fastTSNE - tSNE的快速并行实现
使用FFT加速插值和近似最近邻居在11分23秒内计算来自小鼠神经系统[Zeisel 2018]的160,796个单细胞转录组的可视化。
Python 机器学习
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详细介绍
openTSNE
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], enabling t-SNE to scale to millions of data points and various tricks to improve global alignment of the resulting visualizations [5].
A visualization of 44,808 single cell transcriptomes obtained from the mouse retina [6] 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 3.6 or higher in order to run.
Conda
openTSNE can be easily installed from conda-forge
with
conda install --channel conda-forge opentsne
PyPi
openTSNE is also available through pip
and can be installed with
pip install opentsne
Installing from source
If you wish to install openTSNE from source, please run
python setup.py 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. Additionally, numpy
must be pre-installed in the active environment.
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
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)
Citation
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} }
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 [4] below, but if you use Barnes-Hut the citation is [3].
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.” BioRxiv (2019): 671404. |
[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) 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. |
[5] | Kobak, Dmitry, and Berens, Philipp. “The art of using t-SNE for single-cell transcriptomics.” Nature Communications 10, 5416 (2019). |
[6] | Macosko, Evan Z., et al. “Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.” Cell 161.5 (2015): 1202-1214. |
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