openTSNEslim 1.0.5

openTSNEslim is a slimmed down version of openTSNE that doesn't require use of scikit-learn or scipy for inference. This is useful for creating a smaller bundle for deployment.

openTSNEslim

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openTSNEslim is a slimmed down version of openTSNE that doesn’t require use of scikit-learn or scipy for inference. This is useful for creating a smaller bundle for deployment.

Forked repository create by Pavlin Poličar from https://github.com/pavlin-policar/openTSNE

openTSNE (original repository)

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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].

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

openTSNEslim can be installed on all supported versions of Python.

PyPi

openTSNEslim is also available through pip and can be installed with

pip install opentsneslim

PyPi package

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 openTSNEslim 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

@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] (1,2)

Van Der Maaten, Laurens. “Accelerating t-SNE using tree-based algorithms.” Journal of Machine Learning Research 15.1 (2014): 3221-3245.

[4] (1,2)

Yang, Zhirong, Jaakko Peltonen, and Samuel Kaski. “Scalable optimization of neighbor embedding for visualization.” International Conference on Machine Learning. PMLR, 2013.

[5] (1,2)

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.