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, mean_vprior=None, var_vprior=None, amortization='latent')[source]¶
Bases:
scvi.module.base._base_module.BaseModuleClassModel for multi-resolution deconvolution of spatial transriptomics.
- Parameters
- n_spots :
intint Number of input spots
- n_labels :
intint Number of cell types
- n_hidden :
intint Number of neurons in the hidden layers
- n_layers :
intint Number of layers used in the encoder networks
- n_latent :
intint Number of dimensions used in the latent variables
- n_genes :
intint Number of genes used in the decoder
- decoder_state_dict :
OrderedDictOrderedDict state_dict from the decoder of the CondSCVI model
- px_decoder_state_dict :
OrderedDictOrderedDict state_dict from the px_decoder of the CondSCVI model
- px_r :
ndarrayndarray parameters for the px_r tensor in the CondSCVI model
- mean_vprior :
ndarray|NoneOptional[ndarray] (default:None) Mean parameter for each component in the empirical prior over the latent space
- var_vprior :
ndarray|NoneOptional[ndarray] (default:None) Diagonal variance parameter for each component in the empirical prior over the latent space
- amortization : {‘none’, ‘latent’, ‘proportion’, ‘both’}
Literal[‘none’, ‘latent’, ‘proportion’, ‘both’] (default:'latent') which of the latent variables to amortize inference over (gamma, proportions, both or none)
- n_spots :
Methods
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.
Run the inference (recognition) model.
loss(tensors, inference_outputs, …[, …])Compute the loss for a minibatch of data.
sample(tensors[, n_samples, library_size])Generate samples from the learned model.