Classification of particle trajectories in living cells: machine learning versus statistical testing hypothesis for fractional anomalous diffusion

Abstract

Single-particle tracking (SPT) has become a popular tool to study the intracellular transport of molecules in living cells. Inferring the character of their dynamics is important, because it determines the organization and functions of the cells. For this reason, one of the first steps in the analysis of SPT data is the identification of the diffusion type of the observed particles. The most popular method to identify the class of a trajectory is based on the mean square displacement (MSD). However, due to its known limitations, several other approaches have been already proposed. With the recent advances in algorithms and the developments of modern hardware, the classification attempts rooted in machine learning (ML) are of particular interest. In this work, we adopt two ML ensemble algorithms, i.e. random forest and gradient boosting, to the problem of trajectory classification. We present a new set of features used to transform the raw trajectories data into input vectors required by the classifiers. The resulting models are then applied to real data for G protein-coupled receptors and G proteins. The classification results are compared to recent statistical methods going beyond MSD.

32 pages, 5 figures

References in corpus (4)

• Anomalous transport in the crowded world of biological cells
• Modeling the Time Variability of SDSS Stripe 82 Quasars as a Damped Random Walk
• The Plasma Membrane is Compartmentalized by a Self-Similar Cortical Actin Meshwork
• Measurement of Anomalous Diffusion Using Recurrent Neural Networks

Cited by in corpus (5)

• Machine-Learning Solutions for the Analysis of Single-Particle Diffusion Trajectories
• Gramian Angular Fields for leveraging pretrained computer vision models with anomalous diffusion trajectories
• Efficient recurrent neural network methods for anomalously diffusing single particle short and noisy trajectories
• Persistence of temperature and precipitation: from local to global anomalies
• Characterization of pedestrian contact interaction trajectories