phate

PHATE - Visualizing Transitions and Structure for Biological Data Exploration

Quick Start

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.

Introduction

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.

Moon, van Dijk, Wang, Gigante et al. Visualizing Transitions and Structure for Biological Data Exploration. 2019. 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

System Requirements

Windows (>= 7), Mac OS X (>= 10.8) or Linux
Python >= 3.9

All other software dependencies are installed automatically when installing PHATE.

Installation with `pip`

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.

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

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.

Help

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

Release files for phate 2.0.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for phate 2.0.0
File	Size	Uploaded
phate-2.0.0.tar.gz	46.6 kB	Oct 27, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for phate 2.0.0
File	Interpreter	ABI	Platform	Reset
phate-2.0.0-py3-none-any.whl 52.9 kB Oct 27, 2025	Python 3	none	any	Details

Total release size: 99.5 kB

Release files / phate-2.0.0.tar.gz

Download URL	phate-2.0.0.tar.gz
Size	46.6 kB
Tags	Source
SHA-256 checksum How to use checksums	`4605f33b8ca625e8fe9b6a9552a7113f60f86afd816ba9495559494f1e1b2e26`
BLAKE2b-256 checksum How to use checksums	`7b1d29bf3537a26f9d8adb54190a5f4edc480cd8899a66422f1d0ef55f2a53b7`
Upload date	Oct 27, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.9.24`

Release files / phate-2.0.0-py3-none-any.whl

Download URL	phate-2.0.0-py3-none-any.whl
Size	52.9 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`d0ca74b188d5397be78a5be11322ca0841738b73f3e27986a6f8eb7e799d2d73`
BLAKE2b-256 checksum How to use checksums	`327fab3e73eac07a3d7f0dae6f81028744f2702512cb5a1376870e6de95b7450`
Upload date	Oct 27, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.9.24`