Non-parametric density estimator.
Project description
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:
pip install mellon
or to install using conda you can run:
conda install -c conda-forge mellon
or to install using mamba you can run:
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 or use this basic tutorial notebook.
Basic Usage
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 bioRxiv If you use Mellon for your work, please cite our paper.
@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},
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.