JVAE¶
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class
scvi.models.JVAE(dim_input_list, total_genes, indices_mappings, reconstruction_losses, model_library_bools, n_latent=10, n_layers_encoder_individual=1, n_layers_encoder_shared=1, dim_hidden_encoder=128, n_layers_decoder_individual=0, n_layers_decoder_shared=0, dim_hidden_decoder_individual=32, dim_hidden_decoder_shared=128, dropout_rate_encoder=0.1, dropout_rate_decoder=0.3, n_batch=0, n_labels=0, dispersion='gene-batch', log_variational=True)[source]¶ Bases:
torch.nn.modules.module.ModuleJoint Variational auto-encoder for imputing missing genes in spatial data
Implementation of gimVI [Lopez19].
- dim_input_list
- 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
Total number of different genes
- indices_mappings
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]- reconstruction_losses
list of distributions to use in the generative process ‘zinb’, ‘nb’, ‘poisson’
- model_library_bools bool list
model or not library size with a latent variable or use observed values
- n_latent
dimension of latent space
- n_layers_encoder_individual
number of individual layers in the encoder
- n_layers_encoder_shared
number of shared layers in the encoder
- dim_hidden_encoder
dimension of the hidden layers in the encoder
- n_layers_decoder_individual
number of layers that are conditionally batchnormed in the encoder
- n_layers_decoder_shared
number of shared layers in the decoder
- dim_hidden_decoder_individual
dimension of the individual hidden layers in the decoder
- dim_hidden_decoder_shared
dimension of the shared hidden layers in the decoder
- dropout_rate_encoder
dropout encoder
- dropout_rate_decoder
dropout decoder
- n_batch
total number of batches
- n_labels
total number of labels
- dispersion
See
vae.py- log_variational
Log(data+1) prior to encoding for numerical stability. Not normalization.
Methods Summary
decode(z, mode, library[, batch_index, y])encode(x, mode)forward(x, local_l_mean, local_l_var[, …])Return the reconstruction loss and the Kullback divergences
get_sample_rate(x, batch_index, *_, **__)reconstruction_loss(x, px_rate, px_r, …)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
Methods Documentation
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forward(x, local_l_mean, local_l_var, batch_index=None, y=None, mode=None)[source]¶ Return the reconstruction loss and the Kullback divergences
- Parameters
x (
TensorTensor) – tensor of values with shape(batch_size, n_input)or(batch_size, n_input_fish)depending on the modelocal_l_mean (
TensorTensor) – tensor of means of the prior distribution of latent variable l with shape (batch_size, 1)local_l_var (
TensorTensor) – tensor of variances of the prior distribution of latent variable l with shape (batch_size, 1)batch_index (
Tensor,NoneOptional[Tensor]) – array that indicates which batch the cells belong to with shapebatch_sizey (
Tensor,NoneOptional[Tensor]) – tensor of cell-types labels with shape (batch_size, n_labels)mode (
int,NoneOptional[int]) – indicates which head/tail to use in the joint network
- Return type
- Returns
the reconstruction loss and the Kullback divergences
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sample_from_posterior_l(x, mode=None, deterministic=False)[source]¶ Sample the tensor of library sizes from the posterior
- Parameters
- Return type
- Returns
type tensor of shape
(batch_size, 1)
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sample_from_posterior_z(x, mode=None, deterministic=False)[source]¶ Sample tensor of latent values from the posterior
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sample_rate(x, mode, batch_index, y=None, deterministic=False, decode_mode=None)[source]¶ Returns the tensor of scaled frequencies of expression
- Parameters
x (
TensorTensor) – tensor of values with shape(batch_size, n_input)or(batch_size, n_input_fish)depending on the modey (
Tensor,NoneOptional[Tensor]) – tensor of cell-types labels with shape(batch_size, n_labels)mode (
intint) – int encode mode (which input head to use in the model)batch_index (
TensorTensor) – array that indicates which batch the cells belong to with shapebatch_sizedecode_mode (
int,NoneOptional[int]) – int use to a decode mode different from encoding mode
- Return type
- Returns
type tensor of means of the scaled frequencies
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sample_scale(x, mode, batch_index, y=None, deterministic=False, decode_mode=None)[source]¶ Return the tensor of predicted frequencies of expression
- Parameters
x (
TensorTensor) – tensor of values with shape(batch_size, n_input)or(batch_size, n_input_fish)depending on the modemode (
intint) – int encode mode (which input head to use in the model)batch_index (
TensorTensor) – array that indicates which batch the cells belong to with shapebatch_sizey (
Tensor,NoneOptional[Tensor]) – tensor of cell-types labels with shape(batch_size, n_labels)decode_mode (
int,NoneOptional[int]) – int use to a decode mode different from encoding mode
- Return type
- Returns
type tensor of predicted expression