scvi.module.MULTIVAE

class scvi.module.MULTIVAE(n_input_regions=0, n_input_genes=0, n_batch=0, n_labels=0, gene_likelihood='zinb', 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, region_factors=True, use_batch_norm='none', use_layer_norm='both', latent_distribution='normal', deeply_inject_covariates=False, encode_covariates=False)[source]

Bases: scvi.module.base._base_module.BaseModuleClass

Variational auto-encoder model for joint paired + unpaired RNA-seq and ATAC-seq data.

Parameters
n_input_regions : intint (default: 0)

Number of input regions.

n_input_genes : intint (default: 0)

Number of input genes.

n_batch : intint (default: 0)

Number of batches, if 0, no batch correction is performed.

n_labels : intint (default: 0)

Number of labels, if 0, all cells are assumed to have the same label

gene_likelihood : {‘zinb’, ‘nb’, ‘poisson’}Literal[‘zinb’, ‘nb’, ‘poisson’] (default: 'zinb')

The distribution to use for gene expression data. One of the following * 'zinb' - Zero-Inflated Negative Binomial * 'nb' - Negative Binomial * 'poisson' - Poisson

n_hidden : int | NoneOptional[int] (default: None)

Number of nodes per hidden layer. If None, defaults to square root of number of regions.

n_latent : int | NoneOptional[int] (default: None)

Dimensionality of the latent space. If None, defaults to square root of n_hidden.

n_layers_encoder : intint (default: 2)

Number of hidden layers used for encoder NN.

n_layers_decoder : intint (default: 2)

Number of hidden layers used for decoder NN.

dropout_rate : floatfloat (default: 0.1)

Dropout rate for neural networks

region_factors : boolbool (default: True)

Include region-specific factors in the model

use_batch_norm : {‘encoder’, ‘decoder’, ‘none’, ‘both’}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 * 'both' - use batch normalization in both the encoder and decoder

use_layer_norm : {‘encoder’, ‘decoder’, ‘none’, ‘both’}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

latent_distribution : strstr (default: 'normal')

which latent distribution to use, options are * 'normal' - Normal distribution * 'ln' - Logistic normal distribution (Normal(0, I) transformed by softmax)

deeply_inject_covariates : boolbool (default: False)

Whether to deeply inject covariates into all layers of the decoder. If False, covariates will only be included in the input layer.

encode_covariates : boolbool (default: False)

If True, include covariates in the input to the encoder.

Attributes

Methods

generative(z, qz_m, batch_index[, …])

Runs the generative model.

get_reconstruction_loss_accessibility(x, p, d)

get_reconstruction_loss_expression(x, …)

inference(x, batch_index, cont_covs, cat_covs)

Run the inference (recognition) model.

loss(tensors, inference_outputs, …[, …])

Compute the loss for a minibatch of data.