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    "info": {
        "author": "Albert Zhou",
        "author_email": "j.zhou.3@bham.ac.uk",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
            "Operating System :: POSIX",
            "Programming Language :: Python :: 2",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "# Moca\n\nMOCA (**M**ultiomics m**O**dule **C**orrelation **A**nalysis) is a Python tool to comprehensively use coexpression \nmodule analysis and sparse canonical correlation analysis to identify modules, i.e., feature subsets that are highly \ncorrelated both within and between the omics levels. \n\nMOCA is built on top of Python 2.7 and will be compatible with Python 3.7 in the near future. \n\nMOCA is distributed under the GNU Lesser General Public License v3.0.\n\n## Installation\n\nUsing Docker\n```bash\ndocker pull albertaki/jupyter-lab:0.35.4-moca\n```\n\nUsing pip:\n```bash\npip install moca-py\n```\n\nUsing Pipenv:\n```bash\npipenv install moca-py\n```\n\n## Dependencies\n\nPlease note that you don't need to manually install the python dependencies.\n\nFor the pip and Pipenv users, please install the following R dependencies.\n\n- R (>= 3.4.4)\n- dynamicTreeCut\n- RGCCA\n- fastcluster\n\n## Changelog\n\n*Version 1.1 (beta)*\n\n- Add sample alignment based ensemble for CCA\n- Add hard (subspace disjoint) deflation strategy for CCA\n- Add CCA loading tables ensemble for various component numbers\n- Add coexpression module differential analysis\n- Add ID mapping\n- Add plots for the pipelines\n- Add JupyterLab notebook\n- Add documentations\n- Add Docker images\n\n## Documentation\n\n**GitHub Pages:** https://akialbert.github.io/Moca/\n\n## Citation\n\n*TO BE ADDED*\n\n\n",
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