If you would like to get started using PHATE, check out our guided tutorial in Python.
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 (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 publication in Nature Biotechnology.
PHATE has been implemented in Python >=3.9, MATLAB and R.
- System Requirements
- Installation with pip
- Installation from source
- Quick Start
- Tutorial and Reference
- Help
- Windows (>= 7), Mac OS X (>= 10.8) or Linux
- Python >= 3.9
All other software dependencies are installed automatically when installing PHATE.
The Python version of PHATE can be installed by running the following from a terminal:
pip install phate
Installation of PHATE and all dependencies should take no more than five minutes.
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 PHATE/Python
python setup.py install --user
For more information, read the documentation on ReadTheDocs or view our tutorials on GitHub: single-cell RNA-seq, artificial tree. 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.