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 {Otto2023.07.09.548272,
author = {Dominik Jenz Otto and Cailin Jordan and Brennan Dury and Christine Dien and Manu Setty},
title = {Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes using Mellon},
elocation-id = {2023.07.09.548272},
year = {2023},
doi = {10.1101/2023.07.09.548272},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2023/07/10/2023.07.09.548272},
eprint = {https://www.biorxiv.org/content/early/2023/07/10/2023.07.09.548272.full.pdf},
journal = {bioRxiv}
}