Scvaeit

Minor updates:
- Minor update of training augment
- Add num_repeat option for training small datasets
- Set default values: `'dist_block' = ['Gaussian', ...]`, `learning_rate=3-4`.

- Allow `dim_input_arr` and `dim_block` to be `None`.
- Adjust batch size when larger than the sample size.

What's Changed

Major updates
* Fix bug about masking 3 when computing latent embeddings
* Add MMD loss for batch correction in the latent space
* Add options for continuing training
* Add options for skip connections to improve imputation quality
* Fix bug about activation function (sigmoid for Bernoulli, scaled for NB, which was used in the initial version)

Minor updates
* v0.1.0 slight improvement https://github.com/jaydu1/scVAEIT/pull/5
* v0.2.0 add `max_vals` argument, which can be provided for each block
* Improve efficiency by decorating `tf.function`
* Improve documentation comments


Test

Time consumption on integrating DOGMA-seq, CITE-seq, and ASAP-seq datasets (30987 cells and 42598 features):

- On CPU with 12 cores and 128GB of RAM, 80s per epoch, ~11h for 500 epochs
- On NVIDIA GeForce RTX 4090, 10s per epoch, ~1.4h for 500 epochs
- On NVIDIA A100 (Colab pro+), 30s per epoch, ~4h for 500 epochs

v0.0.0-supp
Version as supplements to the paper