opentsne

Extensible, parallel implementations of t-SNE

https://github.com/pavlin-policar/opentsne

Science Score: 59.0%

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DOI references
Found 2 DOI reference(s) in README
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Links to: sciencedirect.com, springer.com, nature.com
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3 of 13 committers (23.1%) from academic institutions
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Scientific vocabulary similarity
Low similarity (15.2%) to scientific vocabulary

Keywords

dimensionality-reduction embedding machine-learning tsne visualization

Last synced: 6 months ago · JSON representation

Repository

Extensible, parallel implementations of t-SNE

Basic Info

Host: GitHub
Owner: pavlin-policar
License: bsd-3-clause
Language: Python
Default Branch: master
Homepage: https://opentsne.rtfd.io
Size: 70 MB

Statistics

Stars: 1,555
Watchers: 21
Forks: 173
Open Issues: 9
Releases: 10

Topics

dimensionality-reduction embedding machine-learning tsne visualization

Created over 7 years ago · Last pushed 6 months ago

Metadata Files

Readme License

README.rst

openTSNE
========

|Build Status| |ReadTheDocs Badge| |License Badge|

openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE) [1]_, 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 [2]_, massive speed improvements [3]_ [4]_ [5]_, enabling t-SNE to scale to millions of data points and various tricks to improve global alignment of the resulting visualizations [6]_.

.. figure:: docs/source/images/macosko_2015.png
   :alt: Macosko 2015 mouse retina t-SNE embedding
   :align: center

   A visualization of 44,808 single cell transcriptomes obtained from the mouse retina [7]_ 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 `__

Installation
------------

openTSNE requires Python 3.9 or higher in order to run.

Conda
~~~~~

openTSNE can be easily installed from ``conda-forge`` with

::

   conda install --channel conda-forge opentsne

`Conda package `__

PyPi
~~~~

openTSNE is also available through ``pip`` and can be installed with

::

   pip install opentsne

`PyPi package `__

Installing from source
~~~~~~~~~~~~~~~~~~~~~~

If you wish to install openTSNE from source, please run

::

   pip install .


in the root directory to install the appropriate dependencies and compile the necessary binary files.

Please note that openTSNE requires a C/C++ compiler to be available on the system.

In order for openTSNE to utilize multiple threads, the C/C++ compiler
must support ``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

.. code:: python

   from sklearn import datasets

   iris = datasets.load_iris()
   x, y = iris["data"], iris["target"]

then, we'll import and run

.. code:: python

   from openTSNE import TSNE

   embedding = TSNE().fit(x)

Citation
--------

If you make use of openTSNE for your work we would appreciate it if you would cite the paper

.. code::

    @article{Policar2024,
        title={openTSNE: A Modular Python Library for t-SNE Dimensionality Reduction and Embedding},
        author={Poli{\v c}ar, Pavlin G. and Stra{\v z}ar, Martin and Zupan, Bla{\v z}},
        journal={Journal of Statistical Software},
        year={2024},
        volume={109},
        number={3},
        pages={1–30},
        doi={10.18637/jss.v109.i03},
        url={https://www.jstatsoft.org/index.php/jss/article/view/v109i03}
    }
    
openTSNE implements two efficient algorithms for t-SNE. Please consider citing the original authors of the algorithm that you use. If you use FIt-SNE (default), then the citation is [5]_ below, but if you use Barnes-Hut the citations are [3]_ and [4]_.


References
----------

.. [1] Van Der Maaten, Laurens, and Hinton, Geoffrey. `“Visualizing data using
    t-SNE.” `__
    Journal of Machine Learning Research 9.Nov (2008): 2579-2605.
.. [2] Poličar, Pavlin G., Martin Stražar, and Blaž Zupan. `“Embedding to Reference t-SNE Space Addresses Batch Effects in Single-Cell Classification.” `__ Machine Learning (2021): 1-20.
.. [3] Van Der Maaten, Laurens. `“Accelerating t-SNE using tree-based algorithms.”
    `__
    Journal of Machine Learning Research 15.1 (2014): 3221-3245.
.. [4] Yang, Zhirong, Jaakko Peltonen, and Samuel Kaski. `"Scalable optimization of neighbor embedding for visualization." `__ International Conference on Machine Learning. PMLR, 2013.
.. [5] 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.
.. [6] Kobak, Dmitry, and Berens, Philipp. `“The art of using t-SNE for single-cell transcriptomics.” `__
    Nature Communications 10, 5416 (2019).
.. [7] Macosko, Evan Z., et al. `“Highly parallel genome-wide expression profiling of
    individual cells using nanoliter droplets.”
    `__
    Cell 161.5 (2015): 1202-1214.

.. |Build Status| image:: https://dev.azure.com/pavlingp/openTSNE/_apis/build/status/Test?branchName=master
   :target: https://dev.azure.com/pavlingp/openTSNE/_build/latest?definitionId=1&branchName=master
.. |ReadTheDocs Badge| image:: https://readthedocs.org/projects/opentsne/badge/?version=latest
   :target: https://opentsne.readthedocs.io/en/latest/?badge=latest
   :alt: Documentation Status
.. |License Badge| image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg
   :target: https://opensource.org/licenses/BSD-3-Clause

Owner

Name: Pavlin Poličar
Login: pavlin-policar
Kind: user
Location: Slovenia
Company: University of Ljubljana

Twitter: pavlinpolicar
Repositories: 49
Profile: https://github.com/pavlin-policar

PhD student working on applying machine learning methods to biomedical and scRNA-seq data.

GitHub Events

Total

Issues event: 8
Watch event: 85
Issue comment event: 36
Push event: 4
Pull request review event: 5
Pull request review comment event: 8
Pull request event: 3
Fork event: 14
Create event: 2

Last Year

Issues event: 8
Watch event: 85
Issue comment event: 36
Push event: 4
Pull request review event: 5
Pull request review comment event: 8
Pull request event: 3
Fork event: 14
Create event: 2

Committers

Last synced: 10 months ago

All Time

Total Commits: 580
Total Committers: 13
Avg Commits per committer: 44.615
Development Distribution Score (DDS): 0.112

Past Year

Commits: 6
Committers: 3
Avg Commits per committer: 2.0
Development Distribution Score (DDS): 0.333

Top Committers

Name	Email	Commits
Pavlin Poličar	p**p@g**m	515
Dmitry Kobak	d**k@u**e	41
BlazZupan	b**n@f**i	6
cclauss	c**s@b**h	5
PrimozGodec	p**9@g**m	4
mstrazar	m**r@g**m	2
inejc	n**c@g**m	1
Todd	t**8@g**m	1
Tim	T****e	1
Sean Pedersen	3****n	1
Leo@DS	1****G	1
Jake	j**g@g**m	1
Ales Erjavec	a**c@f**i	1

Committer Domains (Top 20 + Academic)

fri.uni-lj.si: 2 bluewin.ch: 1 uni-tuebingen.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 77
Total pull requests: 50
Average time to close issues: 3 months
Average time to close pull requests: 6 days
Total issue authors: 46
Total pull request authors: 8
Average comments per issue: 3.78
Average comments per pull request: 2.1
Merged pull requests: 44
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 5
Pull requests: 4
Average time to close issues: 11 months
Average time to close pull requests: about 1 hour
Issue authors: 5
Pull request authors: 3
Average comments per issue: 4.0
Average comments per pull request: 2.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

dkobak (21)
yurivict (4)
fsvbach (3)
sbembenek18 (2)
pavlin-policar (2)
tomcsojn (2)
ritagonmar (2)
Yangxiaojun1230 (2)
jdtuck (2)
bbaabemhp (1)
austinpatrickm (1)
morningphase (1)
valentinlageard (1)
jlmelville (1)
ivan-marroquin (1)

Pull Request Authors

pavlin-policar (31)
dkobak (12)
Leoo99G (2)
PrimozGodec (2)
luanamarinho (2)
ales-erjavec (1)
jnboehm (1)
SeanPedersen (1)
TimRepke (1)

Top Labels

Issue Labels

bug (8) question (4) enhancement (4) wishlist (2) wontfix (1)

Pull Request Labels

Packages

Total packages: 5
Total downloads:
- pypi 37,310 last-month

Total dependent packages: 11
(may contain duplicates)
Total dependent repositories: 40
(may contain duplicates)
Total versions: 91
Total maintainers: 1

pypi.org: opentsne

Extensible, parallel implementations of t-SNE

Homepage: https://github.com/pavlin-policar/openTSNE
Documentation: https://opentsne.readthedocs.io/
License: BSD-3-Clause
Latest release: 1.0.2
published over 1 year ago

Versions: 25
Dependent Packages: 8
Dependent Repositories: 38
Downloads: 37,310 Last month

Rankings

Dependent packages count: 1.6%

Downloads: 1.8%

Stargazers count: 1.9%

Average: 2.3%

Dependent repos count: 2.4%

Forks count: 4.0%

Maintainers (1)

pavlin

Last synced: 6 months ago

proxy.golang.org: github.com/pavlin-policar/openTSNE

Documentation: https://pkg.go.dev/github.com/pavlin-policar/openTSNE#section-documentation
License: bsd-3-clause
Latest release: v1.0.3
published 9 months ago

Versions: 22
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 6.5%

Average: 6.7%

Dependent repos count: 6.9%

Last synced: 6 months ago

proxy.golang.org: github.com/pavlin-policar/opentsne

Documentation: https://pkg.go.dev/github.com/pavlin-policar/opentsne#section-documentation
License: bsd-3-clause
Latest release: v1.0.3
published 9 months ago

Versions: 22
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 6.5%

Average: 6.7%

Dependent repos count: 6.9%

Last synced: 6 months ago

conda-forge.org: opentsne

Homepage: https://github.com/pavlin-policar/openTSNE
License: BSD-3-Clause
Latest release: 0.6.2
published over 3 years ago

Versions: 17
Dependent Packages: 2
Dependent Repositories: 1

Rankings

Stargazers count: 11.8%

Forks count: 15.3%

Average: 17.8%

Dependent packages count: 19.6%

Dependent repos count: 24.4%

Last synced: 6 months ago

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.

Homepage: https://github.com/pavlin-policar/openTSNE
License: BSD-3-Clause
Latest release: 1.0.2
published 9 months ago

Versions: 5
Dependent Packages: 1
Dependent Repositories: 1

Rankings

Stargazers count: 22.2%

Forks count: 27.4%

Dependent packages count: 30.7%

Average: 32.9%

Dependent repos count: 51.4%

Last synced: 6 months ago

opentsne

Science Score: 59.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.rst

Owner

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

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Issue Labels

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Packages

pypi.org: opentsne

Rankings

Maintainers (1)

proxy.golang.org: github.com/pavlin-policar/openTSNE

Rankings

proxy.golang.org: github.com/pavlin-policar/opentsne

Rankings

conda-forge.org: opentsne

Rankings

anaconda.org: opentsne

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