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    "info": {
        "author": "University of Eastern Finland",
        "author_email": "trung@imito.ai",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "SISUA\n=====\n\n|SISUA_design|\n\n.. |SISUA_design| image:: https://drive.google.com/uc?export=view&id=1PvvG61_Rgbv_rqT6sCeb1XB6CtdiCMXX\n  :width: 405\n  :height: 249\n\n\nSemi-supervised Single-cell modeling:\n\n* Free software: MIT license\n* Documentation: https://github.com/trungnt13/sisua/tree/master/docs.\n\nReference:\n\n* Trung Ngo Trong, Roger Kramer, Juha Mehtonen, Gerardo Gonz\u00e1lez, Ville Hautam\u00e4ki, Merja Hein\u00e4niemi. **\"SISUA: SemI-SUpervised Generative Autoencoder for Single Cell Data\"**, ICML Workshop on Computational Biology, 2019. `[pdf]`__\n\n.. __: https://doi.org/10.1101/631382\n\n\nInstallation\n************\n\nYou only need ``Python 3.6``, the stable version of SISUA installed via pip:\n\n  ``pip install sisua``\n\nInstall the nightly version on github:\n\n  ``pip install git+https://github.com/trungnt13/sisua@master``\n\nFor developers, we create a conda environment for SISUA contribution `sisua_env`__\n\n  ``conda env create -f=sisua_env.yml``\n\n.. __: https://github.com/trungnt13/sisua/blob/master/sisua_env.yml\n\nGetting started\n***************\n\na. The basics:\n    * `Datasets description`__\n    * `Models specification`\n    * `Basic API and work-flow`__\nb. Single-cell analysis:\n    * `Latent space`\n    * `Imputation of genes expression`\n    * `Prediction of protein markers`\nc. Advanced technical topics:\n    * `Probabilistic embedding`__\n    * `Hierarchical modeling` (*coming soon*)\n    * `Causal analysis` (*coming soon*)\n    * `Cross datasets analysis` (*coming soon*)\nd. Benchmarks:\n    * `Scalability test`__\n    * `Fine-tuning networks`\n    * `Data normalization`\ne. Further development:\n    * `Roadmap`__\n    * `SISUA 2`__\n\n.. __: https://github.com/trungnt13/sisua/blob/master/docs/dataset_description.md\n.. __: https://github.com/trungnt13/sisua/blob/master/tutorials/basics.py\n.. __: https://github.com/trungnt13/sisua/blob/master/tutorials/probabilistic_embedding.py\n.. __: https://github.com/trungnt13/sisua/blob/master/tests/scalability.py\n.. __:\n.. __:\n\nToolkits\n********\n\nWe provide binary toolkits for *fast and efficient* analyzing single-cell datasets:\n\n* `sisua-train`__: train single-cell modeling algorithms, support training multiple systems in parallel.\n* `sisua-analyze`__: evaluate, compare, and interpret trained model.\n* `sisua-embed`__: probabilistic embedding for semi-supervised training.\n* `sisua-data`__: *coming soon*\n\n\n.. __: https://github.com/trungnt13/sisua/blob/master/bin/README.rst\n.. __: https://github.com/trungnt13/sisua/blob/master/bin/README.rst\n.. __: https://github.com/trungnt13/sisua/blob/master/bin/README.rst\n.. __: https://github.com/trungnt13/sisua/blob/master/bin/README.rst\n\nSome important arguments:\n\n-model\n            name of function declared in models__\n\n            - ``scvi``: single-cell Variational Inference model\n            - ``dca``: Deep Count Autoencoder\n            - ``vae``: single-cell Variational Autoencoder\n            - ``movae``: SISUA\n-ds\n            name of dataset declared in data__.\n\n            Description of all predefined datasets is in docs__.\n\n            Some good datasets for practicing:\n\n            - ``pbmc8k_ly``\n            - ``cortex``\n            - ``pbmcecc_ly``\n            - ``pbmcscvi``\n            - ``pbmcscvae``\n\n.. __: https://github.com/trungnt13/sisua/tree/master/sisua/models\n.. __: https://github.com/trungnt13/sisua/tree/master/sisua/data\n.. __: https://github.com/trungnt13/sisua/blob/master/docs/dataset_description.md\n\nConfiguration\n*************\n\nBy default, the data will be saved at your home folder at ``~/bio_data``,\nand the experiments' outputs will be stored at ``~/bio_log``\n\nYou can customize these two paths using the environment variables:\n\n* For storing downloaded and preprocessed data: ``SISUA_DATA``\n* For the experiments: ``SISUA_EXP``\n\nFor example:\n\n.. code-block:: python\n\n  import os\n  os.environ['SISUA_DATA'] = '/tmp/bio_data'\n  os.environ['SISUA_EXP'] = '/tmp/bio_log'\n\n  from sisua.data import EXP_DIR, DATA_DIR\n\n  print(DATA_DIR) # /tmp/bio_data\n  print(EXP_DIR)  # /tmp/bio_log\n\nor you could set the variables in advance:\n\n.. code-block:: bash\n\n  export SISUA_DATA=/tmp/bio_data\n  export SISUA_EXP=/tmp/bio_log\n  python sisua/train.py\n  # or using the provided toolkit: sisua-train",
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