Abstract

A recent article introduced a method for detecting subtle spatio-temporal patterns within a dataset of mitotic processes. The method is based on permutation tests, and involves (1) permuting process parameters (e.g., division angle in the earlier case of mitosis), (2) calculating the effects, and (3) checking for distributional changes in a set of measures based on simple considerations of geometry. This article examines the method's application to a more common dataset: particles that undergo migration in three or fewer dimensions. The method is further extended in another direction: multiple types of particle are allowed. Exploiting these distinct types significantly enlarges the set of detectable patterns. Monte Carlo simulations are performed to illustrate the new capabilities. The resulting contribution is an increasingly systematic basis for the inference of patterned behavior from imaging datasets.

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