Adaptive settings for the nearest-neighbor particle tracking algorithm.

Abstract

The performance of the single particle tracking (SPT) nearest-neighbor algorithm is determined by parameters that need to be set according to the characteristics of the time series under study. Inhomogeneous systems, where these characteristics fluctuate spatially, are poorly tracked when parameters are set globally. We present a novel SPT approach that adapts the well-known nearest-neighbor tracking algorithm to the local density of particles to overcome the problems of inhomogeneity. We demonstrate the performance improvement provided by the proposed method using numerical simulations and experimental data and compare its performance with state of the art SPT algorithms. The algorithms proposed here, are released under the GNU General Public License and are freely available on the web at http://sourceforge.net/p/adaptivespt. javier.mazzaferri@gmail.com Supplementary data are available at Bioinformatics online.