# New in 0.16.0 (2022-04-12)#

This release features a refactor of DestVI (#1457):

1. Bug fix in cell type amortization, which leads to on par performance of cell type amortization V_encoder with free parameter for cell type proportions V.

2. Bug fix in library size in CondSCVI, that lead to downstream dependency between sum over cell type proportions v_ind and library size library in DestVI.

3. neg_log_likelihood_prior is not computed anymore on random subset of single cells but cell type specific subclustering using cluster variance var_vprior, cluster mean mean_vprior and cluster mixture proportion mp_vprior for computation. This leads to more stable results and faster computation time. Setting vamp_prior_p in from_rna_model() to the expected resolution is critical in this algorithm.

4. The new default is to also use dropout dropout during the decoder of CondSCVI and subsequently dropout_decoder in DestVI, we found this to be beneficial after bug fixes listed above.

5. We changed the weighting of the loss on the variances of beta and the prior of eta.

Note

Due to bug fixes listed above this version of DestVI is not backwards compatible. Despite instability in training in the outdated version, we were able to reproduce results generated with this code. We therefore do not strictly encourage to rerun old experiments.

We published a new tutorial. This new tutorial incorporates a new utility package destvi_utils that generates exploratory plots of the results of DestVI. We refer to the manual of this package for further documentation.