Abstract
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 applied 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 an unsupervised sub-tomogram 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 becomes possible to satisfyingly classify real sub-tomograms 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 perspectives for improving classification performance.
Competing Interest Statement
The authors have declared no competing interest.
