DecoderSCVI

class scvi.models.modules.DecoderSCVI(n_input, n_output, n_cat_list=None, n_layers=1, n_hidden=128)[source]

Bases: torch.nn.modules.module.Module

Decodes data from latent space of n_input dimensions n_output dimensions using a fully-connected neural network of n_hidden layers.

Parameters

  • n_input (intint) – The dimensionality of the input (latent space)

  • n_output (intint) – The dimensionality of the output (data space)

  • n_cat_list (Iterable[int], NoneOptional[Iterable[int]]) – A list containing the number of categories for each category of interest. Each category will be included using a one-hot encoding

  • n_layers (intint) – The number of fully-connected hidden layers

  • n_hidden (intint) – The number of nodes per hidden layer

  • dropout_rate – Dropout rate to apply to each of the hidden layers

Returns

Methods Summary

forward(dispersion, z, library, *cat_list)

The forward computation for a single sample.

Methods Documentation

forward(dispersion, z, library, *cat_list)[source]

The forward computation for a single sample.

  1. Decodes the data from the latent space using the decoder network

  2. Returns parameters for the ZINB distribution of expression

  3. If dispersion != 'gene-cell' then value for that param will be None

Parameters

  • dispersion (strstr) –

    One of the following

    • 'gene' - dispersion parameter of NB is constant per gene across cells

    • 'gene-batch' - dispersion can differ between different batches

    • 'gene-label' - dispersion can differ between different labels

    • 'gene-cell' - dispersion can differ for every gene in every cell

  • z (TensorTensor) – tensor with shape (n_input,)

  • library (TensorTensor) – library size

  • cat_list (intint) – list of category membership(s) for this sample

Returns

4-tuple of torch.Tensor parameters for the ZINB distribution of expression