Mellon
======
|zenodo| |codecov| |pypi| |conda|
.. image:: https://github.com/settylab/mellon/raw/main/landscape.png?raw=true
:target: https://github.com/settylab/Mellon
Mellon is a non-parametric cell-state density estimator based on a
nearest-neighbors-distance distribution. It uses a sparse gaussian process
to produce a differntiable density function that can be evaluated out of sample.
Installation
============
To install Mellon using **pip** you can run:
.. code-block:: bash
pip install mellon
or to install using **conda** you can run:
.. code-block:: bash
conda install -c conda-forge mellon
or to install using **mamba** you can run:
.. code-block:: bash
mamba install -c conda-forge mellon
Any of these calls should install Mellon and its dependencies within less than 1 minute.
If the dependency jax is not autimatically installed, please refer to https://github.com/google/jax.
Documentation
=============
Please read the
`documentation <https://mellon.readthedocs.io/en/latest/index.html>`_
or use this
`basic tutorial notebook <https://github.com/settylab/Mellon/blob/main/notebooks/basic_tutorial.ipynb>`_.
Basic Usage
===========
.. code-block:: python
import mellon
import numpy as np
X = np.random.rand(100, 10) # 10-dimensional state representation for 100 cells
Y = np.random.rand(100, 10) # arbitrary test data
model = mellon.DensityEstimator()
log_density_x = model.fit_predict(X)
log_density_y = model.predict(Y)
Citations
=========
The Mellon manuscript is available on
`Nature Methods <https://www.nature.com/articles/s41592-024-02302-w>`_
and a preprint on
`bioRxiv <https://www.biorxiv.org/content/10.1101/2023.07.09.548272v1>`_.
If you use Mellon for your work, please cite our paper.
.. code-block:: bibtex
@article{ottoQuantifyingCellstateDensities2024,
title = {Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes Using {{Mellon}}},
author = {Otto, Dominik J. and Jordan, Cailin and Dury, Brennan and Dien, Christine and Setty, Manu},
date = {2024-06-18},
journaltitle = {Nature Methods},
issn = {1548-7105},
doi = {10.1038/s41592-024-02302-w},
url = {https://www.nature.com/articles/s41592-024-02302-w},
}
You can find our
`reproducibility repository <https://github.com/settylab/MellonReproducibility>`_
to reproduce benchmarks and plots of the paper
`here <https://github.com/settylab/MellonReproducibility>`_.
.. |zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.8404223.svg
:target: https://doi.org/10.5281/zenodo.8404223
.. |codecov| image:: https://codecov.io/github/settylab/Mellon/branch/main/graph/badge.svg?token=TKIKXK4MPG
:target: https://app.codecov.io/github/settylab/Mellon
.. |pypi| image:: https://badge.fury.io/py/mellon.svg
:target: https://badge.fury.io/py/mellon
.. |conda| image:: https://anaconda.org/conda-forge/mellon/badges/version.svg
:target: https://anaconda.org/conda-forge/mellon
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"description": "Mellon\n======\n\n|zenodo| |codecov| |pypi| |conda|\n\n.. image:: https://github.com/settylab/mellon/raw/main/landscape.png?raw=true\n :target: https://github.com/settylab/Mellon\n\nMellon is a non-parametric cell-state density estimator based on a\nnearest-neighbors-distance distribution. It uses a sparse gaussian process\nto produce a differntiable density function that can be evaluated out of sample.\n\nInstallation\n============\n\nTo install Mellon using **pip** you can run:\n\n.. code-block:: bash\n\n pip install mellon\n\nor to install using **conda** you can run:\n\n.. code-block:: bash\n\n conda install -c conda-forge mellon\n\nor to install using **mamba** you can run:\n\n.. code-block:: bash\n\n mamba install -c conda-forge mellon\n\nAny of these calls should install Mellon and its dependencies within less than 1 minute.\nIf the dependency jax is not autimatically installed, please refer to https://github.com/google/jax.\n\nDocumentation\n=============\n\nPlease read the\n`documentation <https://mellon.readthedocs.io/en/latest/index.html>`_\nor use this\n`basic tutorial notebook <https://github.com/settylab/Mellon/blob/main/notebooks/basic_tutorial.ipynb>`_.\n\n\nBasic Usage\n===========\n\n.. code-block:: python\n\n import mellon\n import numpy as np\n\n X = np.random.rand(100, 10) # 10-dimensional state representation for 100 cells\n Y = np.random.rand(100, 10) # arbitrary test data\n\n model = mellon.DensityEstimator()\n log_density_x = model.fit_predict(X)\n log_density_y = model.predict(Y)\n\nCitations\n=========\n\nThe Mellon manuscript is available on\n`Nature Methods <https://www.nature.com/articles/s41592-024-02302-w>`_\nand a preprint on\n`bioRxiv <https://www.biorxiv.org/content/10.1101/2023.07.09.548272v1>`_.\nIf you use Mellon for your work, please cite our paper.\n\n.. code-block:: bibtex\n\n @article{ottoQuantifyingCellstateDensities2024,\n title = {Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes Using {{Mellon}}},\n author = {Otto, Dominik J. and Jordan, Cailin and Dury, Brennan and Dien, Christine and Setty, Manu},\n date = {2024-06-18},\n journaltitle = {Nature Methods},\n issn = {1548-7105},\n doi = {10.1038/s41592-024-02302-w},\n url = {https://www.nature.com/articles/s41592-024-02302-w},\n }\n\nYou can find our\n`reproducibility repository <https://github.com/settylab/MellonReproducibility>`_\nto reproduce benchmarks and plots of the paper\n`here <https://github.com/settylab/MellonReproducibility>`_.\n\n\n.. |zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.8404223.svg\n :target: https://doi.org/10.5281/zenodo.8404223\n.. |codecov| image:: https://codecov.io/github/settylab/Mellon/branch/main/graph/badge.svg?token=TKIKXK4MPG \n :target: https://app.codecov.io/github/settylab/Mellon\n.. |pypi| image:: https://badge.fury.io/py/mellon.svg\n :target: https://badge.fury.io/py/mellon\n.. |conda| image:: https://anaconda.org/conda-forge/mellon/badges/version.svg\n :target: https://anaconda.org/conda-forge/mellon\n",
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