Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches.

Area of Science:

• Biophysics
• Physical Chemistry
• Computational Biology

Background:

• Single-particle tracking (SPT) is vital for observing molecular and particle dynamics.
• Analyzing reconstructed trajectories is key to understanding motion mechanisms.
• Traditional methods like mean squared displacement (MSD) have limitations.

Purpose of the Study:

• To review trajectory analysis methods for single-particle tracking.
• To highlight factors affecting traditional analysis accuracy.
• To introduce advanced methods for characterizing complex dynamics.

Main Methods:

• Review of traditional mean squared displacement (MSD) analysis.
• Exploration of methods using displacement, angle, velocity, and time distributions.
• Discussion of Hidden Markov Models (HMMs) for state identification.
• Overview of machine learning approaches (random forest, deep learning) for trajectory classification.

Main Results:

• MSD analysis can be affected by neglected factors.
• Alternative parameter distributions offer higher sensitivity to heterogeneity and transient behaviors.
• HMMs effectively identify dynamic states and kinetics.
• Machine learning provides powerful tools for model-based and model-free trajectory classification.

Conclusions:

• Combining classical statistics with machine learning offers the most comprehensive and accurate trajectory analysis.
• Advanced methods reveal complexities often masked by traditional MSD analysis.
• Free software is available for several analysis techniques, promoting accessibility.