A sequential algorithm to detect diffusion switching along intracellular particle trajectories

Recent advances in molecular biology and fluorescence microscopy imaging have made possible the inference of the dynamics 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, an issue is to estimate the temporal change-points that is the times at which a change of dynamics occurs. We propose a non-parametric procedure based on test statistics [Briane et al., 2018] computed on local windows along the trajectory to detect the change-points. 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 efficacy of the method on real data in 2D and 3D, depicting the motion of mRNA complexes-called mRNP-in neuronal dendrites, Galectin-3 endocytosis and trafficking within the cell. A user-friendly Matlab package containing examples and the code of the simulations used in the paper is available at http://serpico.rennes.inria.fr/doku.php?id=software:cpanalysis: index