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)1 maintainer
Metadata
1 maintainer
Release files for stVAE 0.2.10
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| File | Size | Uploaded | Provenance | Actions |
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| stVAE-0.2.10.tar.gz | 33.5 kB | Without provenance | Details for stVAE-0.2.10.tar.gz |
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| File | Interpreter | ABI | Platform | Actions Reset |
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| stVAE-0.2.10-py3-none-any.whl | Python 3 | none | any | Details for stVAE-0.2.10-py3-none-any.whl |
Total release size: 73.5 kB
Release files / stVAE-0.2.10.tar.gz
| Download URL | stVAE-0.2.10.tar.gz |
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Release files / stVAE-0.2.10-py3-none-any.whl
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| Tags | Python 3 |
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