Single-cell Variational Inference
Project description
====
scVI
====
.. image:: https://travis-ci.org/YosefLab/scVI.svg?branch=master
:target: https://travis-ci.org/YosefLab/scVI
.. image:: https://codecov.io/gh/YosefLab/scVI/branch/master/graph/badge.svg
:target: https://codecov.io/gh/YosefLab/scVI
.. image:: https://readthedocs.org/projects/scvi/badge/?version=latest
:target: https://scvi.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
Single-cell Variational Inference
* Free software: MIT license
* Documentation: https://scvi.readthedocs.io.
Quick Start
--------
1. Install Python 3.6 or later. We typically use the Miniconda_ Python distribution.
.. _Miniconda: https://conda.io/miniconda.html
2. Install PyTorch_. If you have an Nvidia GPU, be sure to install a version of PyTorch that supports it -- scVI runs much faster with a discrete GPU.
.. _PyTorch: http://pytorch.org
3. Install ``scvi`` through conda (``conda install scvi -c bioconda``) or through pip (``pip install scvi``). Alternatively, you may clone this repository and manually install the dependencies listed in setup.py_.
.. _setup.py: https://github.com/YosefLab/scVI/tree/master/setup.py
4. Refer to `this Jupyter notebook`__ to see how to import datasets into scVI.
.. __: https://github.com/YosefLab/scVI/tree/master/docs/examples/scVI-data-loading.ipynb
5. Refer to `this Jupyter notebook`__ to see how to train the scVI model, impute missing data, detect differential expression, and more!
.. __: https://github.com/YosefLab/scVI/tree/master/docs/examples/scVI-dev.ipynb
Benchmarks
--------
To recreate the results appearing in the paper referenced below, run
.. code-block::
python ./run_benchmarks.py --dataset=cortex
Valid choices for ``--dataset`` include ``synthetic``, ``cortex``, ``brain_large``, ``retina``, ``cbmc``, ``hemato``, and ``pbmc``. You may also specify an arbitrary ``.loom``, ``.h5ad`` (AnnData), or ``.csv`` file.
References
--------
Romain Lopez, Jeffrey Regier, Michael B Cole, Michael Jordan, Nir Yosef.
**"Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing."**
In submission. Preprint available at https://www.biorxiv.org/content/early/2018/03/30/292037
=======
History
=======
0.1.0 (2018-06-12)
0.1.1 (2018-06-14)
0.1.2 (2018-06-16)
------------------
* First release on PyPI.
scVI
====
.. image:: https://travis-ci.org/YosefLab/scVI.svg?branch=master
:target: https://travis-ci.org/YosefLab/scVI
.. image:: https://codecov.io/gh/YosefLab/scVI/branch/master/graph/badge.svg
:target: https://codecov.io/gh/YosefLab/scVI
.. image:: https://readthedocs.org/projects/scvi/badge/?version=latest
:target: https://scvi.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
Single-cell Variational Inference
* Free software: MIT license
* Documentation: https://scvi.readthedocs.io.
Quick Start
--------
1. Install Python 3.6 or later. We typically use the Miniconda_ Python distribution.
.. _Miniconda: https://conda.io/miniconda.html
2. Install PyTorch_. If you have an Nvidia GPU, be sure to install a version of PyTorch that supports it -- scVI runs much faster with a discrete GPU.
.. _PyTorch: http://pytorch.org
3. Install ``scvi`` through conda (``conda install scvi -c bioconda``) or through pip (``pip install scvi``). Alternatively, you may clone this repository and manually install the dependencies listed in setup.py_.
.. _setup.py: https://github.com/YosefLab/scVI/tree/master/setup.py
4. Refer to `this Jupyter notebook`__ to see how to import datasets into scVI.
.. __: https://github.com/YosefLab/scVI/tree/master/docs/examples/scVI-data-loading.ipynb
5. Refer to `this Jupyter notebook`__ to see how to train the scVI model, impute missing data, detect differential expression, and more!
.. __: https://github.com/YosefLab/scVI/tree/master/docs/examples/scVI-dev.ipynb
Benchmarks
--------
To recreate the results appearing in the paper referenced below, run
.. code-block::
python ./run_benchmarks.py --dataset=cortex
Valid choices for ``--dataset`` include ``synthetic``, ``cortex``, ``brain_large``, ``retina``, ``cbmc``, ``hemato``, and ``pbmc``. You may also specify an arbitrary ``.loom``, ``.h5ad`` (AnnData), or ``.csv`` file.
References
--------
Romain Lopez, Jeffrey Regier, Michael B Cole, Michael Jordan, Nir Yosef.
**"Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing."**
In submission. Preprint available at https://www.biorxiv.org/content/early/2018/03/30/292037
=======
History
=======
0.1.0 (2018-06-12)
0.1.1 (2018-06-14)
0.1.2 (2018-06-16)
------------------
* First release on PyPI.
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