scvi.module.VAEC.sample

VAEC.sample(tensors, n_samples=1)[source]

Generate observation samples from the posterior predictive distribution.

The posterior predictive distribution is written as \(p(\hat{x} \mid x)\).

Parameters
tensors

Tensors dict

n_samples

Number of required samples for each cell

Return type

ndarrayndarray

Returns

x_new : torch.Tensor tensor with shape (n_cells, n_genes, n_samples)