  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.
