scvi.external.stereoscope.SpatialDeconv

class scvi.external.stereoscope.SpatialDeconv(n_spots, sc_params, prior_weight='n_obs')

Bases: BaseModuleClass

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

Reimplementation of the STModel module of Stereoscope [Andersson20]: https://github.com/almaan/stereoscope/blob/master/stsc/models.py.

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 : {‘n_obs’, ‘minibatch’}Literal[‘n_obs’, ‘minibatch’] (default: 'n_obs')

Whether to sample the minibatch by the number of total observations or the monibatch size

Attributes table

Methods 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()	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.

Attributes

T_destination

SpatialDeconv.T_destination

alias of TypeVar(‘T_destination’, bound=Mapping[str, Tensor])

alias of TypeVar(‘T_destination’, bound=Mapping[str, Tensor]) .. autoattribute:: SpatialDeconv.T_destination device ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

SpatialDeconv.device

dump_patches

SpatialDeconv.dump_patches: bool = False

This allows better BC support for load_state_dict(). In state_dict(), the version number will be saved as in the attribute _metadata of the returned state dict, and thus pickled. _metadata is a dictionary with keys that follow the naming convention of state dict. See _load_from_state_dict on how to use this information in loading.

If new parameters/buffers are added/removed from a module, this number shall be bumped, and the module’s _load_from_state_dict method can compare the version number and do appropriate changes if the state dict is from before the change.

training

SpatialDeconv.training: bool

Methods

generative

SpatialDeconv.generative(x, ind_x)

Build the deconvolution model for every cell in the minibatch.

get_ct_specific_expression

SpatialDeconv.get_ct_specific_expression(y)

Returns cell type specific gene expression at the queried spots.

Parameters:

y

cell types

get_proportions

SpatialDeconv.get_proportions(keep_noise=False)

Returns the loadings.

Return type:

ndarray

inference

SpatialDeconv.inference()

Run the inference (recognition) model.

In the case of variational inference, this function will perform steps related to computing variational distribution parameters. In a VAE, this will involve running data through encoder networks.

This function should return a dictionary with str keys and Tensor values.

loss

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

Compute the loss for a minibatch of data.

This function uses the outputs of the inference and generative functions to compute a loss. This many optionally include other penalty terms, which should be computed here.

This function should return an object of type LossRecorder.

sample

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

Generate samples from the learned model.