scvi.models Package

Classes

SCANVI(n_input[, n_batch, n_labels, …])

Single-cell annotation using variational inference.

VAEC(n_input, n_batch, n_labels[, n_hidden, …])

A semi-supervised Variational auto-encoder model - inspired from M2 model, as described in (https://arxiv.org/pdf/1406.5298.pdf)

VAE(n_input[, n_batch, n_labels, n_hidden, …])

Variational auto-encoder model.

LDVAE(n_input[, n_batch, n_labels, …])

Linear-decoded Variational auto-encoder model.

JVAE(dim_input_list, total_genes, …[, …])

Joint Variational auto-encoder for imputing missing genes in spatial data

Classifier(n_input[, n_hidden, n_labels, …])

Basic fully-connected NN classifier

AutoZIVAE(n_input[, alpha_prior, …])

AutoZI variational auto-encoder model.

TOTALVI(n_input_genes, n_input_proteins[, …])

Total variational inference for CITE-seq data

Class Inheritance Diagram

Inheritance diagram of scvi.models.scanvi.SCANVI, scvi.models.vaec.VAEC, scvi.models.vae.VAE, scvi.models.vae.LDVAE, scvi.models.jvae.JVAE, scvi.models.classifier.Classifier, scvi.models.autozivae.AutoZIVAE, scvi.models.totalvi.TOTALVI