scvi.module.MULTIVAE

scvi.module.MULTIVAE#

class scvi.module.MULTIVAE(n_input_regions=0, n_input_genes=0, n_input_proteins=0, modality_weights='equal', modality_penalty='Jeffreys', n_batch=0, n_obs=0, n_labels=0, gene_likelihood='zinb', gene_dispersion='gene', 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, use_size_factor_key=False, protein_background_prior_mean=None, protein_background_prior_scale=None, protein_dispersion='protein')[source]#

Bases: BaseModuleClass

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

Parameters:

  • n_input_regions (int (default: 0)) – Number of input regions.

  • n_input_genes (int (default: 0)) – Number of input genes.

  • n_input_proteins (int (default: 0)) – Number of input proteins

  • modality_weights (Literal['equal', 'cell', 'universal'] (default: 'equal')) – Weighting scheme across modalities. One of the following: * "equal": Equal weight in each modality * "universal": Learn weights across modalities w_m. * "cell": Learn weights across modalities and cells. w_{m,c}

  • modality_penalty (Literal['Jeffreys', 'MMD', 'None'] (default: 'Jeffreys')) – Training Penalty across modalities. One of the following: * "Jeffreys": Jeffreys penalty to align modalities * "MMD": MMD penalty to align modalities * "None": No penalty

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

  • gene_likelihood (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

  • gene_dispersion (Literal['gene', 'gene-batch', 'gene-label', 'gene-cell'] (default: 'gene')) – One of the following: * 'gene' - dispersion parameter of NB is constant per gene across cells * 'gene-batch' - dispersion can differ between different batches * 'gene-label' - dispersion can differ between different labels * 'gene-cell' - dispersion can differ for every gene in every cell

  • protein_dispersion (str (default: 'protein')) –

    One of the following:

    • 'protein' - protein_dispersion parameter is constant per protein across cells

    • 'protein-batch' - protein_dispersion can differ between different batches NOT TESTED

    • 'protein-label' - protein_dispersion can differ between different labels NOT TESTED

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

  • n_latent (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

  • 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 * '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

  • latent_distribution (Literal['normal', 'ln'] (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 (bool (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 (bool (default: False)) – If True, include covariates in the input to the encoder.

  • use_size_factor_key (bool (default: False)) – Use size_factor AnnDataField defined by the user as scaling factor in mean of conditional RNA distribution.

Attributes table#

training

Methods table#

generative(z, qz_m, batch_index[, ...])

Runs the generative model.

get_reconstruction_loss_accessibility(x, p, d)

Computes the reconstruction loss for the accessibility data.

get_reconstruction_loss_expression(x, ...)

Computes the reconstruction loss for the expression data.

inference(x, y, batch_index, cont_covs, ...)

Run the inference model.

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

Computes the loss function for the model.

Attributes#

MULTIVAE.training: bool#

Methods#

MULTIVAE.generative(z, qz_m, batch_index, cont_covs=None, cat_covs=None, libsize_expr=None, use_z_mean=False, label=None)[source]#

Runs the generative model.

MULTIVAE.get_reconstruction_loss_accessibility(x, p, d)[source]#

Computes the reconstruction loss for the accessibility data.

MULTIVAE.get_reconstruction_loss_expression(x, px_rate, px_r, px_dropout)[source]#

Computes the reconstruction loss for the expression data.

MULTIVAE.inference(x, y, batch_index, cont_covs, cat_covs, label, cell_idx, size_factor, n_samples=1)[source]#

Run the inference model.

Return type:

dict[str, Tensor]

MULTIVAE.loss(tensors, inference_outputs, generative_outputs, kl_weight=1.0)[source]#

Computes the loss function for the model.

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