Skip to main content

Time-lagged t-SNE of molecular trajectories

Project description

Total alerts


Time-lagged t-SNE of molecular trajectories.

Trajectory of molecular simulation is dimensionally reduced by t-distributed stochastic embedding (t-SNE) [1] and by a version of t-SNE that focuses on slow motions via analysis inspired by time-lagged independent component analysis (TICA) [2,3].

The input is a trajectory in pdb, xtc, trr, dcd, netcdf or mdcrd (only atoms intended for analysis). The second input file is a topology (pdb file with same atoms as in trajectory). Output contains frame ID, PCA, TICA, t-SNE and time-lagged t-SNE coordinates.


usage: tltsne [-h] [-i INFILE] [-p INTOP] [-o OUTFILE] [-nofit NOFIT]
              [-lagtime LAGTIME] [-pcadim PCADIM] [-ticadim TICADIM]
              [-maxpcs MAXPCS] [-ncomp NCOMP] [-perplex1 PERPLEX1]
              [-perplex2 PERPLEX2] [-rate RATE] [-niter NITER] [-exag EXAG]

Time-lagged t-SNE of simulation trajectories, requires scimpy, pyemma, sklearn
and mdtraj

optional arguments:
  -h, --help          show this help message and exit
  -i INFILE           Input trajectory in pdb, xtc, trr, dcd, netcdf or mdcrd
                      of atoms to be analyzed. No jumps in PBC allowed.
  -p INTOP            Input topology in pdb with atoms to be analyzed.
  -o OUTFILE          Output file.
  -nofit NOFIT        Structure is NOT fit to reference topology if nofit is
                      set to 1 (default 0).
  -lagtime LAGTIME    Lag time in number of frames (default 1).
  -pcadim PCADIM      Number o PCA coordinates to be printed (defaut 2).
  -ticadim TICADIM    Number o TICA coordinates to be printed (defaut 2).
  -maxpcs MAXPCS      Number of TICA coordinates to be passed to t-SNE
                      (default 50).
  -ncomp NCOMP        Number of t-SNE and time-lagged t-SNE coordinates to be
                      printed (defaut 2).
  -perplex1 PERPLEX1  Perplexity of t-SNE (default 10.0
  -perplex2 PERPLEX2  Perplexity of time-lagged t-SNE (default 10.0
  -rate RATE          Learnning rate of t-SNE and time-lagged t-SNE (default
  -niter NITER        Number of iterations of t-SNE and time-lagged t-SNE
                      (default 1000).
  -exag EXAG          Early exaggeration of t-SNE and time-lagged t-SNE


Install via PIP:

pip3 install tltsne

(or with sudo).

Install from GitHub:

git clone
cd tltsne
pip3 install .

(or with sudo).


  • pyemma [4]
  • mdtraj [5]
  • scipy [6]
  • sklearn [7]


  1. L.J.P. van der Maaten, G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. J. Mach. Learn. Res. 2008, 9, 2579-2605.

  2. G. Perez-Hernandez, F. Paul, T. Giorgino, G. de Fabritiis, F. Noé: Identification of slow molecular order parameters for Markov model construction. J. Chem. Phys. 2013, 139, 015102.

  3. C. R. Schwantes and V. S. Pande: Improvements in Markov state model construction reveal many non-native interactions in the folding of NTL9. J. Chem. Theory Comput. 2013, 9, 2000-2009.





Project details

Release history Release notifications

This version


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for tltsne, version 0.0.1
Filename, size File type Python version Upload date Hashes
Filename, size tltsne-0.0.1-py3-none-any.whl (6.4 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size tltsne-0.0.1.tar.gz (5.1 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page