title: Unsupervised classification of single particles by cluster tracking in multi-dimensional space.
abstract: In cryo-electron microscopy (cryo-EM) single-particle reconstruction, the heterogeneity of two-dimensional projection image data resulting from the co-existence of different conformational or ligand binding states of a macromolecular complex remains a major obstacle as it impairs the validity of reconstructed density maps and limits the progress toward higher resolution. Classification of cryo-EM data according to the different conformations is difficult because of the coexistence of multiple orientations in a single dataset. Here, we present an unsupervised classification method, termed cluster tracking, which utilizes the continuity in multi-dimensional space induced by angular adjacency of projections in large datasets. In a proof of concept, the testing of cluster tracking on simulated projection data, which were generated from multiple conformations and orientations of an existing volume, produced clusters that are consistent with the conformational identity of the data. The application of the method to experimental cryo-EM projection data is found to result in a partition similar to the one generated by supervised classification.
authors.affiliation: Department of Biomedical Sciences, State University of New York at Albany, Empire State Plaza, Albany, NY 12201-0509, USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Fu
authors.forename: Haixiao
authors.initials: H
authors.lastname: Gao
authors.forename: Joachim
authors.initials: J
authors.lastname: Frank
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D020285:Cryoelectron Microscopy / Q000379:methods*; D005240:Feasibility Studies; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008958:Models, Molecular; D010363:Pattern Recognition, Automated / Q000379:methods*; D011487:Protein Conformation; D012343:RNA, Transfer / Q000737:chemistry
chemical_list: D012343:RNA, Transfer