Time-Lagged t-Distributed Stochastic Neighbor Embedding (t-SNE) of Molecular Simulation Trajectories

Abstract

Molecular simulation trajectories represent high-dimensional data. Such data can be visualized by methods of dimensionality reduction. Non-linear dimensionality reduction methods are likely to be more efficient than linear ones due to the fact that motions of atoms are non-linear. Here we test a popular non-linear t-distributed stochastic neighbor embedding (t-SNE) method on analysis of trajectories of alanine dipeptide dynamics and Trp-cage folding and unfolding. Furthermore, we introduced a time-lagged variant of t-SNE in order to focus on slow motions in the molecular system. This time-lagged t-SNE efficiently visualizes slow dynamics of molecular systems.