  We describe a simple automated method to extract and quantify transient
heterogeneous dynamical changes from large datasets generated in single
molecule/particle tracking experiments. Based on wavelet transform, the method
transforms raw data to locally match dynamics of interest. This is accomplished
using statistically adaptive universal thresholding, whose advantage is to
avoid a single arbitrary threshold that might conceal individual variability
across populations. How to implement this multiscale method is described,
focusing on local confined diffusion separated by transient transport periods
or hopping events, with 3 specific examples: in cell biology, biotechnology,
and glassy colloid dynamics. This computationally-efficient method can run
routinely on hundreds of millions of data points analyzed within an hour on a
desktop personal computer.
