PHATE - Potential of Heat-diffusion for Affinity-based Trajectory Embedding
PHATE has been implemented in Python (2.7 and >=3.5), R and Matlab.
Python installation and dependencies
The Python version of PHATE can be installed using:
$ git clone git://github.com/SmitaKrishnaswamy/PHATE.git $ cd Python $ python setup.py install --user
PHATE depends on a number of python packages available on pypi and these dependencies are listed in setup.py All the dependencies will be automatically installed using the above commands
Usage
PHATE has been implemented with an API that should be familiar to those with experience using scikit-learn. The core of the PHATE package is the PHATE class which is a subclass of sklearn.base.BaseEstimator. To get started, import phate and instantiate a phate.PHATE() object. Just like most sklearn estimators, PHATE() objects have both fit() and fit_transform() methods. For more information, check out our notebook below.
If you want to try running our test script on a DLA fractal tree: 1. Make the test scripts executable
$ cd PHATE/Python/test $ chmod +x phate_test_tree.py phate_test_mESC.py $ ./phate_test_tree.py #output saved in a png
Jupyter Notebook
A demo on PHATE usage and visualization for single cell RNA-seq data can be found in this notebook: https://nbviewer.jupyter.org/github/KrishnaswamyLab/PHATE/blob/master/Python/tutorial/PHATE_tree.ipynb
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Release files for phate 0.2
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Release files / phate-0.2.tar.gz
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