This Python package will allow you to replicate the experiments from our research on applying Optimal Transport as a similarity metric in between single-cell omics data.
We propose the use of Optimal Transport (OT) as a cell-cell similarity metric for single-cell omics data. The code in this repository implements entropic-regularized OT distance computation with PyTorch, and applies it to public datasets of single-cell omics data. We compare the results to commonly used metrics like the euclidean distance or Pearson correlation, and demonstrate that OT increases performances in cell-cell similarity inference.
Computing pairwise distance matrices is expensive (O(n²) operations for n cells), so we advise users of the package to limit the number of cells studied to a few thousand.
pip install otscomics
The documentation includes a Jupyter Notebook demonstrating the package. While this notebook can be run un CPU, computations will be faster on GPU.
If you do not have access to a GPU you may want to use the (free) Google Colab platform to run this notebook: colab.research.google.com/github/ComputationalSystemsBiology/.../OT_scOmics.ipynb.
Please make sure that the GPU is enabled. Navigate to "Edit→Notebook Settings" and select "GPU" from the "Hardware Accelerator" drop-down.
The paper describing OT-scOmics has been published on Bioinformatics.
Open Access link (Huizing, Peyré, Cantini, 2022)
@article{10.1093/bioinformatics/btac084,
author = {Huizing, Geert-Jan and Peyré, Gabriel and Cantini, Laura},
title = "{Optimal transport improves cell–cell similarity inference in single-cell omics data}",
journal = {Bioinformatics},
volume = {38},
number = {8},
pages = {2169-2177},
year = {2022},
month = {02},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btac084},
url = {https://doi.org/10.1093/bioinformatics/btac084},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/38/8/2169/43370259/btac084.pdf},
}