Javier Mazzaferri1, Joannie Roy1, Stephane Lefrancois2, Santiago Costantino2. 1. Centre de Recherche de l'Hôpital Maisonneuve-Rosemont, Montréal, Canada H1T 2M4, Département de Médecine, Université de Montréal, Montréal, Canada H3T 3J7 and Département d'Ophtalmologie et Institut de Génie Biomédical, Université de Montréal, Montréal, Canada H3T 1J4. 2. Centre de Recherche de l'Hôpital Maisonneuve-Rosemont, Montréal, Canada H1T 2M4, Département de Médecine, Université de Montréal, Montréal, Canada H3T 3J7 and Département d'Ophtalmologie et Institut de Génie Biomédical, Université de Montréal, Montréal, Canada H3T 1J4 Centre de Recherche de l'Hôpital Maisonneuve-Rosemont, Montréal, Canada H1T 2M4, Département de Médecine, Université de Montréal, Montréal, Canada H3T 3J7 and Département d'Ophtalmologie et Institut de Génie Biomédical, Université de Montréal, Montréal, Canada H3T 1J4.
Abstract
BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. AVAILABILITY AND IMPLEMENTATION: 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. CONTACT: javier.mazzaferri@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. AVAILABILITY AND IMPLEMENTATION: 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. CONTACT: javier.mazzaferri@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Brian E Hsu; Joannie Roy; Jack Mouhanna; Roni F Rayes; LeeAnn Ramsay; Sébastien Tabariès; Matthew G Annis; Ian R Watson; Jonathan D Spicer; Santiago Costantino; Peter M Siegel Journal: Oncogene Date: 2020-02-04 Impact factor: 9.867