(12), expectation maximization (13)(14)(15), and manifold embedding (16)(17)(18)(19).…”

mentioning

confidence: 99%

Reconstruction from limited single-particle diffraction data via simultaneous determination of state, orientation, intensity, and phase

Donatelli

Sethian

Zwart

2017

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Free-electron lasers now have the ability to collect X-ray diffraction patterns from individual molecules; however, each sample is delivered at unknown orientation and may be in one of several conformational states, each with a different molecular structure. Hit rates are often low, typically around 0.1%, limiting the number of useful images that can be collected. Determining accurate structural information requires classifying and orienting each image, accurately assembling them into a 3D diffraction intensity function, and determining missing phase information. Additionally, single particles typically scatter very few photons, leading to high image noise levels. We develop a multitiered iterative phasing algorithm to reconstruct structural information from singleparticle diffraction data by simultaneously determining the states, orientations, intensities, phases, and underlying structure in a single iterative procedure. We leverage real-space constraints on the structure to help guide optimization and reconstruct underlying structure from very few images with excellent global convergence properties. We show that this approach can determine structural resolution beyond what is suggested by standard Shannon sampling arguments for ideal images and is also robust to noise.single-particle imaging | multitiered iterative phasing | structure determination S ingle-particle X-ray diffraction aims to determine the structures of biological molecules from an ensemble of diffraction patterns, each collected from a single particle per shot. Particles are delivered to the beam at random orientations through either a liquid medium (1) or aerosolization (2). Large-scale experimental facilities, such as the Linac Coherent Light Source (3), have been developed to perform these experiments, with initial results indicating the feasibility of single-particle diffraction as a viable technique to address challenges in the health, material, and energy sciences (4, 5).A fundamental challenge in single-particle imaging is that the orientation of each imaged particle is unknown and must be recovered to determine structural information. Additionally, many biological samples display conformational flexibility and may exist in one of many possible structural states. To account for varying structural states and avoid a loss of resolution due to averaging of states, the diffraction patterns may need to be classified to the correct state. Furthermore, single particles scatter very few photons; hence the images are heavily contaminated by shot noise, often with less than a photon per Shannon pixel at high scattering angles.Current approaches typically determine states, orientations, and structures in separate consecutive steps. Classification work includes manifold mapping (6), spectral clustering (7), principal component analysis, and support vector machines (8). Orientation methods include common curve approaches (9-12), expectation maximization (13-15), and manifold embedding (16)(17)(18)(19). Once images are classified, oriented, and assemb...

show abstract

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

Cited by 96 publications

References 16 publications

Femtosecond free-electron laser x-ray diffraction data sets for algorithm development

Femtosecond free-electron laser x-ray diffraction data sets for algorithm development

Automated identification and classification of single particle serial femtosecond X-ray diffraction data

Reconstruction from limited single-particle diffraction data via simultaneous determination of state, orientation, intensity, and phase

Contact Info

Product

Resources

About