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Python translation of the hybrid dynamicTreeCut method created by Peter Langfelder and Bin Zhang.
dynamicTreeCut was originally published by in *Bioinformatics*:
Langfelder P, Zhang B, Horvath S (2007) Defining clusters from a hierarchical cluster tree:
the Dynamic Tree Cut package for R. Bioinformatics 2008 24(5):719-720
dynamicTreeCut R code is distributed under the GPL-3 License and
original sources should be cited.
dynamicTreeCut contains methods for detection of clusters in hierarchical clustering dendrograms.
*NOTE: though the clusters match the R output, the cluster names are shuffled*
Installing
==========
To install, it's best to create an environment after installing and downloading the
`Anaconda Python Distribution <https://www.continuum.io/downloads>`__
conda env create --file environment.yml
PyPI install, presuming you have all its requirements (numpy and scipy) installed::
pip install dynamicTreeCut
Importation
===========
::
>>> from dynamicTreeCut import cutreeHybrid
>>> from scipy.spatial.distance import pdist
>>> import numpy as np
>>> from scipy.cluster.hierarchy import linkage
>>> d = np.transpose(np.arange(1,10001).reshape(100,100))
>>> distances = pdist(d, "euclidean")
>>> link = linkage(distances, "average")
>>> clusters = cutreeHybrid(link, distances)
..cutHeight not given, setting it to 495.1 ===> 99% of the (truncated) height range in dendro.
..done.
>>> clusters["labels"]
[2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
Compared to R::
> library(dynamicTreeCut)
> d = matrix(1:10000, 100)
> distances <- dist(d, method="euclidean")
> dendro <- hclust(distances, method="average")
> clusters <- cutreeDynamic(dendro, distM=as.matrix(distances))
..cutHeight not given, setting it to 495 ===> 99% of the (truncated) height range in dendro.
..done.
> clusters
[1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3
[38] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1
[75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Installation
============
If you dont already have numpy and scipy installed, it is best to download
`Anaconda`, a python distribution that has them included.
https://continuum.io/downloads
Dependencies can be installed by::
pip install -r requirements.txt
License
=======
dynamicTreeCut is available under the GPL-3 License
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"description": "|Stars| |PyPIDownloads| |PyPI| |Build Status| |Coffee|\n\n.. |Stars| image:: https://img.shields.io/github/stars/kylessmith/dynamicTreeCut?logo=GitHub&color=yellow\n :target: https://github.com/kylessmith/dynamicTreeCut/stargazers\n.. |PyPIDownloads| image:: https://pepy.tech/badge/dynamicTreeCut\n :target: https://pepy.tech/project/dynamicTreeCut\n.. |PyPI| image:: https://img.shields.io/pypi/v/dynamicTreeCut.svg\n :target: https://pypi.org/project/dynamicTreeCut\n.. |Build Status| image:: https://travis-ci.org/kylessmith/dynamicTreeCut.svg?branch=master\n :target: https://travis-ci.org/kylessmith/dynamicTreeCut\n.. |Coffee| image:: https://img.shields.io/badge/-buy_me_a%C2%A0coffee-gray?logo=buy-me-a-coffee&color=ff69b4\n :target: https://www.buymeacoffee.com/kylessmith\n\n\nPython translation of the hybrid dynamicTreeCut method created by Peter Langfelder and Bin Zhang.\n\ndynamicTreeCut was originally published by in *Bioinformatics*:\n\tLangfelder P, Zhang B, Horvath S (2007) Defining clusters from a hierarchical cluster tree:\n\tthe Dynamic Tree Cut package for R. Bioinformatics 2008 24(5):719-720\n\ndynamicTreeCut R code is distributed under the GPL-3 License and\noriginal sources should be cited.\n\n\ndynamicTreeCut contains methods for detection of clusters in hierarchical clustering dendrograms.\n*NOTE: though the clusters match the R output, the cluster names are shuffled*\n\nInstalling\n==========\n\nTo install, it's best to create an environment after installing and downloading the\n`Anaconda Python Distribution <https://www.continuum.io/downloads>`__\n\n conda env create --file environment.yml\n\nPyPI install, presuming you have all its requirements (numpy and scipy) installed::\n\n\tpip install dynamicTreeCut\n\n\t\nImportation\n===========\n::\n\n\t>>> from dynamicTreeCut import cutreeHybrid\n\t>>> from scipy.spatial.distance import pdist\n\t>>> import numpy as np\n\t>>> from scipy.cluster.hierarchy import linkage\n\t>>> d = np.transpose(np.arange(1,10001).reshape(100,100))\n\t>>> distances = pdist(d, \"euclidean\")\n\t>>> link = linkage(distances, \"average\")\n\t>>> clusters = cutreeHybrid(link, distances)\n\t..cutHeight not given, setting it to 495.1 ===> 99% of the (truncated) height range in dendro.\n\t..done.\n\t>>> clusters[\"labels\"]\n\t[2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3\n \t 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1\n \t 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n\t\n\t\nCompared to R::\n\n\t> library(dynamicTreeCut)\n\t> d = matrix(1:10000, 100)\n\t> distances <- dist(d, method=\"euclidean\")\n\t> dendro <- hclust(distances, method=\"average\")\n\t> clusters <- cutreeDynamic(dendro, distM=as.matrix(distances))\n\t ..cutHeight not given, setting it to 495 ===> 99% of the (truncated) height range in dendro.\n\t ..done.\n\t> clusters\n\t [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3\n \t [38] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1\n \t [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n\nInstallation\n============\n\nIf you dont already have numpy and scipy installed, it is best to download\n`Anaconda`, a python distribution that has them included. \n\n https://continuum.io/downloads\n\nDependencies can be installed by::\n\n pip install -r requirements.txt\n\n\nLicense\n=======\n\ndynamicTreeCut is available under the GPL-3 License",
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