Covariance matrix estimation for the cryo-em heterogeneity problem

E. Katsevich, A. Katsevich, A. Singer

Research output: Contribution to journalArticle

24 Scopus citations

Abstract

In cryo-electron microscopy (cryo-EM), a microscope generates a top view of a sample of randomly oriented copies of a molecule. The problem of single particle reconstruction (SPR) from cryo-EM is to use the resulting set of noisy two-dimensional projection images taken at unknown directions to reconstruct the three-dimensional (3D) structure of the molecule. In some situations, the molecule under examination exhibits structural variability, which poses a fundamental challenge in SPR. The heterogeneity problem is the task of mapping the space of conformational states of a molecule. It has been previously suggested that the leading eigenvectors of the covariance matrix of the 3D molecules can be used to solve the heterogeneity problem. Estimating the covariance matrix is challenging, since only projections of the molecules are observed, but not the molecules themselves. In this paper, we formulate a general problem of covariance estimation from noisy projections of samples. This problem has intimate connections with matrix completion problems and high-dimensional principal component analysis. We propose an estimator and prove its consistency. When there are finitely many heterogeneity classes, the spectrum of the estimated covariance matrix reveals the number of classes. The estimator can be found as the solution to a certain linear system. In the cryo-EM case, the linear operator to be inverted, which we term the projection covariance transform, is an important object in covariance estimation for tomographic problems involving structural variation. Inverting it involves applying a filter akin to the ramp filter in tomography. We design a basis in which this linear operator is sparse and thus can be tractably inverted despite its large size. We demonstrate via numerical experiments on synthetic datasets the robustness of our algorithm to high levels of noise.

Original languageEnglish (US)
Pages (from-to)126-185
Number of pages60
JournalSIAM Journal on Imaging Sciences
Volume8
Issue number1
DOIs
StatePublished - Jan 22 2015

All Science Journal Classification (ASJC) codes

  • Mathematics(all)
  • Applied Mathematics

Keywords

  • Classification
  • Covariance matrix estimation
  • Cryo-electron microscopy
  • Fourier projection slice theorem
  • Heterogeneity
  • High-dimensional statistics
  • Inverse problems
  • Principal component analysis
  • Spherical harmonics
  • Structural variability
  • X-ray transform

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