| Abstract: | Single particle tracking(SPT) has long been utilized for investigation of complex system dynamics such as nanoparticle-cell interaction, however, the analysis of individual particle motions is always a difficult issue. Existing methods treat each data point or fragment on the recorded trajectory as an isolated "atom" and determine their relationship based on externally predefined models or physical states, which inevitably lead to oversimplification of the associated spatiotemporal complexity. Herein,inspired by the historical analysis in social science, we propose a modeless preprocessing framework for SPT analysis based on the "history" of the particle. This new strategy consists of 3 steps:(1) assign a "history" to each data point and construct successive overlapped historical vectors;(2) perform unsupervised clustering in the vector space to find their relative differences;(3) project differences back to the trajectory by coloring each point accordingly for visualization. As a result, the inner heterogeneity of the particle motion self-emerges as a colored trajectory, exhibiting a global picture of the local state transitions and providing valuable information for further model-based analysis. Since the complexity issues at various spatiotemporal scales have attracted increasing attention, and individual objects such as single molecules, cells, vehicles and even stars in the universe could all be treated as "single particles", this presuppositionless data preprocessing approach could help the investigations of many complex systems in fundamental research. |
