BAVARIA is python package that implements a Batch-adversarial Variational auto-encoder with Negative Multinomial reconstruction loss for single-cell ATAC-seq analysis.
.. image:: bavaria_scheme.svg :width: 600
In particular, the model can be used to extract a latent feature representation of a cell which can be used for downstream analysis tasks, including cell cluster, cell identification, etc. The package is freely available under a GNU Lesser General Public License v3 or later (LGPLv3+)
You can install the package version v0.1.0 via
::
pip install https://github.com/BIMSBbioinfo/bavaria/archive/v0.1.0.zipAlternatively, you can install the latest version from the master branch using
::
pip install git+https://github.com/BIMSBbioinfo/bavaria.gitBAVARIA offers a command line interface that fits an ensemble of BAVARIA models given a raw count matrix (-data) Subsequently, the model parameters and latent features are stored in the output directory (-output)
::
bavaria -data adata.h5ad \
-output
Additional information on available hyper-parameters are available through
::
bavaria -h
Below you find links to the tutorials.
The tutorials will require jupyter and other resources which are defined in
:code:tutorial/requirements.txt. Using the requirements file you instantiate
a new conda environment using
.. code:: bash
conda create --name bavaria_tutorial --file tutorial/requirements.txt+----------------------------------------------------+
| Example notebooks |
+====================================================+
| Data preparation PBMC integration |
+----------------------------------------------------+
| Using BAVARIA to integrate PBMC data |
+----------------------------------------------------+
.. _Data preparation PBMC integration: https://nbviewer.jupyter.org/github/BIMSBbioinfo/bavaria/blob/master/tutorial/00_preparation.ipynb
.. _Using BAVARIA to integrate PBMC data: https://nbviewer.jupyter.org/github/BIMSBbioinfo/bavaria/blob/master/tutorial/01_pbmc_integration.ipynb