Unsupervised single-particle deep clustering via statistical manifold learning

by   Jiayi Wu, et al.

Motivation: 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. 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 cost in computation. Overcoming these limitations requires further development on clustering algorithms for high-performance cryo-EM data analysis. Results: Here we introduce a statistical manifold learning algorithm for unsupervised single-particle deep clustering. We show that statistical manifold learning improves classification accuracy by about 40 references for lower SNR data. Applications to several experimental datasets suggest that our deep clustering approach can detect subtle structural difference among classes. Through code optimization over the Intel high-performance computing (HPC) processors, our software implementation can generate thousands of reference-free class averages within several hours from hundreds of thousands of single-particle cryo-EM images, which allows significant improvement in ab initio 3D reconstruction resolution and quality. Our approach has been successfully applied in several structural determination projects. We expect that it provides a powerful computational tool in analyzing highly heterogeneous structural data and assisting in computational purification of single-particle datasets for high-resolution reconstruction.



There are no comments yet.


page 1

page 2

page 3

page 5

page 15

page 20

page 26

page 28


Unsupervised particle sorting for high-resolution single-particle cryo-EM

Single-particle cryo-Electron Microscopy (EM) has become a popular techn...

Mahalanobis Distance for Class Averaging of Cryo-EM Images

Single particle reconstruction (SPR) from cryo-electron microscopy (EM) ...

Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes

Single-particle electron cryomicroscopy is an essential tool for high-re...

Deep manifold learning reveals hidden dynamics of proteasome autoregulation

The 2.5-MDa 26S proteasome maintains proteostasis and regulates myriad c...

CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images

Cryo-electron microscopy (cryo-EM) has become a tool of fundamental impo...

Cryo-ZSSR: multiple-image super-resolution based on deep internal learning

Single-particle cryo-electron microscopy (cryo-EM) is an emerging imagin...

KLT Picker: Particle Picking Using Data-Driven Optimal Templates

Particle picking is currently a critical step in the cryo-EM single part...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.