scvi.module.VAEC

class scvi.module.VAEC(n_input, n_batch=0, n_labels=0, n_hidden=128, n_latent=5, n_layers=2, log_variational=True, ct_weight=None, dropout_rate=0.05, encode_covariates=False, extra_encoder_kwargs=None, extra_decoder_kwargs=None)

Bases: BaseModuleClass

Conditional Variational auto-encoder model.

This is an implementation of the CondSCVI model

Parameters:

• n_input (int) – Number of input genes

• n_batch (int (default: 0)) – Number of batches. If 0, no batch correction is performed.

• n_labels (int (default: 0)) – Number of labels

• n_hidden (int (default: 128)) – Number of nodes per hidden layer

• n_latent (int (default: 5)) – Dimensionality of the latent space

• n_layers (int (default: 2)) – Number of hidden layers used for encoder and decoder NNs

• log_variational (bool (default: True)) – Log(data+1) prior to encoding for numerical stability. Not normalization.

• ct_weight (ndarray | None (default: None)) – Multiplicative weight for cell type specific latent space.

• dropout_rate (float (default: 0.05)) – Dropout rate for the encoder and decoder neural network.

• encode_covariates (bool (default: False)) – If True, covariates are concatenated to gene expression prior to passing through the encoder(s). Else, only gene expression is used.

• extra_encoder_kwargs (dict | None (default: None)) – Keyword arguments passed into Encoder.

• extra_decoder_kwargs (dict | None (default: None)) – Keyword arguments passed into FCLayers.

Attributes table

training

Methods table

generative(z, library, y[, batch_index])	Runs the generative model.
inference(x, y[, batch_index, n_samples])	High level inference method.
loss(tensors, inference_outputs, ...[, ...])	Loss computation.
sample(tensors[, n_samples])	Generate observation samples from the posterior predictive distribution.

Attributes

VAEC.training: bool

Methods

VAEC.generative(z, library, y, batch_index=None)

Runs the generative model.

Return type:

dict[str, Distribution]

VAEC.inference(x, y, batch_index=None, n_samples=1)

High level inference method.

Runs the inference (encoder) model.

Return type:

dict[str, Tensor | Distribution]

VAEC.loss(tensors, inference_outputs, generative_outputs, kl_weight=1.0)

Loss computation.

VAEC.sample(tensors, n_samples=1)

Generate observation samples from the posterior predictive distribution.

The posterior predictive distribution is written as \(p(\hat{x} \mid x)\).

Parameters:

• tensors (dict[str, Tensor]) – Tensors dict

• n_samples (int (default: 1)) – Number of required samples for each cell

Return type:

Tensor

Returns:

x_new : torch.Tensor tensor with shape (n_cells, n_genes, n_samples)