Single particle reconstruction methods based on the maximum-likelihood principle are popular because of their ability to produce high resolution structures. However, these algorithms are computationally very expensive, requiring a network of servers. To address this problem, we have developed a new algorithm called SubspaceEM for accelerating maximum-likelihood reconstructions. The speedup is by orders of magnitude, and the new algorithm produces similar quality reconstructions compared to the traditional maximum-likelihood formulation. Our approach uses subspace approximations of the cryo-electron microscopy images and the structure projections, greatly reducing the number of image transformations and comparisons that are computed.
The files include an implementation of the SubspaceEM algorithm. The main script is subspaceEM.m. In addition, a small dataset for testing is included. Please view the readme PDF for further details.
Nicha Dvornek (2022). SubspaceEM: A Fast Maximum-a-posteriori Algorithm for Cryo-EM Single Particle Reconstruction (https://www.mathworks.com/matlabcentral/fileexchange/50091-subspaceem-a-fast-maximum-a-posteriori-algorithm-for-cryo-em-single-particle-reconstruction), MATLAB Central File Exchange. Retrieved .
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