Releases: scvae/scvae
Releases · scvae/scvae
scVAE 2.1.4
- Better handling of indefinite losses during training.
scVAE 2.1.3
- 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.
scVAE 2.1.2
- 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.
scVAE 2.1.1
- 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.
scVAE 2.1.0
- 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.
scVAE 2.0.0
- 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.
First public release
This tool is publicly released as part of the submission of the paper for which this tool have been developed.