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 Python (2.7 and >=3.5), MATLAB and R.
The Python version of PHATE can be installed by running the following from a terminal:
pip install --user phate
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.
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The MATLAB version of PHATE can be accessed by running the following from a terminal:
git clone --recursive git://github.com/KrishnaswamyLab/PHATE.git cd PHATE/Matlab -
Add the PHATE/Matlab directory to your MATLAB path.
Run any of our run_* scripts to get a feel for PHATE. Documentation is available in the MATLAB help viewer.
In order to use PHATE in R, you must also install the Python package.
Install phateR from CRAN by running the following code in R:
install.packages("phateR")
Install phate in Python by running the following code from a terminal:
pip install --user phate
If python or pip are not installed, you will need to install them. We recommend Miniconda3 to install Python and pip together, or otherwise you can install pip from https://pip.pypa.io/en/stable/installing/.
The development version of PHATE can be installed directly from R with devtools:
if (!suppressWarnings(require(devtools))) install.packages("devtools")
devtools::install_github("KrishnaswamyLab/phateR")
If you have the development version of reticulate, you can also install phate in Python by running the following code in R:
devtools::install_github("rstudio/reticulate")
reticulate::py_install("phate", pip=TRUE)
The latest source version of PHATE can be accessed by running the following in a terminal:
git clone --recursive git://github.com/SmitaKrishnaswamy/PHATE.git
cd PHATE/phateR
R CMD INSTALL
cd ../Python
python setup.py install --user
If the phateR folder is empty, you have may forgotten to use the --recursive option for git clone. You can rectify this by running the following in a terminal:
cd PHATE
git submodule init
git submodule update
cd phateR
R CMD INSTALL
cd ../Python
python setup.py install --user
For more information and a tutorial, read the phateR README. Documentation is available in the R help viewer with help(phateR::phate). A tutorial notebook running PHATE on a single-cell RNA-seq dataset is available at http://htmlpreview.github.io/?https://github.com/KrishnaswamyLab/phateR/blob/master/inst/examples/bonemarrow_tutorial.html or in phateR/inst/examples.
If you have any questions or require assistance using PHATE, please contact us at https://krishnaswamylab.org/get-help.
