PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding) is a tool for visualizing high dimensional data.
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README.md

PHATE - Visualizing Transitions and Structure for Biological Data Exploration

Latest PyPI version Latest CRAN version Travis CI Build Read the Docs bioRxiv Preprint Twitter

Quick Start

If you would like to get started using PHATE, check out the following tutorials.

Introduction

PHATE is a tool for visualizing high dimensional data. In particular, PHATE is well-suited for visualizing data with natural progressions or trajectories such as single-cell data. PHATE uses a novel conceptual framework for learning and visualizing the manifold inherent to the system in which smooth transitions mark the progressions of data points (e.g. cells) from one state to another. 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. Kevin R. Moon, David van Dijk, Zheng Wang, et al. Visualizing Transitions and Structure for Biological Data Exploration. 2018. BioRxiv

PHATE has been implemented in Python >=3.5, MATLAB and R.

Table of Contents

System Requirements

All other software dependencies are installed automatically when installing PHATE.

Python

Installation with pip

The Python version of PHATE can be installed by running the following from a terminal:

pip install --user phate

Installation of PHATE and all dependencies should take no more than five minutes.

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 PHATE/Python
python setup.py install --user

Quick Start

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.

Tutorial and Reference

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.

MATLAB

Installation

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

Then, add the PHATE/Matlab directory to your MATLAB path.

Installation of PHATE should take no more than five minutes.

Tutorial and Reference

Run any of our run_* scripts to get a feel for PHATE. Documentation is available in the MATLAB help viewer.

R

In order to use PHATE in R, you must also install the Python package.

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/.

Installation from CRAN and PyPi

First install phate in Python by running the following code from a terminal:

pip install --user phate

Then, install phateR from CRAN by running the following code in R:

install.packages("phateR")

Installation of PHATE and all dependencies should take no more than five minutes.

Installation with devtools and reticulate

The development version of PHATE can be installed directly from R with devtools:

if (!suppressWarnings(require(devtools))) install.packages("devtools")
reticulate::py_install("phate", pip=TRUE)
devtools::install_github("KrishnaswamyLab/phateR")

Installation from source

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/Python
python setup.py install --user
cd ../phateR
R CMD INSTALL

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 Python
python setup.py install --user
cd ../phateR
R CMD INSTALL

Quick Start

If you have loaded a data matrix data in R (cells on rows, genes on columns) you can run PHATE as follows:

library(phateR)
data_phate <- phate(data)

phateR accepts R matrices, Matrix sparse matrices, data.frames, and any other data type that can be converted to a matrix with the function as.matrix.

Tutorial and Reference

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.

Help

If you have any questions or require assistance using PHATE, please contact us at https://krishnaswamylab.org/get-help.