scvi.module.PEAKVAE

class scvi.module.PEAKVAE(n_input_regions, n_batch=0, n_hidden=None, n_latent=None, n_layers_encoder=2, n_layers_decoder=2, n_continuous_cov=0, n_cats_per_cov=None, dropout_rate=0.1, model_depth=True, region_factors=True, use_batch_norm='none', use_layer_norm='both', latent_distribution='normal', deeply_inject_covariates=False, encode_covariates=False)

Bases: BaseModuleClass

Variational auto-encoder model for ATAC-seq data.

This is an implementation of the peakVI model descibed in.

Parameters:

• n_input_regions (int) – Number of input regions.

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

• n_hidden (Optional[int] (default: None)) – Number of nodes per hidden layer. If None, defaults to square root of number of regions.

• n_latent (Optional[int] (default: None)) – Dimensionality of the latent space. If None, defaults to square root of n_hidden.

• n_layers_encoder (int (default: 2)) – Number of hidden layers used for encoder NN.

• n_layers_decoder (int (default: 2)) – Number of hidden layers used for decoder NN.

• dropout_rate (float (default: 0.1)) – Dropout rate for neural networks

• model_depth (bool (default: True)) – Model library size factors or not.

• region_factors (bool (default: True)) – Include region-specific factors in the model

• use_batch_norm (Literal[‘encoder’, ‘decoder’, ‘none’, ‘both’] (default: 'none')) –

  One of the following

  • 'encoder' - use batch normalization in the encoder only

  • 'decoder' - use batch normalization in the decoder only

  • 'none' - do not use batch normalization (default)

  • 'both' - use batch normalization in both the encoder and decoder

• use_layer_norm (Literal[‘encoder’, ‘decoder’, ‘none’, ‘both’] (default: 'both')) –

  One of the following

  • 'encoder' - use layer normalization in the encoder only

  • 'decoder' - use layer normalization in the decoder only

  • 'none' - do not use layer normalization

  • 'both' - use layer normalization in both the encoder and decoder (default)

• latent_distribution (str (default: 'normal')) –

  which latent distribution to use, options are

  • 'normal' - Normal distribution (default)

  • 'ln' - Logistic normal distribution (Normal(0, I) transformed by softmax)

• deeply_inject_covariates (bool (default: False)) – Whether to deeply inject covariates into all layers of the decoder. If False (default), covairates will only be included in the input layer.

Attributes table

Methods table

generative(z, qz_m, batch_index[, ...])	Runs the generative model.
get_reconstruction_loss(p, d, f, x)	Compute the reconstruction loss.
inference(x, batch_index, cont_covs, cat_covs)	Helper function used in forward pass.
loss(tensors, inference_outputs, ...[, ...])	Compute the loss.

Attributes

training

PEAKVAE.training: bool

Methods

generative

PEAKVAE.generative(z, qz_m, batch_index, cont_covs=None, cat_covs=None, use_z_mean=False)

Runs the generative model.

get_reconstruction_loss

PEAKVAE.get_reconstruction_loss(p, d, f, x)

Compute the reconstruction loss.

inference

PEAKVAE.inference(x, batch_index, cont_covs, cat_covs, n_samples=1)

Helper function used in forward pass.

Return type:

Dict[str, Tensor]

loss

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

Compute the loss.