scvi.external.gimvi.JVAE

scvi.external.gimvi.JVAE#

class scvi.external.gimvi.JVAE(dim_input_list, total_genes, indices_mappings, gene_likelihoods, model_library_bools, library_log_means, library_log_vars, n_latent=10, n_layers_encoder_individual=1, n_layers_encoder_shared=1, dim_hidden_encoder=64, n_layers_decoder_individual=0, n_layers_decoder_shared=0, dim_hidden_decoder_individual=64, dim_hidden_decoder_shared=64, dropout_rate_encoder=0.2, dropout_rate_decoder=0.2, n_batch=0, n_labels=0, dispersion='gene-batch', log_variational=True)[source]#

Bases: BaseModuleClass

Joint variational auto-encoder for imputing missing genes in spatial data.

Implementation of gimVI [Lopez et al., 2019].

Parameters:

  • dim_input_list (list[int]) –

    List of number of input genes for each dataset. If

    the datasets have different sizes, the dataloader will loop on the smallest until it reaches the size of the longest one

  • total_genes (int) – Total number of different genes

  • indices_mappings (list[ndarray | slice]) – list of mapping the model inputs to the model output Eg: [[0,2], [0,1,3,2]] means the first dataset has 2 genes that will be reconstructed at location [0,2] the second dataset has 4 genes that will be reconstructed at [0,1,3,2]

  • gene_likelihoods (list[str]) – list of distributions to use in the generative process ‘zinb’, ‘nb’, ‘poisson’

  • list (library_log_vars np.ndarray) – model or not library size with a latent variable or use observed values

  • list – List of 1 x n_batch array of means of the log library sizes. Parameterizes prior on library size if not using observed library sizes.

  • list – List of 1 x n_batch array of variances of the log library sizes. Parameterizes prior on library size if not using observed library sizes.

  • n_latent (int (default: 10)) – dimension of latent space

  • n_layers_encoder_individual (int (default: 1)) – number of individual layers in the encoder

  • n_layers_encoder_shared (int (default: 1)) – number of shared layers in the encoder

  • dim_hidden_encoder (int (default: 64)) – dimension of the hidden layers in the encoder

  • n_layers_decoder_individual (int (default: 0)) – number of layers that are conditionally batchnormed in the encoder

  • n_layers_decoder_shared (int (default: 0)) – number of shared layers in the decoder

  • dim_hidden_decoder_individual (int (default: 64)) – dimension of the individual hidden layers in the decoder

  • dim_hidden_decoder_shared (int (default: 64)) – dimension of the shared hidden layers in the decoder

  • dropout_rate_encoder (float (default: 0.2)) – dropout encoder

  • dropout_rate_decoder (float (default: 0.2)) – dropout decoder

  • n_batch (int (default: 0)) – total number of batches

  • n_labels (int (default: 0)) – total number of labels

  • dispersion (str (default: 'gene-batch')) – See vae.py

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

Attributes table#

training

Methods table#

generative(z, library[, batch_index, y, ...])

Run the generative model.

get_sample_rate(x, batch_index, *_, **__)

Get the sample rate for the model.

inference(x[, mode, n_samples, batch_index])

Run the inference model.

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

Return the reconstruction loss and the Kullback divergences.

reconstruction_loss(x, px_rate, px_r, ...)

Compute the reconstruction loss.

sample_from_posterior_l(x[, mode, deterministic])

Sample the tensor of library sizes from the posterior.

sample_from_posterior_z(x[, mode, deterministic])

Sample tensor of latent values from the posterior.

sample_rate(x, mode, batch_index[, y, ...])

Returns the tensor of scaled frequencies of expression.

sample_scale(x, mode, batch_index[, y, ...])

Return the tensor of predicted frequencies of expression.

Attributes#

JVAE.training: bool#

Methods#

JVAE.generative(z, library, batch_index=None, y=None, mode=0, transform_batch=None)[source]#

Run the generative model.

Return type:

dict

JVAE.get_sample_rate(x, batch_index, *_, **__)[source]#

Get the sample rate for the model.

JVAE.inference(x, mode=0, n_samples=1, batch_index=None)[source]#

Run the inference model.

Return type:

dict

JVAE.loss(tensors, inference_outputs, generative_outputs, mode=None, kl_weight=1.0)[source]#

Return the reconstruction loss and the Kullback divergences.

Parameters:

  • x – tensor of values with shape (batch_size, n_input) or (batch_size, n_input_fish) depending on the mode

  • batch_index – array that indicates which batch the cells belong to with shape batch_size

  • y – tensor of cell-types labels with shape (batch_size, n_labels)

  • mode (int | None (default: None)) – indicates which head/tail to use in the joint network

Return type:

tuple[Tensor, Tensor]

Returns:

the reconstruction loss and the Kullback divergences

JVAE.reconstruction_loss(x, px_rate, px_r, px_dropout, mode)[source]#

Compute the reconstruction loss.

Return type:

Tensor

JVAE.sample_from_posterior_l(x, mode=None, deterministic=False)[source]#

Sample the tensor of library sizes from the posterior.

Parameters:

  • x (Tensor) – tensor of values with shape (batch_size, n_input) or (batch_size, n_input_fish) depending on the mode

  • mode (int (default: None)) – head id to use in the encoder

  • deterministic (bool (default: False)) – bool - whether to sample or not

Return type:

Tensor

Returns:

type tensor of shape (batch_size, 1)

JVAE.sample_from_posterior_z(x, mode=None, deterministic=False)[source]#

Sample tensor of latent values from the posterior.

Parameters:

  • x (Tensor) – tensor of values with shape (batch_size, n_input)

  • mode (int (default: None)) – head id to use in the encoder

  • deterministic (bool (default: False)) – bool - whether to sample or not

Return type:

Tensor

Returns:

type tensor of shape (batch_size, n_latent)

JVAE.sample_rate(x, mode, batch_index, y=None, deterministic=False, decode_mode=None)[source]#

Returns the tensor of scaled frequencies of expression.

Parameters:

  • x (Tensor) – tensor of values with shape (batch_size, n_input) or (batch_size, n_input_fish) depending on the mode

  • y (Tensor | None (default: None)) – tensor of cell-types labels with shape (batch_size, n_labels)

  • mode (int) – int encode mode (which input head to use in the model)

  • batch_index (Tensor) – array that indicates which batch the cells belong to with shape batch_size

  • deterministic (bool (default: False)) – bool - whether to sample or not

  • decode_mode (int (default: None)) – int use to a decode mode different from encoding mode

Return type:

Tensor

Returns:

type tensor of means of the scaled frequencies

JVAE.sample_scale(x, mode, batch_index, y=None, deterministic=False, decode_mode=None)[source]#

Return the tensor of predicted frequencies of expression.

Parameters:

  • x (Tensor) – tensor of values with shape (batch_size, n_input) or (batch_size, n_input_fish) depending on the mode

  • mode (int) – int encode mode (which input head to use in the model)

  • batch_index (Tensor) – array that indicates which batch the cells belong to with shape batch_size

  • y (Tensor | None (default: None)) – tensor of cell-types labels with shape (batch_size, n_labels)

  • deterministic (bool (default: False)) – bool - whether to sample or not

  • decode_mode (int | None (default: None)) – int use to a decode mode different from encoding mode

Return type:

Tensor

Returns:

type tensor of predicted expression

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