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# scConfluence | ||
scConfluence is a novel method for the integration of unpaired multiomics data combining | ||
uncoupled autoencoders and Optimal Transport. Read the preprint! (!!!!add link!!!!) | ||
uncoupled autoencoders and Inverse Optimal Transport to learn low-dimensional cell | ||
embeddings. These embeddings can then be used for visualization and clustering, useful | ||
for discovering subpopulations of cells, and for imputation of features across | ||
modalities. | ||
Read the preprint! (!!!!add link!!!!) | ||
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![figure](model.png) | ||
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```bash | ||
git clone [email protected]:cantinilab/scconfluence.git | ||
pip install ./scconfluence/ | ||
cd scconfluence | ||
pip install . | ||
``` | ||
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### Test your installation (optional) | ||
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```bash | ||
pip install pytest | ||
pytest . | ||
``` | ||
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## Getting started | ||
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scConfluence takes as an input a MuData object and populates its `obsm` field | ||
with the latent embeddings. Visit !!!!!link docs!!!!! for more documentation and | ||
tutorials. | ||
with the latent embeddings. Visit (read the docs page currently under preparation) for | ||
more documentation and tutorials (which can be found now in the tutorials folder of | ||
this repository). | ||
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You may download a preprocessed 10X Multiome demo dataset [here](https://figshare.com/s/b0840d90e42e37fa165f). | ||
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``` | ||
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If you're looking for the repository with code to reproduce the experiments in our | ||
preprint, [here is is!](!!!! add link !!!!) | ||
preprint, [here it is!](https://github.com/cantinilab/scc_reproducibility) |