ArticlePloS one2017
Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- The membrane-proximal external region of human immunodeficiency virus (HIV-1) envelope glycoprotein trimers in A18-lipid nanodiscs.Communications biology · 2025Article
- Simcryocluster: a semantic similarity clustering method of cryo-EM images by adopting contrastive learning.BMC bioinformatics · 2024Article
- Determining subunit-subunit interaction from statistics of cryo-EM images: observation of nearest-neighbor coupling in a circadian clock protein complex.Nature communications · 2023Article
- Article
- Asymmetric conformations of cleaved HIV-1 envelope glycoprotein trimers in styrene-maleic acid lipid nanoparticles.Communications biology · 2023Article
- An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Images Based on Autoencoders.International journal of molecular sciences · 2023Article
- Emerging Themes in CryoEM─Single Particle Analysis Image Processing.Chemical reviews · 2022Review
- Visualizing Conformational Space of Functional Biomolecular Complexes by Deep Manifold Learning.International journal of molecular sciences · 2022Article
- Article
- Probing Structural Perturbation of Biomolecules by Extracting Cryo-EM Data Heterogeneity.Biomolecules · 2022Review
- Asymmetric Structures and Conformational Plasticity of the Uncleaved Full-Length Human Immunodeficiency Virus Envelope Glycoprotein Trimer.Journal of virology · 2021Article
- Structure, Dynamics and Function of the 26S Proteasome.Sub-cellular biochemistry · 2021Review
- CryoEM structure ofLife science alliance · 2020Article
- AAA+ ATPases in Protein Degradation: Structures, Functions and Mechanisms.Biomolecules · 2020Review
- CDAE: A Cascade of Denoising Autoencoders for Noise Reduction in the Clustering of Single-Particle Cryo-EM Images.Frontiers in genetics · 2020Article
- Yeast R2TP Interacts with Extended Termini of Client Protein Nop58p.Scientific reports · 2019Article
- Securing the future of research computing in the biosciences.PLoS computational biology · 2019Article
- Robustness of signal detection in cryo-electron microscopy via a bi-objective-function approach.BMC bioinformatics · 2019Article
- Article
- New software tools in EMAN2 inspired by EMDatabank map challenge.Journal of structural biology · 2018Article
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8 authors.
Funding
Abstract
Structural 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.