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.
PHATE has been implemented in Python >=3.5, R and MATLAB.
Python installation and dependencies
Installation with pip
The Python version of PHATE can be installed using:
pip install --user phate
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 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.
Usage
PHATE has been implemented with an API that should be familiar to those with experience using scikit-learn. The core of the PHATE package is the PHATE class which is a subclass of sklearn.base.BaseEstimator. To get started, import phate and instantiate a phate.PHATE() object. Just like most sklearn estimators, PHATE() objects have both fit() and fit_transform() methods. For more information, check out our notebook below.
If you want to try running our test script on a DLA fractal tree, run the following in a Python interpreter:
import phate tree_data, tree_clusters = phate.tree.gen_dla() phate_operator = phate.PHATE(k=15, t=100) tree_phate = phate_operator.fit_transform(tree_data) phate.plot.scatter2d(phate_operator, c=tree_clusters) # or phate.plot.scatter2d(tree_phate, c=tree_clusters) phate.plot.rotate_scatter3d(phate_operator, c=tree_clusters)
Jupyter Notebooks
A demo on PHATE usage and visualization for single cell RNA-seq data can be found in this Jupyter notebook. A second tutorial is available here which works with the artificial tree shown above in more detail. 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
4 maintainers
Metadata
4 maintainers
Release files for phate 0.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| phate-0.4.0.tar.gz | 20.7 kB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| phate-0.4.0-py3.7.egg | Legacy Egg format | - | - | Details |
| phate-0.4.0-py3.6.egg | Legacy Egg format | - | - | Details |
| phate-0.4.0-py3.5.egg | Legacy Egg format | - | - | Details |
| phate-0.4.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 171.4 kB
Release files / phate-0.4.0.tar.gz
| Download URL | phate-0.4.0.tar.gz |
|---|---|
| Size | 20.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c21f0812a0940f7daba218a6622ace1f40d09f312926f511647ffca92b9c98a9
|
|
BLAKE2b-256 checksum How to use checksums |
f035cb37bbf060708452160b9e895768641d9befb50a4e90dd264cc8472c615a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.12.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.30.0 CPython/3.6.3
|
Release files / phate-0.4.0-py3.7.egg
| Download URL | phate-0.4.0-py3.7.egg |
|---|---|
| Size | 43.0 kB |
| Tags | Egg |
|
SHA-256 checksum How to use checksums |
d35b9557f43728105e653fe10294256cf8181834946133ddc3d43b21c6a3ba45
|
|
BLAKE2b-256 checksum How to use checksums |
168cfde1d8de0b8190597895123fe688c99f3d8e89b4f8dab60d85bd1e44bf43
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.12.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.30.0 CPython/3.7.0a4+
|
Release files / phate-0.4.0-py3.6.egg
| Download URL | phate-0.4.0-py3.6.egg |
|---|---|
| Size | 43.0 kB |
| Tags | Egg |
|
SHA-256 checksum How to use checksums |
51f0b8ffa632f99b396644036f8369773e168f1f9b7f05c0ea8e58b52ab052c2
|
|
BLAKE2b-256 checksum How to use checksums |
07245d0a7e44abaf462cc0830b979efa79588dc285c32cd77eb6f14f996f8354
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.12.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.30.0 CPython/3.6.3
|
Release files / phate-0.4.0-py3.5.egg
| Download URL | phate-0.4.0-py3.5.egg |
|---|---|
| Size | 43.5 kB |
| Tags | Egg |
|
SHA-256 checksum How to use checksums |
2bde51709f6c275146c118be55efa259aaa601c3e779169a84d95a707f1889f0
|
|
BLAKE2b-256 checksum How to use checksums |
1abb97dc1d769c2dc9bb4f4a681651afb0ec9995618485125d1421ee0b8c6cf3
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.12.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.30.0 CPython/3.5.6
|
Release files / phate-0.4.0-py3-none-any.whl
| Download URL | phate-0.4.0-py3-none-any.whl |
|---|---|
| Size | 21.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
b97a54a8c2f87793da10efb64222ddbeb8b24547585748ac4e60c713c1edab5a
|
|
BLAKE2b-256 checksum How to use checksums |
aa6bf106d52abcfcac3f2d493d977bf7483d23fb813a5232d6ed0123988ce22f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.12.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.30.0 CPython/3.6.3
|
