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)

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 (Optional[ndarray] (default: None)) – Mean parameter for each component in the empirical prior over the latent space

• var_vprior (Optional[ndarray] (default: None)) – Diagonal variance parameter for each component in the empirical prior over the latent space

• mp_vprior (Optional[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.)

Attributes table

Methods table

generative(x, ind_x)	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

training

MRDeconv.training: bool

Methods

generative

MRDeconv.generative(x, ind_x)

Build the deconvolution model for every cell in the minibatch.

get_ct_specific_expression

MRDeconv.get_ct_specific_expression(x=None, ind_x=None, y=None)

Returns cell type specific gene expression at the queried spots.

Parameters:

• x (Optional[Tensor] (default: None)) – tensor of data

• ind_x (Optional[Tensor] (default: None)) – tensor of indices

• y (Optional[int] (default: None)) – integer for cell types

get_gamma

MRDeconv.get_gamma(x=None)

Returns the loadings.

Return type:

Tensor

Returns:

type tensor

get_proportions

MRDeconv.get_proportions(x=None, keep_noise=False)

Returns the loadings.

Return type:

ndarray

inference

MRDeconv.inference()

Run the inference model.

loss

MRDeconv.loss(tensors, inference_outputs, generative_outputs, kl_weight=1.0, n_obs=1.0)

Compute the loss.

sample

MRDeconv.sample(tensors, n_samples=1, library_size=1)

Sample from the posterior.