Diagnosing Heterogeneous Dynamics in Single Molecule/Particle Trajectories with Multiscale Wavelets

TL;DR

This paper introduces a multiscale wavelet-based method for automatically detecting and quantifying transient heterogeneous dynamics in large single molecule/particle tracking datasets, applicable across various scientific fields.

Contribution

The paper presents a novel, computationally efficient wavelet transform approach with adaptive thresholding to analyze heterogeneous dynamics without arbitrary thresholds.

Findings

Effective detection of confined diffusion and hopping events

Applicable to cell biology, biotechnology, and colloid physics

Can process hundreds of millions of data points within an hour

Abstract

We describe a simple automated method to extract and quantify transient heterogeneous dynamical changes from large datasets generated in single molecule/particle tracking experiments. Based on wavelet transform, the method transforms raw data to locally match dynamics of interest. This is accomplished using statistically adaptive universal thresholding, whose advantage is to avoid a single arbitrary threshold that might conceal individual variability across populations. How to implement this multiscale method is described, focusing on local confined diffusion separated by transient transport periods or hopping events, with 3 specific examples: in cell biology, biotechnology, and glassy colloid dynamics. This computationally-efficient method can run routinely on hundreds of millions of data points analyzed within an hour on a desktop personal computer.