Before starting anything with tSNE let’s read what is tSNE and how it has been compared with PCA. You can read it here. Several implementations of t-SNE are available here. A great introductory video on tSNE can be found here.

The dataset used in this explanation can be accessed here (named combine_times_ca.dcd and corresponding GRO file prot_ca.gro . Use VMD to open them and crosscheck).

# Download Matlab_r2017b

# Add the path of .dcd file reader for MatLab.Download the package from here

addpath('/home/sbhakat/matdcd-1.0')

# Give the path of your dcd file. In my case I am using a dcd file named combine_times_ca.dcd which has atoms starting from 1 to 331.

x=readdcd('/home/sbhakat/Plasmepsin_r1_r2_PCA/Gromacs_plmr2/Combine/combine_times_ca.dcd',1:331);

This will produce a following output

h =
struct with fields:
fid: 3
endoffile: 42789396
NSET: 10560
ISTART: 0
NSAVC: 1
NAMNF: 0
charmm: 1
charmm_extrablock: 1
charmm_4dims: 0
DELTA: 1
N: 331

# Perform Pincipal Component Analysis

[pc, score, latent, tsquare] = pca(x(2:end,:));

# Plot first two principal components

plot(score(:,1),score(:,2),'.')

# Label the plot

xlabel('PC1')
ylabel('PC2')

# It will pop up a window with PCA plot something the following

pca_combined

# Carrying on the calculation on the same Matlab window

rng default % for reproducibility

# Perform tSNE analysis with Barneshut algorithm

Y = tsne(x,'Algorithm','barneshut','NumPCAComponents',50);

#Produce the figure

figure
gscatter(Y(:,1),Y(:,2))
xlabel('tSNE1')
ylabel('tSNE2')

# It will produce something like the following

tsne_combined

**Reference**

The initial part of the tutorial was inspired by this one.

**Collaboration on use of tSNE in molecular dynamics simulation is highly appreciated.**

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