Metadata-Version: 2.1
Name: scvae
Version: 2.1.3
Summary: Model single-cell transcript counts using deep learning.
Home-page: http://github.com/scvae/scvae
Author: Christopher Heje Grønbech, Maximillian Fornitz Vording
Author-email: christopher.groenbech@qlucore.com
License: Apache 2.0
Description: # scVAE: Single-cell variational auto-encoders #
        
        scVAE is a command-line tool for modelling single-cell transcript counts using variational auto-encoders.
        
        Install scVAE using pip for Python 3.6 and 3.7:
        
        	$ python3 -m pip install scvae
        
        scVAE can then be used to train a variational auto-encoder on a data set of single-cell transcript counts:
        
        	$ scvae train transcript_counts.tsv
        
        And the resulting model can be evaluated on the same data set:
        
        	$ scvae evaluate transcript_counts.tsv
        
        For more details, see the [documentation][], which include a user guide and a short tutorial.
        
        [documentation]: https://scvae.readthedocs.io
        
        
        # Release History #
        
        ## 2.1.3 (2020-06-29) ##
        
        * Fix loading cell and gene names for H5 data sets.
        * Report expected model directory path when scVAE cannot find a model during evaluation for easier troubleshooting.
        
        ## 2.1.2 (2020-04-07) ##
        
        * Export of decomposition of data sets and latent values as compressed TSV files.
        * Export of predictions as compressed TSV files.
        * Fix potential crash during *t*-SNE decomposition.
        
        ## 2.1.1 (2020-02-24) ##
        
        * Requires TensorFlow 1.15.2 because of a security vulnerability.
        * Export of latent values as compressed TSV files.
        * Make folder names and filenames more safe on Windows.
        * Regrouped analyses, so fewer analyses are performed by default. All available analyses can be performed using ``--included-analyses all``.
        * Fix loading of KL divergences when evaluating VAE models.
        * Fix crash during model analyses, if the model did not exist.
        
        ## 2.1.0 (2019-11-12) ##
        
        * Requires Python 3.6 or 3.7 as well as TensorFlow 1.15.
        * Documentation with user guide and tutorial.
        * Support for sparse matrices in HDF5 format.
        * Improved support for Loom files by following conventions.
        * Scatter plots of classes against the primary latent feature as well as the two primary latent features against each other when evaluating a model.
        * Fix crash related to `argparse` when using Python 3.6.
        
        ## 2.0.0 (2019-05-18) ##
        
        * Complete refactor and clean-up including structuring as Python package.
        * Easier loading of custom data sets.
        * Batch correction included in models for data sets with batch indices.
        * Learnable mixture coefficients for the GMVAE model.
        * Full covariance matrix for the GMVAE model.
        * Sampling from models.
        
        ## 1.0 (2018-04-25) ##
        
        Initial release.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6, <3.8
Description-Content-Type: text/markdown
Provides-Extra: docs
