Model single-cell transcript counts using deep learning.
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.
- 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
argparsewhen using Python 3.6.
- 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.
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