Unsupervised cryo-EM data clustering through adaptively constrained K-means algorithm

TL;DR

This paper introduces an adaptive constrained K-means clustering algorithm tailored for cryo-EM data, improving accuracy and class size balance in unsupervised 2D classification of noisy biological images.

Contribution

It presents a novel clustering method with an adaptive constraint that enhances classification accuracy and class size consistency in cryo-EM data analysis.

Findings

Improved clustering accuracy on simulated cryo-EM data

Better class size balance in experimental datasets

Significant performance improvement over traditional K-means

Abstract

In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis.