scvi.module.MRDeconv

scvi.module.MRDeconv#

class scvi.module.MRDeconv(n_spots, n_labels, n_hidden, n_layers, n_latent, n_genes, decoder_state_dict, px_decoder_state_dict, px_r, dropout_decoder, dropout_amortization=0.05, mean_vprior=None, var_vprior=None, mp_vprior=None, amortization='both', l1_reg=0.0, beta_reg=5.0, eta_reg=0.0001, extra_encoder_kwargs=None, extra_decoder_kwargs=None)[source]#

Bases: BaseModuleClass

Model for multi-resolution deconvolution of spatial transriptomics.

Parameters:

n_spots (int) – Number of input spots
n_labels (int) – Number of cell types
n_hidden (int) – Number of neurons in the hidden layers
n_layers (int) – Number of layers used in the encoder networks
n_latent (int) – Number of dimensions used in the latent variables
n_genes (int) – Number of genes used in the decoder
dropout_decoder (float) – Dropout rate for the decoder neural network (same dropout as in CondSCVI decoder)
dropout_amortization (float (default: 0.05)) – Dropout rate for the amortization neural network
decoder_state_dict (OrderedDict) – state_dict from the decoder of the CondSCVI model
px_decoder_state_dict (OrderedDict) – state_dict from the px_decoder of the CondSCVI model
px_r (ndarray) – parameters for the px_r tensor in the CondSCVI model
mean_vprior (ndarray (default: None)) – Mean parameter for each component in the empirical prior over the latent space
var_vprior (ndarray (default: None)) – Diagonal variance parameter for each component in the empirical prior over the latent space
mp_vprior (ndarray (default: None)) – Mixture proportion in cell type sub-clustering of each component in the empirical prior
amortization (Literal['none', 'latent', 'proportion', 'both'] (default: 'both')) – which of the latent variables to amortize inference over (gamma, proportions, both or none)
l1_reg (float (default: 0.0)) – Scalar parameter indicating the strength of L1 regularization on cell type proportions. A value of 50 leads to sparser results.
beta_reg (float (default: 5.0)) – Scalar parameter indicating the strength of the variance penalty for the multiplicative offset in gene expression values (beta parameter). Default is 5 (setting to 0.5 might help if single cell reference and spatial assay are different e.g. UMI vs non-UMI.)
eta_reg (float (default: 0.0001)) – Scalar parameter indicating the strength of the prior for the noise term (eta parameter). Default is 1e-4. (changing value is discouraged.)
extra_encoder_kwargs (dict | None (default: None)) – Extra keyword arguments passed into FCLayers.
extra_decoder_kwargs (dict | None (default: None)) – Extra keyword arguments passed into FCLayers.

Attributes table#

training

Methods table#

`generative`(x, ind_x[, batch_index, ...])	Build the deconvolution model for every cell in the minibatch.
`get_ct_specific_expression`([x, ind_x, y])	Returns cell type specific gene expression at the queried spots.
`get_gamma`([x])	Returns the loadings.
`get_proportions`([x, keep_noise])	Returns the loadings.
`inference`()	Run the inference model.
`loss`(tensors, inference_outputs, ...[, ...])	Compute the loss.
`sample`(tensors[, n_samples, library_size])	Sample from the posterior.

Attributes#

MRDeconv.training: bool#

Methods#

MRDeconv.generative(x, ind_x, batch_index=None, transform_batch=None)[source]#: Build the deconvolution model for every cell in the minibatch.

MRDeconv.get_ct_specific_expression(x=None, ind_x=None, y=None)[source]#

Returns cell type specific gene expression at the queried spots.

Parameters:

x (Tensor (default: None)) – tensor of data
ind_x (Tensor (default: None)) – tensor of indices
y (int (default: None)) – integer for cell types

MRDeconv.get_gamma(x=None)[source]#

Returns the loadings.

Return type:: Tensor
Returns:: type tensor

MRDeconv.get_proportions(x=None, keep_noise=False)[source]#