2018/04/13 by Vincent Briane, Briane, Vincent, Charles Kervrann +3
Biochemistry, Genetics and Molecular Biology · #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Methodology (stat.ME) #RNA Research and Splicing #RNA and protein synthesis mechanisms
paper · pdf · doi:10.48550/arxiv.1804.04977
openalex publication_date 2018/04/13 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28
Single-particle tracking allows to infer the motion of single molecules in\nliving cells. When we observe a long trajectory (more than 100 points), it is\npossible that the particle switches mode of motion over time. Then, fitting a\nsingle model to the trajectory can be misleading. In this paper, we propose a\nmethod to detect the temporal change points : the times at which a change of\ndynamics occurs. More specifically, we consider that the particle switches\nbetween three main modes of motion : Brownian motion, subdiffusion and\nsuperdiffusion. We use an algorithm based on a statistic (Briane et al. 2016)\ncomputed on local windows along the trajectory. The method is non parametric as\nthe statistic is not related to any particular model. This algorithm controls\nthe number of false change point detections in the case where the trajectory is\nfully Brownian. A Monte Carlo study is proposed to demonstrate the performances\nof the method and also to compare the procedure to two competitive algorithms.\nAt the end, we illustrate the utility of the method on real data depicting the\nmotion of mRNA complexes (called mRNP) in neuronal dendrites.\n