Wu, JiayiMa, Yong-BeiCongdon, CharlesBrett, BevinChen, ShuobingXu, YaofangOuyang, QiMao, Youdong2017-11-212017Wu, Jiayi, Yong-Bei Ma, Charles Congdon, Bevin Brett, Shuobing Chen, Yaofang Xu, Qi Ouyang, and Youdong Mao. 2017. “Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.” PLoS ONE 12 (8): e0182130. doi:10.1371/journal.pone.0182130. http://dx.doi.org/10.1371/journal.pone.0182130.http://nrs.harvard.edu/urn-3:HUL.InstRepos:34375025Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.en-USBiology and Life SciencesImmunologyImmune System ProteinsInflammasomesMedicine and Health SciencesBiochemistryProteinsMicroscopyElectron MicroscopyElectron Cryo-MicroscopyComputational TechniquesSplit-Decomposition MethodMultiple Alignment CalculationPhysical SciencesMathematicsApplied MathematicsAlgorithmsSimulation and ModelingMachine Learning AlgorithmsComputer and Information SciencesArtificial IntelligenceMachine LearningImaging TechniquesOptimizationJournal Article2017-11-2110.1371/journal.pone.0182130