scvi.module.TOTALVAE#
- class scvi.module.TOTALVAE(n_input_genes, n_input_proteins, n_batch=0, n_labels=0, n_hidden=256, n_latent=20, n_layers_encoder=2, n_layers_decoder=1, n_continuous_cov=0, n_cats_per_cov=None, dropout_rate_decoder=0.2, dropout_rate_encoder=0.2, gene_dispersion='gene', protein_dispersion='protein', log_variational=True, gene_likelihood='nb', latent_distribution='normal', protein_batch_mask=None, encode_covariates=True, protein_background_prior_mean=None, protein_background_prior_scale=None, use_size_factor_key=False, use_observed_lib_size=True, library_log_means=None, library_log_vars=None, use_batch_norm='both', use_layer_norm='none')[source]#
Bases:
BaseModuleClassTotal variational inference for CITE-seq data.
Implements the totalVI model of [GayosoSteier21].
- Parameters:
- n_input_genes :
int Number of input genes
- n_input_proteins :
int Number of input proteins
- n_batch :
int(default:0) Number of batches
- n_labels :
int(default:0) Number of labels
- n_hidden :
int(default:256) Number of nodes per hidden layer for encoder and decoder
- n_latent :
int(default:20) Dimensionality of the latent space
- n_layers
Number of hidden layers used for encoder and decoder NNs
- n_continuous_cov :
int(default:0) Number of continuous covarites
- n_cats_per_cov :
Iterable[int] |NoneOptional[Iterable[int]] (default:None) Number of categories for each extra categorical covariate
- dropout_rate
Dropout rate for neural networks
- gene_dispersion :
str(default:'gene') One of the following
'gene'- genes_dispersion parameter of NB is constant per gene across cells'gene-batch'- genes_dispersion can differ between different batches'gene-label'- genes_dispersion can differ between different labels
- 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
- log_variational :
bool(default:True) Log(data+1) prior to encoding for numerical stability. Not normalization.
- gene_likelihood :
str(default:'nb') One of
'nb'- Negative binomial distribution'zinb'- Zero-inflated negative binomial distribution
- latent_distribution :
str(default:'normal') One of
'normal'- Isotropic normal'ln'- Logistic normal with normal params N(0, 1)
- protein_batch_mask : {
str|int:ndarray} |NoneOptional[Dict[Union[str,int],ndarray]] (default:None) Dictionary where each key is a batch code, and value is for each protein, whether it was observed or not.
- encode_covariates :
bool(default:True) Whether to concatenate covariates to expression in encoder
- protein_background_prior_mean :
ndarray|NoneOptional[ndarray] (default:None) Array of proteins by batches, the prior initialization for the protein background mean (log scale)
- protein_background_prior_scale :
ndarray|NoneOptional[ndarray] (default:None) Array of proteins by batches, the prior initialization for the protein background scale (log scale)
- use_size_factor_key :
bool(default:False) Use size_factor AnnDataField defined by the user as scaling factor in mean of conditional distribution. Takes priority over use_observed_lib_size.
- use_observed_lib_size :
bool(default:True) Use observed library size for RNA as scaling factor in mean of conditional distribution
- library_log_means :
ndarray|NoneOptional[ndarray] (default:None) 1 x n_batch array of means of the log library sizes. Parameterizes prior on library size if not using observed library size.
- library_log_vars :
ndarray|NoneOptional[ndarray] (default:None) 1 x n_batch array of variances of the log library sizes. Parameterizes prior on library size if not using observed library size.
- n_input_genes :
Attributes table#
Methods table#
|
Run the generative model. |
|
Compute reconstruction loss. |
|
Returns the tensors of dispersions for genes and proteins. |
|
Internal helper function to compute necessary inference quantities. |
|
Returns the reconstruction loss and the Kullback divergences. |
|
|
|
Generate samples from the learned model. |
Attributes#
T_destination#
alias of TypeVar(‘T_destination’, bound=Dict[str, Any])
.. autoattribute:: TOTALVAE.T_destination
device
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
dump_patches#
training#
Methods#
generative#
- TOTALVAE.generative(z, library_gene, batch_index, label, cont_covs=None, cat_covs=None, size_factor=None, transform_batch=None)[source]#
Run the generative model.
This function should return the parameters associated with the likelihood of the data. This is typically written as \(p(x|z)\).
This function should return a dictionary with str keys and
Tensorvalues.
get_reconstruction_loss#
get_sample_dispersion#
- TOTALVAE.get_sample_dispersion(x, y, batch_index=None, label=None, n_samples=1)[source]#
Returns the tensors of dispersions for genes and proteins.
- Parameters:
- x :
Tensor tensor of values with shape
(batch_size, n_input_genes)- y :
Tensor tensor of values with shape
(batch_size, n_input_proteins)- batch_index :
Tensor|NoneOptional[Tensor] (default:None) array that indicates which batch the cells belong to with shape
batch_size- label :
Tensor|NoneOptional[Tensor] (default:None) tensor of cell-types labels with shape
(batch_size, n_labels)- n_samples :
int(default:1) number of samples
- x :
- Return type:
- Returns:
type tensors of dispersions of the negative binomial distribution
inference#
- TOTALVAE.inference(x, y, batch_index=None, label=None, n_samples=1, cont_covs=None, cat_covs=None)[source]#
Internal helper function to compute necessary inference quantities.
We use the dictionary
px_to contain the parameters of the ZINB/NB for genes. The rate refers to the mean of the NB, dropout refers to Bernoulli mixing parameters. scale refers to the quanity upon which differential expression is performed. For genes, this can be viewed as the mean of the underlying gamma distribution.We use the dictionary
py_to contain the parameters of the Mixture NB distribution for proteins. rate_fore refers to foreground mean, while rate_back refers to background mean.scalerefers to foreground mean adjusted for background probability and scaled to reside in simplex.back_alphaandback_betaare the posterior parameters forrate_back.fore_scaleis the scaling factor that enforces rate_fore > rate_back.px_["r"]andpy_["r"]are the inverse dispersion parameters for genes and protein, respectively.- Parameters:
- x :
Tensor tensor of values with shape
(batch_size, n_input_genes)- y :
Tensor tensor of values with shape
(batch_size, n_input_proteins)- batch_index :
Tensor|NoneOptional[Tensor] (default:None) array that indicates which batch the cells belong to with shape
batch_size- label :
Tensor|NoneOptional[Tensor] (default:None) tensor of cell-types labels with shape (batch_size, n_labels)
- n_samples
Number of samples to sample from approximate posterior
- cont_covs
Continuous covariates to condition on
- cat_covs
Categorical covariates to condition on
- x :
- Return type:
{
str:Tensor| {str:Tensor}}Dict[str,Union[Tensor,Dict[str,Tensor]]]
loss#
- TOTALVAE.loss(tensors, inference_outputs, generative_outputs, pro_recons_weight=1.0, kl_weight=1.0)[source]#
Returns the reconstruction loss and the Kullback divergences.
- Parameters:
- x
tensor of values with shape
(batch_size, n_input_genes)- y
tensor of values with shape
(batch_size, n_input_proteins)- batch_index
array that indicates which batch the cells belong to with shape
batch_size- label
tensor of cell-types labels with shape (batch_size, n_labels)
- Return type:
Tuple[FloatTensor,FloatTensor,FloatTensor,FloatTensor]- Returns:
type the reconstruction loss and the Kullback divergences