Riley Juenemann

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  Particle tracking in live cell data
  Riley Juenemann

  In contrast to in vitro particle tracking experiments, wherein there are great controls on particle and environmental homogeneity, live cell (in vivo) tracking features tremendous diversity in particle movement. In this work, we have developed a suite of “first-pass� statistical tools to categorize disparate types of particle trajectories. The data we used for this project was generated in the the lab of Prof. Christine Payne, using fluorescence microscopy in HeLa (model human) cells. Some particle paths were easily distinguishable as free diffusion, stuck diffusion, or directed transport, while other trajectories were difficult to categorize. Several of the more complex paths indicated the potential for tracking error. The tools we developed for the categorization process include the correlation between consecutive increments and effective diffusivity from a maximum likelihood estimation. The standard deviation for the major and minor axis and the creation of a parameterized path to represent a fictional moving anchor employed principal components analysis. This anchor estimation allowed the computation of effective velocity and the average distance the particle deviated from the anchor. Based on these data measures, K-means clustering was utilized to distinguish between free diffusion, stuck diffusion, directed transport, and tracker error. This automated categorization process proved to be successful on data simulated using stochastic differential equations and provided interesting results on the live cell data.