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An extension of the [Monge Gap](https://proceedings.mlr.press/v202/uscidda23a.html), an approach to estimate transport maps conditionally on arbitrary context vectors. It is based on a two-step training procedure combining an encoder-decoder architecture with an OT estimator. The model is applied to [4i](https://pubmed.ncbi.nlm.nih.gov/30072512/) and [scRNA-seq](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7289078/) datasets.
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## Systems and software requirements
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Software package requirements and version information can be found in `requirements.txt` and/or the `pyproject.toml`. This package has been tested on Python versions 3.10 and 3.11.
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Software package requirements and version information are defined in `pyproject.toml` and locked in `uv.lock`. This package has been tested on Python versions 3.10 and 3.11.
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Hardware requirements enough memory (RAM) to process the data and batches. GPU is not needed but does accelerate computation. This software has been tested on HPCs and local machines (iOS).
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## Installation from PyPI
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which should take about two minutes on a laptop.
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## Development setup & installation
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The package environment is managed by [poetry](https://python-poetry.org/docs/managing-environments/).
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The package environment is managed by [uv](https://docs.astral.sh/uv/).
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