Towards unsupervised classification of macromolecular complexes in cryo electron tomography: Challenges and opportunities

Background and Objectives: Cryo electron tomography visualizes native cells at nanometer resolution, but analysis is challenged by noise and artifacts. Recently, supervised deep learning methods have been ap-plied to decipher the 3D spatial distribution of macromolecules. However, in order to discover unknown objects, unsupervised classification techniques are necessary. In this paper, we provide an overview of unsupervised deep learning techniques, discuss the challenges to analyze cryo-ET data, and provide a proof-of-concept on real data.Methods: We propose a weakly supervised subtomogram classification method based on transfer learning. We use a deep neural network to learn a clustering friendly representation able to capture 3D shapes in the presence of noise and artifacts. This representation is learned here from a synthetic data set.Results: We show that when applying k-means clustering given a learning-based representation, it be-comes possible to satisfyingly classify real subtomograms according to structural similarity. It is worth noting that no manual annotation is used for performing classification. Conclusions: We describe the advantages and limitations of our proof-of-concept and raise several per-spectives for improving classification performance.(c) 2022 Published by Elsevier B.V.

Automated summary

This paper presents a weakly supervised subtomogram classification method based on transfer learning, using a deep neural network to learn a clustering friendly representation capable of capturing 3D shapes in the presence of noise and artifacts. By applying k-means clustering to a learning-based representation, real subtomograms can be classified satisfactorily according to structural similarity without manual annotation.