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Model single-cell transcript counts using deep learning.

Project 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.

Release History

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.

Project details

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