Dimensionality reduction is in the hurt of molecular dynamics simulations keeping in mind its importance in reducing the high-dimension data produced during molecular dynamics simulations. It also found application in building Markov State Models and also used as CVs to drive enhanced sampling simulations. Several dimensionality reduction techniques have been used over the years such as Principal Component Analysis (PCA), non-linear PCA, tICA (time lagged independent component analysis), auto-encoder etc (See here).

However PCA and tICA are the most commonly used algorithms in this purpose. Both of these algorithms are linear however might suffer in giving a better representation of non-linear data. However tSNE (original site here) perform the job in a more efficient way (See here).

So instead of using tICA during Markov State Modeling we can use tSNE to handle the job followed by some other clustering such as K-means clustering. I performed an initial testing of the PCA vs tSNE in one of my molecular dynamics trajectory which looks like the attached images. It is not a surprise that tSNE distinguished the data in a better way compared to PCA.

**Figure 1**. Dimensionality reduction using PCA technique on my trajectory.

**Figure 2**. Dimensionality reduction using tSNE technique on my trajectory.

Also it is very much possible to drive a simulation based on the tSNE generated clusters. You can use MODE-TASK which is a python programme which has integrated functionalities to perform PCA, Kernel PCA and tSNE. Results are shown below (using my system). Keep an eye on the island like clusters in tSNE.

**Figure 3**. PCA using MODE-TASK.

**Figure 4**. Kernel PCA using MODE-TASK.

**Figure 5**. tSNE using MODE-TASK.

*Please email me if you want to collaborate on this interesting project.*