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.
To install Mellon using pip you can run:
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.
Please read the documentation or use this basic tutorial notebook.
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)
The Mellon manuscript is available on Nature Methods and a preprint 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},
}
You can find our reproducibility repository to reproduce benchmarks and plots of the paper here.
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