scvi.external.cellassign.CellAssignModule

class scvi.external.cellassign.CellAssignModule(n_genes, rho, basis_means, b_g_0=None, random_b_g_0=True, n_batch=0, n_cats_per_cov=None, n_continuous_cov=0)

Bases: scvi.module.base._base_module.BaseModuleClass

Model for CellAssign.

Parameters

n_genes : int

Number of input genes

n_labels

Number of input cell types

rho : Tensor

Binary matrix of cell type markers

basis_means : Tensor

Basis means numpy array

b_g_0 : Tensor | NoneOptional[Tensor] (default: None)

Base gene expression tensor. If None, use randomly initialized b_g_0.

random_b_g_0 : bool (default: True)

Override to enforce randomly initialized b_g_0. If True, use random default, if False defaults to b_g_0.

n_batch : int (default: 0)

Number of batches, if 0, no batch correction is performed.

n_cats_per_cov : Iterable[int] | NoneOptional[Iterable[int]] (default: None)

Number of categories for each extra categorical covariate

n_continuous_cov : int (default: 0)

Number of continuous covariates

Attributes table

Methods table

generative(x, size_factor[, design_matrix])	Run the generative model.
inference()	Run the inference (recognition) model.
loss(tensors, inference_outputs, ...[, n_obs])	Compute the loss for a minibatch of data.
sample(tensors[, n_samples, library_size])	Generate samples from the learned model.

Attributes

T_destination

CellAssignModule.T_destination

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

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

CellAssignModule.device

dump_patches

CellAssignModule.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

CellAssignModule.training: bool

Methods

generative

CellAssignModule.generative(x, size_factor, design_matrix=None)

Run the generative model.

This function should return the parameters associated with the likelihood of the data. This is typically written as \(p(x|z)\).

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

inference

CellAssignModule.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

CellAssignModule.loss(tensors, inference_outputs, generative_outputs, 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

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

Generate samples from the learned model.