The field of cryo-electron microscopy has made astounding advancements in the past few years, mainly due to improvements in the hardware of the microscopes . 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, one must first classify their images into homogeneous groups , where each group corresponds to the same underlying structure , followed by reconstruction of a three-dimensional model from each of the homogeneous groups. This task has been proven to be extremely difficult . In this paper we present an iterative algorithm for processing heterogeneous data sets  that combines the classification and reconstruction steps. We prove accuracy and stability bounds on the algorithm, and demonstrate it on simulated as well as experimental datasets .
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 .
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improvements in the hardware of the microscopes
advancements in electron detectors' technology
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one must first classify their images into homogeneous groups , where each group corresponds
the algorithms must include some classification procedure to identify homogeneous groups within the data, so that the images in each group correspond
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, followed by reconstruction of a three-dimensional model from each of the homogeneous groups. This task has been proven to be extremely difficult
. 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
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we present an iterative
, 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
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, and prove accuracy and stability bounds for it. We also suggest an extension of this algorithm
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prove accuracy and stability bounds on the algorithm, and
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as well as experimental datasets
data, and compare its performance to the state-of-the-art algorithm in RELION