A Sequential Algorithm to Detect Diffusion Switching along Intracellular Particle Trajectories

TL;DR

This paper introduces a non-parametric sequential algorithm to detect change points in intracellular particle trajectories, distinguishing between different modes of motion such as Brownian, subdiffusive, and superdiffusive, with applications to biological data.

Contribution

The paper presents a new non-parametric method for detecting motion mode switches in particle trajectories, improving accuracy over existing algorithms.

Findings

The method effectively detects change points with controlled false positives.

Performance demonstrated through Monte Carlo simulations.

Application to real biological data shows practical utility.

Abstract

Single-particle tracking allows to infer the motion of single molecules in living cells. When we observe a long trajectory (more than 100 points), it is possible that the particle switches mode of motion over time. Then, fitting a single model to the trajectory can be misleading. In this paper, we propose a method to detect the temporal change points : the times at which a change of dynamics occurs. More specifically, we consider that the particle switches between three main modes of motion : Brownian motion, subdiffusion and superdiffusion. We use an algorithm based on a statistic (Briane et al. 2016) computed on local windows along the trajectory. The method is non parametric as the statistic is not related to any particular model. This algorithm controls the number of false change point detections in the case where the trajectory is fully Brownian. A Monte Carlo study is proposed to demonstrate the performances of the method and also to compare the procedure to two competitive algorithms. At the end, we illustrate the utility of the method on real data depicting the motion of mRNA complexes (called mRNP) in neuronal dendrites.