  Motivation: Cellular Electron CryoTomography (CECT) enables 3D visualization
of cellular organization at near-native state and in sub-molecular resolution,
making it a powerful tool for analyzing structures of macromolecular complexes
and their spatial organizations inside single cells. However, high degree of
structural complexity together with practical imaging limitations make the
systematic de novo discovery of structures within cells challenging. It would
likely require averaging and classifying millions of subtomograms potentially
containing hundreds of highly heterogeneous structural classes. Although it is
no longer difficult to acquire CECT data containing such amount of subtomograms
due to advances in data acquisition automation, existing computational
approaches have very limited scalability or discrimination ability, making them
incapable of processing such amount of data.
  Results: To complement existing approaches, in this paper we propose a new
approach for subdividing subtomograms into smaller but relatively homogeneous
subsets. The structures in these subsets can then be separately recovered using
existing computation intensive methods. Our approach is based on supervised
structural feature extraction using deep learning, in combination with
unsupervised clustering and reference-free classification. Our experiments show
that, compared to existing unsupervised rotation invariant feature and
pose-normalization based approaches, our new approach achieves significant
improvements in both discrimination ability and scalability. More importantly,
our new approach is able to discover new structural classes and recover
structures that do not exist in training data.
