{ "info": { "author": "Pavlin Poli\u010dar", "author_email": "pavlin.g.p@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved", "Operating System :: MacOS", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX", "Operating System :: Unix", "Programming Language :: Python :: 3", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Visualization", "Topic :: Software Development", "Topic :: Software Development :: Libraries :: Python Modules" ], "description": "openTSNE\n========\n\n|Build Status| |ReadTheDocs Badge| |Codacy Badge| |License Badge|\n\nopenTSNE 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.\n\n.. figure:: docs/source/images/macosko_2015.png\n :alt: Macosko 2015 mouse retina t-SNE embedding\n :align: center\n\n A visualization of 44,808 single cell transcriptomes obtained from the mouse retina [5]_ embedded using the multiscale kernel trick to better preserve the global aligment of the clusters.\n\n- `Documentation `__\n- `User Guide and Tutorial `__\n- Examples: `basic `__, `advanced `__, `preserving global alignment `__, `embedding large data sets `__\n- `Speed benchmarks `__\n\nInstallation\n------------\n\nopenTSNE requires Python 3.6 or higher in order to run.\n\nConda\n~~~~~\n\nopenTSNE can be easily installed from ``conda-forge`` with\n\n::\n\n conda install --channel conda-forge opentsne\n\n`Conda package `__\n\nPyPi\n~~~~\n\nopenTSNE is also available through ``pip`` and can be installed with\n\n::\n\n pip install opentsne\n\n`PyPi package `__\n\nNote that openTSNE requires a C/C++ compiler. ``numpy`` must also be\ninstalled.\n\nIn order for openTSNE to utilize multiple threads, the C/C++ compiler\nmust also implement ``OpenMP``. In practice, almost all compilers\nimplement this with the exception of older version of ``clang`` on OSX\nsystems.\n\nTo squeeze the most out of openTSNE, you may also consider installing\nFFTW3 prior to installation. FFTW3 implements the Fast Fourier\nTransform, which is heavily used in openTSNE. If FFTW3 is not available,\nopenTSNE will use numpy\u2019s implementation of the FFT, which is slightly\nslower than FFTW. The difference is only noticeable with large data sets\ncontaining millions of data points.\n\nA hello world example\n---------------------\n\nGetting started with openTSNE is very simple. First, we'll load up some data using scikit-learn\n\n.. code:: python\n\n from sklearn import datasets\n\n iris = datasets.load_iris()\n x, y = iris[\"data\"], iris[\"target\"]\n\nthen, we'll import and run\n\n.. code:: python\n\n from openTSNE import TSNE\n\n embedding = TSNE().fit(x)\n\nCitation\n--------\n\nIf you make use of openTSNE for your work we would appreciate it if you would cite the paper\n\n.. code::\n\n @article {Poli{\\v c}ar731877,\n author = {Poli{\\v c}ar, Pavlin G. and Stra{\\v z}ar, Martin and Zupan, Bla{\\v z}},\n title = {openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding},\n year = {2019},\n doi = {10.1101/731877},\n publisher = {Cold Spring Harbor Laboratory},\n URL = {https://www.biorxiv.org/content/early/2019/08/13/731877},\n eprint = {https://www.biorxiv.org/content/early/2019/08/13/731877.full.pdf},\n journal = {bioRxiv}\n }\n\n\nReferences\n----------\n\n.. [1] Maaten, Laurens van der, and Geoffrey Hinton. `\u201cVisualizing data using\n t-SNE.\u201d `__\n Journal of machine learning research 9.Nov (2008): 2579-2605.\n.. [2] Van Der Maaten, Laurens. `\u201cAccelerating t-SNE using tree-based algorithms.\u201d\n `__\n The Journal of Machine Learning Research 15.1 (2014): 3221-3245.\n.. [3] Linderman, George C., et al. `\"Fast interpolation-based t-SNE for improved\n visualization of single-cell RNA-seq data.\" `__ Nature methods 16.3 (2019): 243.\n.. [4] Kobak, Dmitry, and Philipp Berens. `\u201cThe art of using t-SNE for single-cell\n transcriptomics.\u201d `__\n bioRxiv (2018): 453449.\n.. [5] Macosko, Evan Z., et al.\u00a0\\ `\u201cHighly parallel genome-wide expression profiling of\n individual cells using nanoliter droplets.\u201d\n `__\n Cell 161.5 (2015): 1202-1214.\n\n.. |Build Status| image:: https://dev.azure.com/pavlingp/openTSNE/_apis/build/status/Test?branchName=master\n :target: https://dev.azure.com/pavlingp/openTSNE/_build/latest?definitionId=1&branchName=master\n.. |ReadTheDocs Badge| image:: https://readthedocs.org/projects/opentsne/badge/?version=latest\n :target: https://opentsne.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n.. |Codacy Badge| image:: https://api.codacy.com/project/badge/Grade/ef67c21a74924b548acae5a514bc443d\n :target: https://app.codacy.com/app/pavlin-policar/openTSNE?utm_source=github.com&utm_medium=referral&utm_content=pavlin-policar/openTSNE&utm_campaign=Badge_Grade_Dashboard\n.. |License Badge| image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\n :target: 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