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arXiv · 1412.0985

Covariance estimation using conjugate gradient for 3D classification in Cryo-EM

Joakim Andén·Eugene Katsevich·Amit Singer
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Abstract

Classifying structural variability in noisy projections of biological macromolecules is a central problem in Cryo-EM. In this work, we build on a previous method for estimating the covariance matrix of the three-dimensional structure present in the molecules being imaged. Our proposed method allows for incorporation of contrast transfer function and non-uniform distribution of viewing angles, making it more suitable for real-world data. We evaluate its performance on a synthetic dataset and an experimental dataset obtained by imaging a 70S ribosome complex.

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Joakim Andén, Eugene Katsevich, Amit Singer. 2015-02-11. Covariance estimation using conjugate gradient for 3D classification in Cryo-EM. https://doi.org/10.1109/isbi.2015.7163849

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