Metadata-Version: 2.1
Name: sisua
Version: 0.4.2
Summary: SemI-SUpervised generative Autoencoder for single cell data
Home-page: https://github.com/trungnt13/sisua
Author: University of Eastern Finland
Author-email: trung@imito.ai
License: MIT license
Description: SISUA
        =====
        
        |SISUA_design|
        
        .. |SISUA_design| image:: https://drive.google.com/uc?export=view&id=1PvvG61_Rgbv_rqT6sCeb1XB6CtdiCMXX
          :width: 405
          :height: 249
        
        
        Semi-supervised Single-cell modeling:
        
        * Free software: MIT license
        * Documentation: https://github.com/trungnt13/sisua/tree/master/docs.
        
        Reference:
        
        * Trung Ngo Trong, Roger Kramer, Juha Mehtonen, Gerardo González, Ville Hautamäki, Merja Heinäniemi. **"SISUA: SemI-SUpervised Generative Autoencoder for Single Cell Data"**, ICML Workshop on Computational Biology, 2019. `[pdf]`__
        
        .. __: https://doi.org/10.1101/631382
        
        
        Installation
        ************
        
        You only need ``Python 3.6``, the stable version of SISUA installed via pip:
        
          ``pip install sisua``
        
        Install the nightly version on github:
        
          ``pip install git+https://github.com/trungnt13/sisua@master``
        
        For developers, we create a conda environment for SISUA contribution `sisua_env`__
        
          ``conda env create -f=sisua_env.yml``
        
        .. __: https://github.com/trungnt13/sisua/blob/master/sisua_env.yml
        
        Getting started
        ***************
        
        a. The basics:
            * `Datasets description`__
            * `Models specification`
            * `Basic API and work-flow`__
        b. Single-cell analysis:
            * `Latent space`
            * `Imputation of genes expression`
            * `Prediction of protein markers`
        c. Advanced technical topics:
            * `Probabilistic embedding`__
            * `Hierarchical modeling` (*coming soon*)
            * `Causal analysis` (*coming soon*)
            * `Cross datasets analysis` (*coming soon*)
        d. Benchmarks:
            * `Scalability test`__
            * `Fine-tuning networks`
            * `Data normalization`
        e. Further development:
            * `Roadmap`__
            * `SISUA 2`__
        
        .. __: https://github.com/trungnt13/sisua/blob/master/docs/dataset_description.md
        .. __: https://github.com/trungnt13/sisua/blob/master/tutorials/basics.py
        .. __: https://github.com/trungnt13/sisua/blob/master/tutorials/probabilistic_embedding.py
        .. __: https://github.com/trungnt13/sisua/blob/master/tests/scalability.py
        .. __:
        .. __:
        
        Toolkits
        ********
        
        We provide binary toolkits for *fast and efficient* analyzing single-cell datasets:
        
        * `sisua-train`__: train single-cell modeling algorithms, support training multiple systems in parallel.
        * `sisua-analyze`__: evaluate, compare, and interpret trained model.
        * `sisua-embed`__: probabilistic embedding for semi-supervised training.
        * `sisua-data`__: *coming soon*
        
        
        .. __: https://github.com/trungnt13/sisua/blob/master/bin/README.rst
        .. __: https://github.com/trungnt13/sisua/blob/master/bin/README.rst
        .. __: https://github.com/trungnt13/sisua/blob/master/bin/README.rst
        .. __: https://github.com/trungnt13/sisua/blob/master/bin/README.rst
        
        Some important arguments:
        
        -model
                    name of function declared in models__
        
                    - ``scvi``: single-cell Variational Inference model
                    - ``dca``: Deep Count Autoencoder
                    - ``vae``: single-cell Variational Autoencoder
                    - ``movae``: SISUA
        -ds
                    name of dataset declared in data__.
        
                    Description of all predefined datasets is in docs__.
        
                    Some good datasets for practicing:
        
                    - ``pbmc8k_ly``
                    - ``cortex``
                    - ``pbmcecc_ly``
                    - ``pbmcscvi``
                    - ``pbmcscvae``
        
        .. __: https://github.com/trungnt13/sisua/tree/master/sisua/models
        .. __: https://github.com/trungnt13/sisua/tree/master/sisua/data
        .. __: https://github.com/trungnt13/sisua/blob/master/docs/dataset_description.md
        
        Configuration
        *************
        
        By default, the data will be saved at your home folder at ``~/bio_data``,
        and the experiments' outputs will be stored at ``~/bio_log``
        
        You can customize these two paths using the environment variables:
        
        * For storing downloaded and preprocessed data: ``SISUA_DATA``
        * For the experiments: ``SISUA_EXP``
        
        For example:
        
        .. code-block:: python
        
          import os
          os.environ['SISUA_DATA'] = '/tmp/bio_data'
          os.environ['SISUA_EXP'] = '/tmp/bio_log'
        
          from sisua.data import EXP_DIR, DATA_DIR
        
          print(DATA_DIR) # /tmp/bio_data
          print(EXP_DIR)  # /tmp/bio_log
        
        or you could set the variables in advance:
        
        .. code-block:: bash
        
          export SISUA_DATA=/tmp/bio_data
          export SISUA_EXP=/tmp/bio_log
          python sisua/train.py
          # or using the provided toolkit: sisua-train
        
        
Keywords: sisua
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Description-Content-Type: text/x-rst
