openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE), a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings, massive speed improvements, enabling t-SNE to scale to millions of data points and various tricks to improve global alignment of the resulting visualizations.
A visualization of 44,808 single cell transcriptomes obtained from the mouse retina1 embedded using the multiscale kernel trick to better preserve the global aligment of the clusters.- Documentation
- User Guide and Tutorial
- Examples: basic, advanced, preserving global alignment, embedding large data sets
- Speed benchmarks
openTSNE requires Python 3.6 or higher in order to run.
openTSNE can be easily installed from conda-forge
with
conda install --channel conda-forge opentsne
openTSNE is also available through pip
and can be installed with
pip install opentsne
Note that openTSNE requires a C/C++ compiler. numpy
must also be installed.
In order for openTSNE to utilize multiple threads, the C/C++ compiler must also implement OpenMP
. In practice, almost all compilers implement this with the exception of older version of clang
on OSX systems.
To squeeze the most out of openTSNE, you may also consider installing FFTW3 prior to installation. FFTW3 implements the Fast Fourier Transform, which is heavily used in openTSNE. If FFTW3 is not available, openTSNE will use numpy’s implementation of the FFT, which is slightly slower than FFTW. The difference is only noticeable with large data sets containing millions of data points.
Getting started with openTSNE is very simple. First, we'll load up some data using scikit-learn
from sklearn import datasets
iris = datasets.load_iris()
x, y = iris["data"], iris["target"]
then, we'll import and run
from openTSNE import TSNE
embedding = TSNE().fit(x)
If you make use of openTSNE for your work we would appreciate it if you would cite the paper
@article {Poli{\v c}ar731877,
author = {Poli{\v c}ar, Pavlin G. and Stra{\v z}ar, Martin and Zupan, Bla{\v z}},
title = {openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding},
year = {2019},
doi = {10.1101/731877},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2019/08/13/731877},
eprint = {https://www.biorxiv.org/content/early/2019/08/13/731877.full.pdf},
journal = {bioRxiv}
}
Macosko, Evan Z., et al. “Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.” Cell 161.5 (2015): 1202-1214.↩