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 retina  embedded using the multiscale kernel trick to better preserve the global aligment of the clusters.
- 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 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.
A hello world example
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)
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|||Van Der Maaten, Laurens. “Accelerating t-SNE using tree-based algorithms.” The Journal of Machine Learning Research 15.1 (2014): 3221-3245.|
|||Linderman, George C., et al. "Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data." Nature methods 16.3 (2019): 243.|
|||Kobak, Dmitry, and Philipp Berens. “The art of using t-SNE for single-cell transcriptomics.” bioRxiv (2018): 453449.|
|||Macosko, Evan Z., et al. “Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.” Cell 161.5 (2015): 1202-1214.|