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:
BaseModuleClass
Model for CellAssign.
- Parameters:
n_genes (
int
) – Number of input genesn_labels – Number of input cell types
rho (
Tensor
) – Binary matrix of cell type markersbasis_means (
Tensor
) – Basis means numpy arrayb_g_0 (
Optional
[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 (
Optional
[Iterable
[int
]] (default:None
)) – Number of categories for each extra categorical covariaten_continuous_cov (
int
(default:0
)) – Number of continuous covariates
Attributes table#
Methods table#
|
Run the generative model. |
Run the recognition model. |
|
|
Compute the loss. |
|
Sample from the posterior distribution. |
Attributes#
training
Methods#
generative
inference
- CellAssignModule.inference()[source]#
Run the 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)[source]#
Compute the loss.
sample