scvi.external.cellassign.CellAssignModule

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)[source]#

Bases: VAE

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 | None (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] | None (default: None)) – Number of categories for each extra categorical covariate

  • n_continuous_cov (int (default: 0)) – Number of continuous covariates

Attributes table#

training

Methods table#

generative(x, size_factor, batch_index[, ...])

Run the generative model.

loss(tensors, inference_outputs, ...[, n_obs])

Compute the loss.

sample(tensors[, n_samples, library_size])

Sample from the posterior distribution.

Attributes#

CellAssignModule.training: bool#

Methods#

CellAssignModule.generative(x, size_factor, batch_index, design_matrix=None, transform_batch=None)[source]#

Run the generative model.

CellAssignModule.loss(tensors, inference_outputs, generative_outputs, n_obs=1.0)[source]#

Compute the loss.

CellAssignModule.sample(tensors, n_samples=1, library_size=1)[source]#

Sample from the posterior distribution.

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