scvi.external.stereoscope.SpatialDeconv#

class scvi.external.stereoscope.SpatialDeconv(n_spots, sc_params, prior_weight='n_obs')[source]#

Model of single-cell RNA-sequencing data for deconvolution of spatial transriptomics.

Reimplementation of the STModel module of Stereoscope [Andersson et al., 2020]: almaan/stereoscope.

Parameters

n_spots (int) – Number of input spots
sc_params (tuple[ndarray]) – Tuple of ndarray of shapes [(n_genes, n_labels), (n_genes)] containing the dictionnary and log dispersion parameters
prior_weight (Literal['n_obs', 'minibatch'] (default: 'n_obs')) – Whether to sample the minibatch by the number of total observations or the monibatch size

Attributes table#

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

SpatialDeconv.generative(x, ind_x)[source]#: Build the deconvolution model for every cell in the minibatch.

SpatialDeconv.get_ct_specific_expression(y)[source]#

Returns cell type specific gene expression at the queried spots.

SpatialDeconv.get_proportions(keep_noise=False)[source]#

Returns the loadings.

SpatialDeconv.loss(tensors, inference_outputs, generative_outputs, kl_weight=1.0, n_obs=1.0)[source]#: Loss computation.

SpatialDeconv.sample(tensors, n_samples=1, library_size=1)[source]#: Sample from the model.