Warning
The scvi.core top-level module is PRIVATE. We document here for contributors. Please use the top-level module scvi.models.
scvi.core
scvi.models
core.distributions.NegativeBinomial([…])
core.distributions.NegativeBinomial
Negative binomial distribution.
core.distributions.NegativeBinomialMixture(…)
core.distributions.NegativeBinomialMixture
Negative binomial mixture distribution.
core.distributions.ZeroInflatedNegativeBinomial([…])
core.distributions.ZeroInflatedNegativeBinomial
Zero-inflated negative binomial distribution.
core.models.BaseModelClass([adata, use_cuda])
core.models.BaseModelClass
core.models.VAEMixin()
core.models.VAEMixin
core.models.RNASeqMixin()
core.models.RNASeqMixin
core.modules.VAE(n_input[, n_batch, …])
core.modules.VAE
Variational auto-encoder model.
core.modules.LDVAE(n_input[, n_batch, …])
core.modules.LDVAE
Linear-decoded Variational auto-encoder model.
core.modules.TOTALVAE(n_input_genes, …[, …])
core.modules.TOTALVAE
Total variational inference for CITE-seq data.
core.modules.SCANVAE(n_input[, n_batch, …])
core.modules.SCANVAE
Single-cell annotation using variational inference.
core.modules.JVAE(dim_input_list, …[, …])
core.modules.JVAE
Joint variational auto-encoder for imputing missing genes in spatial data.
core.modules.AutoZIVAE(n_input[, …])
core.modules.AutoZIVAE
Implementation of the AutoZI model [Clivio19].
core.modules.Classifier(n_input[, n_hidden, …])
core.modules.Classifier
Basic fully-connected NN classifier
core.data_loaders.ScviDataLoader(model, adata)
core.data_loaders.ScviDataLoader
Scvi Data Loader.
core.data_loaders.TotalDataLoader(model, adata)
core.data_loaders.TotalDataLoader
Extended data loader for totalVI.
core.data_loaders.AnnotationDataLoader(*args)
core.data_loaders.AnnotationDataLoader
core.trainers.UnsupervisedTrainer(model, adata)
core.trainers.UnsupervisedTrainer
Class for unsupervised training of an autoencoder.
core.trainers.TotalTrainer(model, dataset[, …])
core.trainers.TotalTrainer
Unsupervised training for totalVI using variational inference.
core.trainers.SemiSupervisedTrainer(model, adata)
core.trainers.SemiSupervisedTrainer
Class for the semi-supervised training of an autoencoder.
core.trainers.ClassifierTrainer(*args[, …])
core.trainers.ClassifierTrainer
Class for training a classifier either on the raw data or on top of the latent space of another model.
core.trainers.trainer.Trainer(model, adata)
core.trainers.trainer.Trainer
The abstract Trainer class for training a PyTorch model and monitoring its statistics.
core.trainers.trainer.EarlyStopping([…])
core.trainers.trainer.EarlyStopping
core.utils.DifferentialComputation(model_fn, …)
core.utils.DifferentialComputation
Unified class for differential computation.