topometry 0.2.0.0

A comprehensive dimensional reduction framework to recover latent information from data.

TopOMetry - Topologically Optimized geoMetry

A global framework for dimensionality reduction: learning topologic metrics, orthonormal bases and graph layouts

TopOMetry is a high-level python library to explore data topology through manifold learning. It is compatible with scikit-learn, meaning most of its operators can be easily pipelined.

Its main idea is to approximate the Laplace-Beltrami Operator (LBO). This is done by learning properly weighted similarity graphs and their Laplacian and Diffusion operators. By definition, the eigenfunctions of these operators describe all underlying data topology in an set of orthonormal eigenbases (classically named the spectral or diffusion components). New topological operators are then learned from such eigenbases and can be used for clustering and graph-layout optimization (visualization).

For more information, see the manuscript.

TopOMetry was designed to handle large-scale data matrices containing extreme sample diversity, such as those generated from single-cell omics. It includes wrappers to deal with AnnData objects using scanpy.

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Documentation

Documentation is available at Read The Docs. There you'll find installation instructions, tutorials, walkthroughs and a detailed API for reference.

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Contributing

Contributions are very welcome! If you're interested in adding a new feature, just let me know in the Issues section.

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License

MIT License

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Citation

If you find TopOMetry useful for your work, please cite our manuscript:

@article {Sidarta-Oliveira2022.03.14.484134,
	author = {Sidarta-Oliveira, Davi and Velloso, Licio A},
	title = {A comprehensive dimensional reduction framework to learn single-cell phenotypic topology uncovers T cell diversity},
	elocation-id = {2022.03.14.484134},
	year = {2022},
	doi = {10.1101/2022.03.14.484134},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2022/03/17/2022.03.14.484134},
	eprint = {https://www.biorxiv.org/content/early/2022/03/17/2022.03.14.484134.full.pdf},
	journal = {bioRxiv}
}