Clustering and variance maps for cryo-electron tomography using wedge-masked differences.


Cryo-electron tomography provides 3D imaging of frozen hydrated biological samples with nanometer resolution. Reconstructed volumes suffer from low signal-to-noise-ratio (SNR)(1) and artifacts caused by systematically missing tomographic data. Both problems can be overcome by combining multiple subvolumes with varying orientations, assuming they contain identical structures. Clustering (unsupervised classification) is required to ensure or verify population homogeneity, but this process is complicated by the problems of poor SNR and missing data, the factors that led to consideration of multiple subvolumes in the first place. Here, we describe a new approach to clustering and variance mapping in the face of these difficulties. The combined subvolume is taken as an estimate of the true subvolume, and the effect of missing data is computed for individual subvolumes. Clustering and variance mapping then proceed based on differences between expected and observed subvolumes. We show that this new method is faster and more accurate than two current, widely used techniques.

DOI: 10.1016/j.jsb.2011.05.011
Citations per Year

238 Citations

Semantic Scholar estimates that this publication has 238 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Heumann2011ClusteringAV, title={Clustering and variance maps for cryo-electron tomography using wedge-masked differences.}, author={John M. Heumann and Andreas H Hoenger and David N. Mastronarde}, journal={Journal of structural biology}, year={2011}, volume={175 3}, pages={288-99} }