stVAE 0.2.9

Style transfer variational autoencoder

The official pytorch implementation of “Style transfer with variational autoencoders is a promising approach to RNA-Seq data harmonization and analysis”. The package contains a code for training and testing the model, as well as a code for working with different types of datasets.

Installation

To install the latest version from PyPI, use:

>>> pip install stvae

Benchmarks

The original code containing code with testing several models can be found here.

Example

ds = stvae.datasets.MouseDataset(download=True) # download data to the current directory
cfg = stvae.Config()
train, test, classif = ds.split(0.15, True, 0.15)
cfg.count_classes = ds.n_labels
cfg.count_classes = ds.n_batches
cfg.input_dim = ds.nb_genes
cfg.use_cuda = True # if you have a CUDA compatibility gpu
cfg.epochs = 600 # number of training epocs
cfg.classifier_epochs = 450 # number of epochs for testing classifirs training
model = stvae.stVAE(cfg)
model.train(train, None)
d = model.test(test, classif)