A max-cut approach to heterogeneity in cryo-electron microscopy
This addresses the lack of rigorous guarantees in classification algorithms for heterogeneous cryo-EM data, which is crucial for structural biology but incremental in providing mathematical foundations.
The paper tackles the problem of processing heterogeneous data sets in cryo-electron microscopy by developing an algorithm with rigorous mathematical analysis, proving accuracy and stability bounds, and demonstrating competitive performance on simulated data compared to the state-of-the-art RELION algorithm.
The field of cryo-electron microscopy has made astounding advancements in the past few years, mainly due to advancements in electron detectors' technology. Yet, one of the key open challenges of the field remains the processing of heterogeneous data sets, produced from samples containing particles at several different conformational states. For such data sets, the algorithms must include some classification procedure to identify homogeneous groups within the data, so that the images in each group correspond to the same underlying structure. The fundamental importance of the heterogeneity problem in cryo-electron microscopy has drawn many research efforts, and resulted in significant progress in classification algorithms for heterogeneous data sets. While these algorithms are extremely useful and effective in practice, they lack rigorous mathematical analysis and performance guarantees. In this paper, we attempt to make the first steps towards rigorous mathematical analysis of the heterogeneity problem in cryo-electron microscopy. To that end, we present an algorithm for processing heterogeneous data sets, and prove accuracy and stability bounds for it. We also suggest an extension of this algorithm that combines the classification and reconstruction steps. We demonstrate it on simulated data, and compare its performance to the state-of-the-art algorithm in RELION.