=====================
Cell Maps CoEmbedder
The Cell Maps CoEmbedding is part of the Cell Mapping Toolkit
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Creates Coembedding from Cell Maps ImmunoFluorscent Image Embedder <https://cellmaps-image-embedding.readthedocs.io>
__
and Cell Maps PPI Embedder <https://cellmaps-ppi-embedding.readthedocs.io>
__ using an implementation of MUSE <https://github.com/AltschulerWu-Lab/MUSE>
__
Dependencies
cellmaps_utils <https://pypi.org/project/cellmaps-utils>
__phenograph <https://pypi.org/project/phenograph>
__numpy <https://pypi.org/project/numpy>
__torch <https://pypi.org/project/torch>
__pandas <https://pypi.org/project/pandas>
__matplotlib <https://pypi.org/project/matplotlib>
__dill <https://pypi.org/project/dill>
__tqdm <https://pypi.org/project/tqdm>
__
Compatibility
Installation
.. code-block::
git clone https://github.com/idekerlab/cellmaps_coembedding
cd cellmaps_coembedding
make dist
pip install dist/cellmaps_coembedding*whl
Run make command with no arguments to see other build/deploy options including creation of Docker image
.. code-block::
make
Output:
.. code-block::
clean remove all build, test, coverage and Python artifacts
clean-build remove build artifacts
clean-pyc remove Python file artifacts
clean-test remove test and coverage artifacts
lint check style with flake8
test run tests quickly with the default Python
test-all run tests on every Python version with tox
coverage check code coverage quickly with the default Python
docs generate Sphinx HTML documentation, including API docs
servedocs compile the docs watching for changes
testrelease package and upload a TEST release
release package and upload a release
dist builds source and wheel package
install install the package to the active Python's site-packages
dockerbuild build docker image and store in local repository
dockerpush push image to dockerhub
Before running tests, please install pip install -r requirements_dev
.
For developers
To deploy development versions of this package
Below are steps to make changes to this code base, deploy, and then run
against those changes.
#. Make changes
Modify code in this repo as desired
#. Build and deploy
.. code-block::
# From base directory of this repo cellmaps_coembedding
pip uninstall cellmaps_coembedding -y ; make clean dist; pip install dist/cellmaps_coembedding*whl
Needed files
------------
The output directories for the image embeddings (see `Cell Maps Image Embedding <https://github.com/idekerlab/cellmaps_image_embedding/>`__) and protein-protein interaction network embeddings (see `Cell Maps PPI Embedding <https://github.com/idekerlab/cellmaps_ppi_embedding/>`__) are required.
Usage
-----
For information invoke :code:`cellmaps_coembeddingcmd.py -h`
**Example usage**
.. code-block::
cellmaps_coembeddingcmd.py ./cellmaps_coembedding_outdir --embeddings ./cellmaps_image_embedding_outdir ./cellmaps_ppi_embedding_outdir
Via Docker
~~~~~~~~~~~~~~~~~~~~~~
**Example usage**
.. code-block::
Coming soon...
Credits
-------
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
.. _NDEx: http://www.ndexbio.org
=======
History
=======
0.4.0 (2024-12-02)
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* Added README generation.
* Refactor code.
0.3.1 (2024-09-13)
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* Bug fix: raise more informative error when no embeddings overlap.
0.3.0 (2024-09-06)
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* Added ``--provenance`` flag to pass a path to json file with provenance information. This removes the
necessity of input directory to be an RO-Crate.
0.2.0 (2024-07-17)
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* Added a new coembedding algorithm accessible via flag ``--algorithm auto``. This algorithm utilizes neural networks
to generate latent embeddings, optimizing both reconstruction and triplet losses to improve embedding accuracy
by learning intra- and inter-modality relationships.
0.1.0 (2024-02-12)
------------------
* First release on PyPI.