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.. automodule:: scconfluence.base_module | ||
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:undoc-members: | ||
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.. automodule:: scconfluence.dataset_utils | ||
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:undoc-members: | ||
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.. automodule:: scconfluence.distributions | ||
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@@ -7,14 +7,108 @@ Welcome to scConfluence's documentation! | |
======================================== | ||
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.. toctree:: | ||
:maxdepth: 2 | ||
:caption: Contents: | ||
:maxdepth: 1 | ||
:caption: Tutorials | ||
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tutorials/*.ipynb | ||
.. toctree:: | ||
:hidden: | ||
:maxdepth: 3 | ||
:caption: API | ||
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scConfluence is a novel method for the integration of unpaired multiomics data combining | ||
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! <https://www.biorxiv.org/content/10.1101/2024.02.26.582051v1>`_ | ||
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.. image:: ../../model.png | ||
:alt: Explanatory figure | ||
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Install the package | ||
------------------- | ||
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scConfluence is implemented as a Python package seamlessly integrated within the scverse | ||
ecosystem, in particular Muon and Scanpy. | ||
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via PyPI | ||
^^^^^^^^ | ||
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On all operating systems, the easiest way to install scConfluence is via PyPI. | ||
Installation should typically take a minute and is continuously tested with Python 3.10 | ||
on an Ubuntu virtual machine. | ||
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.. code-block:: bash | ||
pip install scconfluence | ||
via GitHub (development version) | ||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
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.. code-block:: bash | ||
git clone [email protected]:cantinilab/scconfluence.git | ||
cd scconfluence | ||
pip install . | ||
Test your development installation (optional) | ||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
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.. code-block:: bash | ||
pip install pytest | ||
pytest . | ||
Getting started | ||
--------------- | ||
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scConfluence takes as an input a MuData object and populates its `obsm` field | ||
with the latent embeddings. See tutorials for more detailed examples of applications of the method. | ||
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You may download a preprocessed 10X Multiome demo dataset `here <https://figshare.com/s/b0840d90e42e37fa165f>`_. | ||
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A GPU is not required for the method to run, but is strongly recommended. | ||
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.. code-block:: python | ||
import scconfluence | ||
import mudata as md | ||
import scanpy as sc | ||
from scipy.spatial.distance import cdist | ||
# Load data into a Muon object. | ||
mdata = md.read_h5mu("my_data.h5mu") | ||
# Compute the cross-modality distance matrix using connected features | ||
mdata.uns["cross_modality1+modality2"] = cdist(mdata["modality1"].obsm["cm_features"], | ||
mdata["modality2"].obsm["cm_features"]) | ||
mdata.uns["cross_keys"] = ["cross_modality1+modality2"] | ||
# Initialize and train the model. | ||
autoencoders = {"modality1": scconfluence.model.AutoEncoder(mdata["modality1"], | ||
modality="modality1"), | ||
"modality2": scconfluence.model.AutoEncoder(mdata["modality2"], | ||
modality="modality2")} | ||
model = scconfluence.model.ScConfluence(mdata, unimodal_aes=autoencoders) | ||
model.fit(save_path="results") | ||
mdata.obsm["latent"] = model.get_latent().loc[mdata.obs_names] | ||
# Visualize the embedding with UMAP. | ||
sc.pp.neighbors(mdata, use_rep="latent") | ||
sc.tl.umap(mdata) | ||
sc.pl.umap(mdata) | ||
Citation | ||
-------- | ||
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Indices and tables | ||
================== | ||
.. code-block:: bibtex | ||
* :ref:`genindex` | ||
* :ref:`modindex` | ||
* :ref:`search` | ||
@article {Samaran2024unpaired, | ||
author = {Jules Samaran and Gabriel Peyre and Laura Cantini}, | ||
title = {scConfluence : single-cell diagonal integration with regularized Inverse Optimal Transport on weakly connected features}, | ||
year = {2024}, | ||
doi = {10.1101/2024.02.26.582051}, | ||
publisher = {Cold Spring Harbor Laboratory}, | ||
journal = {bioRxiv} | ||
} |
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.. automodule:: scconfluence.model | ||
:members: | ||
:undoc-members: | ||
:show-inheritance: |
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.. automodule:: scconfluence.nn | ||
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:undoc-members: | ||
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.. automodule:: scconfluence.unimodal | ||
:members: | ||
:undoc-members: | ||
:show-inheritance: |