diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..63d21ea --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,20 @@ +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the version of Python and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.10" + +# We recommend specifying your dependencies to enable reproducible builds: +# https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html +python: + install: + - method: pip + path: . + extra_requirements: + - docsbuild \ No newline at end of file diff --git a/README.md b/README.md index e231502..6eeda18 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,15 @@ # scConfluence +[![Tests](https://github.com/cantinilab/scconfluence/actions/workflows/main.yml/badge.svg)](https://github.com/cantinilab/scconfluence/actions/workflows/main.yml) +[![codecov](https://codecov.io/github/cantinilab/scconfluence/graph/badge.svg?token=JCLE8L9GNH)](https://codecov.io/github/cantinilab/scconfluence) +[![PyPI version](https://img.shields.io/pypi/v/scconfluence?color=blue)](https://img.shields.io/pypi/v/scconfluence?color=blue) +[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) + 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! (!!!!add link!!!!) +[Read the preprint!](https://www.biorxiv.org/content/10.1101/2024.02.26.582051v1) ![figure](model.png) @@ -13,7 +18,7 @@ Read the preprint! (!!!!add link!!!!) scConfluence is implemented as a Python package seamlessly integrated within the scverse ecosystem, in particular Muon and Scanpy. -### via PyPI (!!!!!!!!! not yet available!!!!!!!) +### via PyPI 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 @@ -80,9 +85,16 @@ sc.pl.umap(mdata) ``` ## Our preprint - +https://www.biorxiv.org/content/10.1101/2024.02.26.582051v1 ```bibtex -!!!! add citation !!!! +@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} +} ``` If you're looking for the repository with code to reproduce the experiments in our diff --git a/poetry.lock b/poetry.lock index eb22152..ee73705 100644 --- a/poetry.lock +++ b/poetry.lock @@ -121,6 +121,17 @@ files = [ [package.dependencies] frozenlist = ">=1.1.0" +[[package]] +name = "alabaster" +version = "0.7.16" +description = "A light, configurable Sphinx theme" +optional = true 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""" - # theta is the dispersion rate. If .ndimension() == 1, it is shared for all cells (regardless of batch or labels) + # theta is the dispersion rate. If .ndimension() == 1, it is shared for all cells + # (regardless of batch or labels) if theta.ndimension() == 1: theta = theta.view( 1, theta.size(0) @@ -224,7 +225,7 @@ def variance(self): def sample( self, sample_shape: Union[torch.Size, Tuple] = torch.Size() - ) -> torch.Tensor: + ) -> torch.Tensor: # pragma: no cover with torch.no_grad(): gamma_d = self._gamma() p_means = gamma_d.sample(sample_shape) @@ -330,7 +331,7 @@ def zi_probs(self) -> torch.Tensor: def sample( self, sample_shape: Union[torch.Size, Tuple] = torch.Size() - ) -> torch.Tensor: + ) -> torch.Tensor: # pragma: no cover with torch.no_grad(): samp = super().sample(sample_shape=sample_shape) is_zero = torch.rand_like(samp) <= self.zi_probs