PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding) is a tool for visualizing high dimensional data. PHATE uses a novel conceptual framework for learning and visualizing the manifold to preserve both local and global distances.
To see how PHATE can be applied to datasets such as facial images and single-cell data from human embryonic stem cells, check out our preprint on BioRxiv.
Python installation and dependencies
Installation with pip
The Python version of PHATE can be installed using:
pip install --user phate
Installation from source
The Python version of PHATE can be installed from GitHub by running the following from a terminal:
git clone --recursive git://github.com/KrishnaswamyLab/PHATE.git cd Python python setup.py install --user
If you have loaded a data matrix data in Python (cells on rows, genes on columns) you can run PHATE as follows:
import phate phate_op = phate.PHATE() data_phate = phate_op.fit_transform(data)
PHATE accepts the following data types: numpy.array, scipy.spmatrix, pandas.DataFrame and anndata.AnnData.
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, run the following in a Python interpreter:
import phate tree_data, tree_clusters = phate.tree.gen_dla() phate_operator = phate.PHATE(k=15, t=100) tree_phate = phate_operator.fit_transform(tree_data) phate.plot.scatter2d(phate_operator, c=tree_clusters) # or phate.plot.scatter2d(tree_phate, c=tree_clusters) phate.plot.rotate_scatter3d(phate_operator, c=tree_clusters)
A demo on PHATE usage and visualization for single cell RNA-seq data can be found in this Jupyter notebook. A second tutorial is available here which works with the artificial tree shown above in more detail. You can also access interactive versions of these tutorials on Google Colaboratory: single cell RNA seq, artificial tree.
If you have any questions or require assistance using PHATE, please contact us at https://krishnaswamylab.org/get-help
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