transmorph 0.0.7

Optimal transport-based tools for data integration.

Transmorph (anciently WOTi)

Transmorph is a python toolbox dedicated to transportation theory-based data analysis. Originally consisting in a data integration pipeline based on optimal transport and correcting for density, we are now adding extra features relevant in single-cell data analysis such as label transfer (already implemented), applications of Wasserstein barycenters, trajectory analysis and more.

Warning: This package is still in a very early stage of its development. Feel free to open an issue in case of unexpected behvior.

Installation

Requirements

These packages should be installed automatically by pip.

• numpy
• scipy
• osqp (quadratic program solver)
• POT (optimal transport in python)

Install from source (latest version)

git clone https://github.com/Risitop/transmorph
pip install ./transmorph

Install from PyPi (recommended, latest stable version)

pip install transmorph

Usage

Model fitting

We choose to adopt a philosophy similar to sklearn's package, with a numerical method encapsulated in a python object. The main class here is the Transmorph, and should be fitted prior to any analysis. First, you need to create a Transmorph object, selecting its parameters (transportation technique, entropic regularization, density correction...).

import transmorph as tr

t = tr.Transmorph(method='ot')

You can then load your two datasets and fit the Transmorph. You can provide extra arguments such as custom cost matrix (default is Euclidean distance).

X, Y = ... # datasets, np.ndarrays
t.fit(X, Y)

Data integration

Once the Transmorph is fitted, data integration is very straightforward through the transform method, following (Ferradans 2013) methodology.

X_integrated = t.transform()

Label transfer

Label transfer can be carried out to transfer labels from a dataset to the other in a semi-supervised fashion according to the optimal transport plan, following (Taherkhani 2020).

lY = ... # Gathering labels from Y dataset 
lX = t.label_transfer(lY)

Examples

See three example notebooks in examples/ directory.

Reference

https://www.biorxiv.org/content/10.1101/2021.05.12.443561v1

Documentation

Work in progress.