Anchored Brownian motion and Bayesian methods for the analysis of single particle tracking data

Abstract

We present a novel statistical method and a prototype computational implementation for estimating the diffusion coefficient from single particle tracking (SPT) data. Our method is based on anchored Brownian motion which is a novel representation that relaxes the restrictions of conventional Brownian motion. Our method is fully developed in Bayesian terms and allows for robust estimation of diffusion coefficient and quantification of the uncertainly propagated from limited data quantity and quality as appropriate for the analysis of live-cell SPT data. We compare our methods with conventional Brownian motion and demonstrate superior performance in estimating the correct value of the diffusion coefficient. Finally, we benchmark our methods with SPT data from in cellulo and in silico experiments.