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https://github.com/trungnt13/sisua

SemI-SUpervised generative Autoencoder models for single cell data
https://github.com/trungnt13/sisua

bayesian-inference deep-learning semi-supervised-learning single-cell variational-autoencoder

Last synced: 23 days ago
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SemI-SUpervised generative Autoencoder models for single cell data

Lists

README 

        
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`



.. __: 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



Roadmap

*******



1. [x] Multi-OMICs single-cell dataset (`link`__)

2. [x] Disentanglement VAE for multi-OMICs data (`link`__)

3. [x] New model: FactorVAE, BetaVAE, MIxture Semi-supervised Autoencoder (MISA)  (`link`__)

4. [ ] Better imputation via hierarchical latents model.

5. [ ] Release SISUA 2



.. __: https://github.com/trungnt13/sisua/blob/master/sisua/data/single_cell_dataset.py

.. __: https://github.com/trungnt13/sisua/blob/master/sisua/models/fvae.py

.. __: https://github.com/trungnt13/sisua/blob/master/sisua/models/vae.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