PHATE - Potential of Heat-diffusion for Affinity-based Trajectory Embedding
PHATE is a tool for visualizing high dimensional single-cell data with natural progressions or trajectories. PHATE uses a novel conceptual framework for learning and visualizing the manifold inherent to biological systems in which smooth transitions mark the progressions of cells from one state to another. To see how PHATE can be applied to single-cell RNA-seq datasets from hematopoietic stem cells, human embryonic stem cells, and bone marrow samples, check out our preprint on BioRxiv.
PHATE has been implemented in Python3 and Matlab.
Python installation and dependencies
The Python3 version of PHATE can be installed using:
$ git clone git://github.com/SmitaKrishnaswamy/PHATE.git $ cd Python $ python3 setup.py install --user
PHATE depends on a number of
python3packages available on pypi and these dependencies are listed in
setup.pyAll the dependencies will be automatically installed using the above commands
The MATLAB version of PHATE can be accessed using:
$ git clone git://github.com/SmitaKrishnaswamy/PHATE.git $ cd PHATE/Matlab
Add the PHATE/Matlab directory to your MATLAB path and run any of our
testscripts to get a feel for PHATE.
A demo on PHATE usage and visualization for single cell RNA-seq data can be found in this notebook: https://nbviewer.jupyter.org/github/SmitaKrishnaswamy/PHATE/blob/master/Python/test/phate_examples.ipynb